This PDF file contains the front matter associated with SPIE Proceedings Volume 8714, including the Title Page, Copyright Information, Table of Contents, and the Conference Committee listing.

This paper describes a millimeter-wave (mm-wave) radar system that has been used to range humans concealed in light foliage at 30 meters and range exposed humans at distances up to 213 meters. Human micro-Doppler is also detected through light foliage at 30 meters and up to 90 meters when no foliage is present. This is done by utilizing a composite signal consisting of two waveforms: a wide-band noise waveform and a single tone. These waveforms are summed together and transmitted simultaneously. Matched filtering of the received and transmitted noise signals is performed to range targets with high resolution, while the received single tone signal is used for Doppler analysis. The Doppler measurements are used to distinguish between different human movements using characteristic micro-Doppler signals. Using hardware and software filters allows for simultaneous processing of both the noise and Doppler waveforms. Our measurements establish the mm-wave system's ability to range humans up to 213 meters and distinguish between different human movements at 90 meters. The radar system was also tested through light foliage. In this paper, we present results on human target ranging and Doppler characterization of human movements.

A unified digital pulse compression processor is introduced as a radar-application-specific-processor (RASP) architecture for the next generation of adaptive radar. Based on traditional pulse compression matched filter and correlation receiver, the processor integrates specific designs to handle waveform diversities, which includes random noise waveforms, as well as digital transceiver self-reconfiguration for adaptive radars. Initial prototype of this processor is implemented with the latest Xilinx FPGA device and tested with an RF spaceborne radar transceiver testbed. Initial validation results show the effectiveness of real-time processing and engineering concepts.

Matched filters are used in radar systems to identify echo signals embedded in noise. They allow us to extract range and Doppler information about the target from the reflected signal. In high frequency radars, matched filters make the system expensive and complex. For that reason, the radar research community is looking at techniques like compressive sensing or compressive sampling to eliminate the use of matched filters and high frequency analog-to-digital converters. In this work, compressive sensing is proposed as a method to increase the resolution and eliminate the use of matched filters in chaotic radars. Two basic scenarios are considered, one for stationary targets and one for non-stationary targets. For the stationary targets, the radar scene was a one dimensional vector, in which each element from the vector represents a target position. For the non-stationary targets, the radar scene was a two dimensional matrix, in which one direction of the matrix represents the target’s range, and the other direction represents the target’s velocity. Using optimization techniques, it was possible to recover both radar scenes from an under sampled echo signal. The reconstructed scenes were compared against a traditional matched filter system. In both cases, the matched filter was capable of recovering the radar scene. However, there was a considerable amount of artifacts introduced by the matched filter that made target identification a daunting task. On the other hand, using compressive sensing it was possible to recover both radar scenes perfectly, even when the echo signal was under sampled.

Acoustic experiments demonstrate a novel approach to ranging and detection that exploits the properties of a solvable chaotic oscillator. This nonlinear oscillator includes an ordinary differential equation and a discrete switching condition. The chaotic waveform generated by this hybrid system is used as the transmitted waveform. The oscillator admits an exact analytic solution that can be written as the linear convolution of binary symbols and a single basis function. This linear representation enables coherent reception using a simple analog matched filter and without need for digital sampling or signal processing. An audio frequency implementation of the transmitter and receiver is described. Successful acoustic ranging measurements are presented to demonstrate the viability of the approach.

Experimental results from recent field testing with the noise correlation radar (NCR) are presented as a proof of concept. In order to understand the effectiveness of the NCR, a predetermined set of measures is established. We discuss the three experimental configurations used in evaluating the system’s range resolution/error, robustness to interference, and secure radio frequency (RF) emission. We show that the advanced pulse compression noise (APCN) radar waveform has low range measurement error, is robust to interference, and is spectrally nondeterministic. In addition, we determine that an improvement in range resolution due to phase modulation is achieved as a function of the random code length rather than the compressed pulse length.

An S-band noise radar has been developed for through-wall ranging and tracking of targets. Ranging to target is achieved by the cross-correlation between the time-delayed reflected return signal and the replica of the transmit signal; both are bandlimited ultrawideband (UWB) noise signals. Furthermore, successive scene subtraction allows for target tracking using the range profiles created by the cross-correlation technique. In this paper, we explore the performance of the radar system for target detection through varied, lossy media (e.g. a 4-inch thick brick wall and an 8-inch thick cinder-block wall) via correlation measurements using the S-band radar system. Moreover, we present a qualitative analysis of the S-band noise radar as operated under disparate testing configurations (i.e. different walls, targets, and distances.) with different antennas (e.g. dual polarized horns, helical antennas with different ground planes, etc.). In addition, we discuss key concepts of the noise radar design, considerations for an antenna choice, as well as experimental results for a few scenarios.

A hybrid-aperture radar system is being developed for passive, GNSS-based sensing and imaging missions. Different from previous work, the real aperture (RA) array has excellent cross-range resolution and electronic scanning capability, and synthetic aperture processing is applied for the dimension along the UAV/aircraft flight path. The hybrid aperture thus provides real-time, combined sensing capability and multiple functions. Multi-level signal synchronization and tracking is used to ensure the signal phase coherency and integrity. The advantages of covert radar sensing and reduced onboard computing complexity of this sensor are being demonstrated through experiments.

The design of high-resolution radars which can operate in theater involves a careful consideration of the radar’s radiated spectrum. While a wide bandwidth yields better target detectability and classification, it can also interfere with other devices and/or violate federal and international communication laws. Under the Army Research Laboratory (ARL) Partnerships in Research Transition (PIRT) program, we are developing a Stepped-Frequency Radar (SFR) which allows for manipulation of the radiated spectrum, while still maintaining an effective ultra-wide bandwidth for achieving good range resolution. The SFR is a forward-looking, ultra-wideband (UWB) imaging radar capable of detecting concealed targets. This paper presents the research and analysis undertaken during the design of the SFR which will eventually complement an existing ARL system, the Synchronous Impulse REconstruction (SIRE) radar. The SFR is capable of excising prohibited frequency bands, while maintaining the down-range resolution capability of the original SIRE radar. The SFR has two transmit antennas and a 16-element receive antenna array, and this configuration achieves suitable cross-range resolution for target detection. The SFR, like the SIRE radar, is a vehicle mounted, forward-looking, ground penetrating radar (GPR) capable of using synthetic aperture radar (SAR) technology for the detection of subsurface targets via 3D imaging. Many contradicting design considerations are analyzed in this paper. The selection of system bandwidth, antenna types, number of antennas, frequency synthesizers, digitizers, receive amplifiers, wideband splitters, and many other components are critical to the design of the SFR. Leveraging commercial components and SIRE sub-systems were design factors offering an expedited time to the initial implementation of the radar while reducing overall costs. This SFR design will result in an ARL asset to support obscured target detection such as improvised explosive devices (IEDs) and landmines.

A millimeter wave (75 - 110 GHz) polarimetric radar system (MiRTLE) has been developed for the detection of threat objects, such as guns, knives, or explosive devices, which have been concealed under clothing upon the human body. The system uses a Gaussian lens antenna to enable operation at stand-off ranges up to 25 meters. By utilizing ultra-wideband Swept Frequency Continuous Wave Radar very high range resolution (~ 10mm) is realized. The system is capable of detecting objects positioned in front of the body and of measuring the range of a target. By interpretation of the scattered waveform, the presence of a wide spectrum of threat items concealed on the human body may be detected. Threat detection is autonomously rendered by application of a neural network to the scattered time domain, polarimetric radar returns and the system may be taught to alarm or reject certain classes of objects; this allows for highly specific or broad spectrum threat detection. The radar system is portable and operator steerable allowing standoff monitoring of moving human targets in real time. Rapid (1ms) sweep times and fast signal acquisition and processing allow decisions to be made at video frame rates (30 fps) and integrated directly to a video feed providing the operator with a field of view and facilitating aiming. Performance parameters for detection of guns and simulated explosive devices are presented for ranges up to 25 meters.

Large-scale, full-wave modeling of multistatic target imaging in a rough ground environment is described. The emulation methodology employs a parallelized three-dimensional “near-field” finite-difference time-domain algorithm in characterizing the electromagnetic scattering from the ground surface and buried and on-surface targets in the form of landmines and unexploded ordnances; subsequent focusing of the scattered fields into an image is obtained with the time-reversal technique. The emphasis of this study is on investigating the detectability of discrete ground targets in the presence of distributed variable ground clutter as relevant to performance prediction for ultra-wideband forward-looking radar applications.

Nonlinear radar exploits the electronic response from a target whose reflected frequencies are different from those transmitted. Reception of frequencies that are not part of the transmitted probe distinguishes the received signal from a linear return produced by clutter and indicates the presence of electronics. Presented in this paper is a type of nonlinear radar that transmits multiple frequencies and listens for a harmonic of these frequencies as well as other frequencies near that harmonic. A laboratory test-bed has been constructed to demonstrate the multitone radar concept. Measurements of nonlinear responses from RF devices probed by multiple tones are reported.

Radar tomography has been an active area of investigation at the Air Force Research Laboratory (AFRL) for many years. Building upon this knowledge base, recent efforts have begun to focus on developing synergistic combinations between noise based waveforms and radar tomographic imaging techniques. More specifically, an emphasis has been placed on extending the traditional dimensionality from two to three, while condensing the familiar overall required instantaneous bandwidth for noise based radar systems. Through the inclusion of a Direction of Arrival (DOA) capability into the radar RF architecture, and through back projection processing, a target is capable of being located both in its azimuthal and elevation position between multiple towers. The previously developed Noisy Stepped Frequency (NSF) waveform is utilized as the excitation source from each radar towers thereby reducing the necessary instantaneous bandwidth. The thumb-tack-like response of the NSF waveform provides a sharpened" image of the target and better assists in the localization of the target in its appropriate elevation. The DOA is implemented through the Generalized Cross Correlation (GCC) method. Through the processing combination of back projection and DOA, imaging and localization of both single, and multiple targets is realizable for a three dimensional geometry. Simulated and experimental validations shall be provided and compared.

An increasingly cluttered electromagnetic environment (EME) is a growing problem for radar systems. This problem is becoming critical as the available frequency spectrum shrinks due to growing wireless communication device usage and changing regulations. A possible solution to these problems is cognitive radar, where the cognitive radar learns from the environment and intelligently modifies the transmit waveform. In this paper, a cognitive nonlinear radar processing framework is introduced where the main components of this framework consist of spectrum sensing processing, target detection and classification, and decision making. The emphasis of this paper is to introduce a spectrum sensing processing technique that identifies a transmit-receive frequency pair for nonlinear radar. It will be shown that the proposed technique successfully identifies a transmit-receive frequency pair for nonlinear radar from data collected from the EME.

The electromagnetic scattering responses of nonlinearly loaded antenna structures excited by single-tone or multi-tone incident fields are considered in the frequency domain by employing a combination of the method-of-moments and a harmonic balance technique. Subsequently, standoff detection and localization of the scatterers in the presence of a half space is demonstrated with a subspace imaging procedure by exploiting the harmonic scattering responses.

Detection of landmines based on complex resonance frequencies has been studied in the past and no distinctive results have been reported. Especially for low metal content landmines buried at depths greater than 9 cm, resonant frequencies become fairly distributed in the background and no specific frequency of interest can be used. However, in a typical impulse radar, spectral energy density of the transmitted pulse can be very broad and its peak can be located anywhere. Usually, a compromise is made between penetration depth and feature resolution for spectral energy peak allocation. Pulse amplitude, duration, symmetry, its spectral energy distribution, ringing level all affect depth and resolution metrics in a complicated way. Considering receiver dynamic range, we study two distinct pulses having different spectral energy density peaks and their detection ability for landmines with little or no metallic content. We carry out experiments to show that pulse shape/fidelity is critical to obtain desired contrast in post-processing of data.

The problem of detecting buried objects has engaged radar system developers for quite some time. Many systems—both experimental and commercial—have been developed, including vehicle-mounted systems that look beneath road surfaces. Most of these downward-looking systems exploit multiple transmit and receive channels to enhance resolution in the final radar imagery used for target detection. In such a system, the configuration and operation of the various transmit and receive elements play a critical role in the quality of the output imagery. In what follows, we leverage high-fidelity electromagnetic model data to examine a multistatic downward-looking radar system. We evaluate the signatures produced by various targets of interest and describe, both qualitatively and quantitatively, the variations in target signatures produced by different system configurations. Finally, we analyze the underlying physics of the problem to explain certain characteristics in the observed target signatures.

This paper describes the study of a through-the-wall radar system for three-dimensional (3-D) building imaging, based on computer simulations. Two possible configurations are considered, corresponding to an airborne spotlight and a ground-based strip-map geometry. The paper details all the steps involved in this analysis: creating the computational meshes, calculating the radar signals scattered by the target, forming the radar images, and processing the images for visualization and interpretation. Particular attention is given to the scattering phenomenology and its dependence on the system geometry. The images are created via the backprojection algorithm and further processed using a constant falsealarm rate (CFAR) detector. We discuss methods of 3-D image visualization and interpretation of the results.

This paper investigates the exploitation of the spectral properties of human targets and indoor clutter for sensing through the wall (STTW) applications. The paper focuses on analysis and comparison between human targets and that of common indoor clutter for STTW by comparing modeling results with that of measured data. The characterization of spectral properties for targets and clutter are accomplished through two approaches. The first approach utilizes finite difference time domain (FDTD) techniques to examine the radar cross section (RCS) of humans and indoor clutter objects by using different types of computer models. FDTD allows for the spectral characteristics to be acquired over a wide range of frequencies, polarizations, and aspect angles. The second approach makes use of calibrated RCS calculations using network analyzer measurements to characterize human RCS. We compare and contrast the RCS responses for analysis and use in STTW.

Stationary target detection in through-the-wall radar imaging (TWRI) using image segmentation techniques has recently been considered in the literature. Specifically, histogram thresholding methods have been used to aid in removing the clutter, resulting in ‘clean’ radar images with target regions only. In this paper, we show that histogram thresholding schemes are effective only against clutter regions, which are distinct from target regions. Target detection using these methods becomes challenging, if not impossible, in the presence of multipath ghosts and clutter that closely mimics the target in size and intensity. Because of the small variations between the target regions and such clutter and multipath ghosts, we propose a textural feature based classifier for through-the-wall target detection. The feature based scheme is applied as a follow-on step after application of histogram thresholding techniques. The training set consists of feature vectors based on gray level co-occurrence matrices corresponding to the target and ghost/clutter image regions. Feature vectors are then used in training a minimum distance classifier based on Mahalanobis distance metric. Performance of the proposed scheme is evaluated using real-data collected with Defence Research and Development Canada’s vehicle-borne TWRI system. The results show that the proposed textural feature based method yields much improved results compared to histogram thresholding based segmentation methods for the considered cases.

Multiple-input multiple-output (MIMO) radar systems have been shown to have significant performance improvements over their single-input multiple-output (SIMO) counterparts. For transmit and receive elements that are collocated, the waveform diversity afforded by this radar is exploited for performance improvements. These improvements include but are not limited to improved target detection, improved parameter identifiability and better resolvability. In this paper, we present the Synchronous Impulse Reconstruction Radar (SIRE) Ultra-wideband (UWB) radar designed by the Army Research Lab (ARL) for landmine and improvised explosive device (IED) detection as a 2 by 16 MIMO radar (with collocated antennas). Its improvement over its SIMO counterpart in terms of beampattern/cross range resolution are discussed and demonstrated using simulated data herein. The limitations of this radar for Radio Frequency Interference (RFI) suppression are also discussed in this paper. A relaxation method (RELAX) combined with averaging of multiple realizations of the measured data is presented for RFI suppression; results show no noticeable target signature distortion after suppression. In this paper, the back-projection (delay and sum) data independent method is used for generating SAR images. A side-lobe minimization technique called recursive side-lobe minimization (RSM) is also discussed for reducing side-lobes in this data independent approach. We introduce a data-dependent sparsity based spectral estimation technique called Sparse Learning via Iterative Minimization (SLIM) as well as a data-dependent CLEAN approach for generating SAR images for the SIRE radar. These data-adaptive techniques show improvement in side-lobe reduction and resolution for simulated data for the SIRE radar.

Researchers have recently proposed a widely separated multiple-input multiple-output (MIMO) radar using monopulse angle estimation techniques for target tracking. The widely separated antennas provide improved tracking performance by mitigating complex target radar cross-section fades and angle scintillation. An adaptive array is necessary in this paradigm because the direct path from any transmitter could act as a jammer at a receiver. When the target-free covariance matrix is not available, it is critical to include robustness into the adaptive beamformer weights. This work explores methods of robust adaptive monopulse beamforming techniques for MIMO tracking radar.

Regular micro and nano radars cannot provide reliable tracking of low altitude low profile aerial targets in urban and mountain areas because of reflection and re-reflections from buildings and terrain. They become visible and vulnerable to guided missiles if positioned on a tower or blimp. Doppler radar cannot distinguish moving cars and small low altitude aerial targets in an urban area. A new concept of pocket size distributed radar technology based on the application of UAV (Unmanned Air Vehicles), UGV (Unmanned Ground Vehicles) is proposed for tracking of low altitude low profile aerial targets at short and medium distances for protection of stadium, camp, military facility in urban or mountain areas.

This paper investigates the feasibility of using a noise waveform in an ultra-wideband (UWB) radar system for two-dimensional tomographic imaging of a stationary object with a multistatic tomographic geometry. Multiple UWB transmitters and receivers are positioned along each side of the imaging area. We perform several numerical simulations in time-domain, and the successful imaging of the target is achieved by visual inspection of the formed images.

The Synchronous Impulse Reconstruction (SIRE) forward-looking radar, developed by the U.S. Army Research Laboratory (ARL), can detect concealed targets using ultra-wideband synthetic aperture technology. The SIRE radar has been mounted on a Ford Expedition and combined with other sensors, including a pan/tilt/zoom camera, to test its capabilities of concealed target detection in a realistic environment. Augmented Reality (AR) can be used to combine the SIRE radar image with the live camera stream into one view, which provides the user with information that is quicker to assess and easier to understand than each separated. In this paper we present an AR system which utilizes a global positioning system (GPS) and inertial measurement unit (IMU) to overlay a SIRE radar image onto a live video stream. We describe a method for transforming 3D world points in the UTM coordinate system onto the video stream by calibrating for the intrinsic parameters of the camera. This calibration is performed offline to save computation time and achieve real time performance. Since the intrinsic parameters are affected by the zoom of the camera, we calibrate at eleven different zooms and interpolate. We show the results of a real time transformation of the SAR imagery onto the video stream. Finally, we quantify both the 2D error and 3D residue associated with our transformation and show that the amount of error is reasonable for our application.

A small and lightweight dual-channel radar has been developed for SAR data collections. Using standard Displaced Phase Center Antenna (DPCA) radar digital signal processing, SAR GMTI images have been obtained. The prototype radar weighs 5-lbs and has demonstrated the extraction of ground moving targets (GMTs) embedded in high-resolution SAR imagery data. Heretofore this type of capability has been reserved for much larger systems such as the JSTARS. Previously, small lightweight SARs featured only a single channel and only displayed SAR imagery. Now, with the advent of this new capability, SAR GMTI performance is now possible for small UAV class radars.

In recent years ARTEMIS, Inc. has developed a series of compact, versatile Synthetic Aperture Radar (SAR) systems which have been operated on a variety of small manned and unmanned aircraft. The multi-frequency-band SlimSAR has demonstrated a variety of capabilities including maritime and littoral target detection, ground moving target indication, polarimetry, interferometry, change detection, and foliage penetration. ARTEMIS also continues to build upon the radar's capabilities through fusion with other sensors, such as electro-optical and infrared camera gimbals and light detection and ranging (LIDAR) devices. In this paper we focus on experiments and applications employing SAR and LIDAR fusion. LIDAR is similar to radar in that it transmits a signal which, after being reflected or scattered by a target area, is recorded by the sensor. The differences are that a LIDAR uses a laser as a transmitter and optical sensors as a receiver, and the wavelengths used exhibit a very different scattering phenomenology than the microwaves used in radar, making SAR and LIDAR good complementary technologies. LIDAR is used in many applications including agriculture, archeology, geo-science, and surveying. Some typical data products include digital elevation maps of a target area and features and shapes extracted from the data. A set of experiments conducted to demonstrate the fusion of SAR and LIDAR data include a LIDAR DEM used in accurately processing the SAR data of a high relief area (mountainous, urban). Also, feature extraction is used in improving geolocation accuracy of the SAR and LIDAR data.

To meet the demands for higher efficiency and size, weight, power, and cost (SWaP-C), a high efficiency Power Amplifier (PA) design is discussed. The primary focus is on Class F and Inverse Class F PAs for increased efficiency in P, L, S, and X band applications. By incorporating innovative matching techniques at the second and third harmonics and including fast voltage switching circuits, efficiencies over 80% are achievable at a peak output power greater than 25 Watts. These technologies allow for a reduction in battery size and cooling requirements, while achieving state-of-the-art efficiencies.

For successful beam shaping and scanning in phased array radars, it is essential to precisely set the amplitude and phase of each element channel. However, considerable amplitude and phase differences among the channels can occur due to the different RF hardware connected to each element. Also, the phase and amplitude characteristics of most RF devices depend on frequency and temperature and usually drift in time. In order to equalize the phase and amplitude effects of the channels, phased array radars need to be calibrated periodically. In the literature, various phased array calibration methods are discussed. However, the specifics of these methods are usually not covered. Here, we describe four of the most commonly used calibration methods in detail: near-field scanning probe, peripheral fixed probes, calibration lines, and mutual coupling. Each calibration method is described step by step and relevant formulas are given. The advantages and disadvantages of each method are also discussed.

Providing situational awareness to the warfighter requires radar, communications, and other electronic systems that operate in increasingly cluttered and dynamic electromagnetic environments. There is a growing need for cognitive RF systems that are capable of monitoring, adapting to, and learning from their environments in order to maintain their effectiveness and functionality. Additionally, radar systems are needed that are capable of adapting to an increased number of targets of interest. Cognitive nonlinear radar may offer critical solutions to these growing problems. This work focuses on ongoing efforts at the U.S. Army Research Laboratory (ARL) to develop a cognitive nonlinear radar test-bed. ARL is working toward developing a test-bed that uses spectrum sensing to monitor the RF environment and dynamically change the transmit waveforms to achieve detection of nonlinear targets with high confidence. This work presents the architecture of the test-bed system along with a discussion of its current capabilities and limitations. A brief outlook is presented for the project along with a discussion of a future cognitive nonlinear radar test-bed.

The utilization of unmanned aerial systems (UASs) for intelligence, surveillance and reconnaissance (ISR) applications continues to increase and unmanned systems have become a critical asset in current and future battlespaces. With the development of medium-to-low altitude, rapidly deployable aircraft platforms, the ISR community has seen an increasing push to develop ISR sensors and systems with real-time mission support capabilities. This paper describes the design and development of the RASAR (Real-time, Autonomous, Synthetic Aperture Radar) sensor system and presents demonstration flight test results. RASAR is a modular, multi-band (L and X) synthetic aperture radar (SAR) imaging sensor designed for self-contained, autonomous, real-time operation with mission flexibility to support a wide range of ISR needs within the size, weight and power constraints of Group III UASs. SAR waveforms are generated through direct digital synthesis enabling arbitrary waveform notching to enable operations in cluttered RF environments. RASAR is capable of simultaneous dual-channel receive to enable polarization based target discrimination. The sensor command and control and real-time image formation processing are designed to enable integration of RASAR into larger, multi-intelligence system of systems. The multi-intelligence architecture and a demonstration of real-time autonomous cross-cueing of a separate optical sensor will be presented.

The marine transportation system (MTS) is a vital component of the United States Economy. Waterborne cargo accounts for more than $742 billion of the nation’s economy and creates employment for 13 million citizens. A disruption in this system would have far reaching consequences to the security of the country. The US National High Frequency radar network, which comprises 130 radar stations around the country, became operational in May 2009. It provides hourly measurements of surface currents to the US Coast Guard for search and rescue (SAR). This system has the capability of being a dual use system providing information for environmental monitoring as well as vessel position information for maritime security. Real time vessel detection has been implemented at two of the radar stations outside New York Harbor. Several experiments were conducted to see the amount vessel traffic that the radar could capture. The radars were able to detect a majority of the vessels that are reporting via the Automatic Identification System (AIS) as well as 30 percent of mid to large size vessels that are not reporting via AIS. The radars were able to detect vessels out to 60 km from the coast. The addition of a vessel detection capability to the National HF radar network will provide valuable information to maritime security sector. This dual use capability will fill a gap in the current surveillance of US coastal waters. It will also provide longer-range situational awareness necessary to detect and track smaller size vessels in the large vessel clutter.

A method of effectively detecting remote concealed threats, particularly knives and guns, has been developed. This method uses multi-polarimetric ultra wide band active microwave radar to remotely scan a person under investigation. It has been shown that the radar signatures from such scans can be used to detect whether a person is carrying a concealed threat. A Principal Component Analysis (PCA) data reduction technique followed by a neural network (NN) is used to classify the information extracted from the radar signals. The technique combines the co, 45°, cross, and 135° polarized transceived radar signals into a single data set for classification. Illuminating the target with a range of polarizations, together with choosing a radar beam size commensurate with the targets in question, produces good discrimination between threat and non-threat items. Once collected, the data sets obtained are reduced via PCA, which significantly improves the correct classification rate at the NN stage and makes the technique more tolerant of variations in the threat objects orientation and better able to detect a wider range of threat types. Experimental results are presented which show that a detection rate of up to 80% for knives and guns can be achieved, with a false alarm rate as low as 4%.

The fusion of image data from different sensor types is an important processing step for many remote sensing applications to maximize information retrieval from a given area of interest. The basic process to fuse image data is to select a common coordinate system and resample the data to this new image space. Usually, this is done by orthorectifying all those different image spaces, which means a transformation of the image’s projection plane to a geographic coordinate system. Unfortunately, the resampling of the slant-range based image space of a space borne synthetic aperture radar (SAR) to such a coordinate system strongly distorts its content and therefore reduces the amount of extractable information. The understanding of the complex signatures, which are already hard to interpret in the original data, even gets worse. To preserve maximum information extraction, this paper shows an approach to transform optical images into the radar image space. This can be accomplished by using an optical image along with a digital elevation model and project it to the same slant-range image plane as the one from the radar image acquisition. This whole process will be shown in detail for practical examples.

A three dimensional (3-D) spatio-temporal analog signal processing scheme is presented for the selective removal of off-dish interference and noise from focal plane array (FPA) received signals. The method exploits specific geometrical properties of the 3-D spatio-temporal frequency spectrum of FPA signals to perform the filtering operation. A 3-D infinite impulse response (IIR) filter having a cone-shaped filter passband in the 3-D spatio-temporal frequency space is employed to extract the spectra of desired FPA signals while rejecting the spectral components from undesired off-dish interference and coupled noise from front-end electronics. A proof-of-concept example is provided by considering the filtering operation in 3-D spatio-temporal frequency domain.

Synthetic aperture radar systems that use the polar format algorithm are subject to a focused scene size limit inherent to the polar format algorithm. The classic focused scene size limit is determined from the dominant residual range phase error term. Given the many sources of phase error in a synthetic aperture radar, a system designer is interested in how much phase error results from the assumptions made with the polar format algorithm. Autofocus algorithms have limits to the amount and type of phase error that can be corrected. Current methods correct only one or a few terms of the residual phase error. A system designer needs to be able to evaluate the contribution of the residual or uncorrected phase error terms to determine the new focused scene size limit. This paper describes a method to estimate the complete residual phase error, not just one or a few of the dominant residual terms. This method is demonstrated with polar format image formation, but is equally applicable to other image formation algorithms. A benefit for the system designer is that additional correction terms can be added or deleted from the analysis as necessary to evaluate the resulting effect upon image quality.

High quality focused SAR imaging dictates that the relative phase error over an aperture must be kept below a fraction of a wavelength. On most deployed SAR systems the internal measurement systems ability to measure position uncertainty is not sufficient to achieve this required precision. This necessitates an additional post-processing step of data-driven phase error mitigation known as autofocus. We present results comparing the performance of a variety of autofocus techniques including image metric optimization based techniques and several variants of phase gradient autofocus (PGA). The degree of focusing is evaluated with an image focus metric, specific to SAR images, that is not biased toward any particular autofocus algorithm. This evaluation is performed on a variety of scene types using injected (known) phase errors. We show that PGA autofocus outperforms the image metric optimization techniques tested (based on minimizing image entropy) in low contrast SAR scenes.

In this paper, target position and velocity estimation is investigated for track declaration using an air-to-air radar. A post-Kalman filtering processing method is proposed to improve the filtering accuracy and thus to improve the target position and velocity estimation accuracy. The proposed method passes the outputs of the Kalman filters (KFs) within a sliding window through a weighted least squares (WLS) estimator to refine the estimates of current target position and velocity and their variances. It is therefore referred to as the post-KF-WLS method. The post-KF-WLS estimates of the current target position and velocity are utilized to project the target position in a future time of interest. The uncertainty of the target position projection is derived and a closed-form solution is formulated. The effectiveness of the proposed method is demonstrated by using Monte Carlo simulations. Impacts of contributing factors to the target position projection uncertainty are quantified via simulations and the dominating factor is identified as well.

Synthetic aperture (SA) techniques are currently employed in a variety of imaging modalities, such as radar (SAR) and ladar (SAL). The advantage of fine resolution provided by these systems far outweighs the disadvantage of having large amounts of raw data to process to obtain the final image. Digital processors have been the mainstay for synthetic aperture processing since the 1980’s; however, the original method was optical that is, it employed lenses and other optical elements. This paper provides a global review of a compact light weight optronic processor that combines optical and digital techniques for ultra-fast generation of synthetic aperture images. The overall design of the optronic processor is detailed, including the optical design and data control and handling. As well, its real-time capabilities are demonstrated. Example ENVISAT/ASAR images generated optronically are also presented and compared with ENVISAT Level 1 products. As well, the extended capabilities of optronic processing, including wavefront correction and interferometry are discussed. Finally, a tabletop synthetic aperture ladar system is introduced and SAL images generated using the exact optronic processor designed for SAR image generation are presented.

The detection of ground-moving targets requires clutter cancellation, which is typically performed using space-time adaptive processing (STAP). The detections from STAP provide the measurements of range, bearing, and Doppler. These measurements can then be fed to Bayesian state estimators. In this paper, results from an airborne radar data set are processed and the performance of filtering and smoothing algorithms are compared. The standard nonlinear filtering algorithms, namely the extended Kalman filter, are used. It is found that while the smoother performance is significantly better than that of the filter, the smoothing window need not be large to obtain the superior performance.

This paper considers compressive sensing for time-frequency signal representation (TFSR) of nonstationary radar signals which can be considered as instantaneously narrowband. Under-sampling and random sampling of the signal stem from avoiding aliasing and relaxing Nyquist sampling constraints. Unlike previous work on compressive sensing (CS) and TFSR based on the ambiguity function, reduced observations in the underlying problem are time-domain data. In the reconstruction process, Orthogonal Matching Pursuit (OMP) is used. Since the frequency index in the first iteration of OMP is the same as the one obtained by finding the frequency position of the highest Spectrogram peak, it becomes necessary to consider several OMP iterations to improve over Spectrograms performance. We examine various methods for estimating IF from higher number of OMP iterations, including the S-method. The paper also applies CS for signal time-frequency signature estimations corresponding to human gait radar returns.

The estimation of the covariance matrix from real data is required in the application of space-time adaptive processing (STAP) to an airborne ground moving target indication (GMTI) radar. A natural approach to estimation of the covariance matrix that is based on the information geometry has been proposed. In this paper, the output of the Riemannian mean is used in inversion and projection algorithms. It is found that the projection class of algorithms can yield very significant gains, even when the gains due to inversion-based algorithms are marginal over standard algorithms. The performance of the projection class of algorithms does not appear to be overly sensitive to the projected subspace dimension.

The goal of this paper is to describe a novel high-resolution 3D numerical method for the solution of high frequency electromagnetic wave propagation. This method will be used later by the author to computationally simulate data for the solution of the inverse problem of imaging mine-like targets. Thus the solution of the forward problem presented in this paper is a necessary prelude to the future solution of a related inverse problem. In this paper, land mines are modeled as small abnormalities imbedded in an otherwise uniform media with an air-ground interface. These abnormalities are characterized by the electrical permittivity and the conductivity, whose values differ from those of the host media. The main challenge in the calculation of the scattered electromagnetic signal in these settings is the requirement of solving the Helmholtz equation for high frequencies which is excessively time consuming using standard direct solution techniques. A high-resolution and rapid numerical procedure for the solution of this equation is described in this paper. The kernel of this algorithm is a combination of a fourth order compact finite-difference scheme and a preconditioned Krylov subspace approach. A fourth order compact approximation for the Helmholtz equation is considered to reduce approximation and pollution errors, thereby softening the point-per-wavelength constraint. The coefficient matrix of the resulting system is not Hermitian and possesses as positive as well as negative eigenvalues so represent a significant challenge for constructing an efficient iterative solver. In our approach this system is solved by a combination of Krylov subspace-type method with a direct FFT-type preconditioner. The resulting numerical method allows efficient implementation on parallel computers. Numerical results for realistic ranges of parameters in soil and mine-like targets confirm the high efficiency of the proposed iterative algorithm.

Micro-Doppler is an emerging technique for the measurement and analysis of target modulation characteristics, rooted in the analysis of X-band radar measurements of people. Despite the advantage of higher Doppler sensitivity, there appears to be little such work reported at (sub-) millimeter wave frequencies. We have developed fully coherent, solid state, FMCW radar systems operating at 94 and 340 GHz, suitable for micro-Doppler and vibrometry studies (as well as SAR/ISAR), which make use of DDS chirp generation combined with upconversion and MMIC or Schottky diode frequency multiplication. Due to the low phase noise architecture, the phase (i.e. displacement) sensitivity can be below 1 micron in distance.

The design of a 29.5 GHz experimental active interferometer for the measurement of the angular velocity of moving humans is presented in this paper, as well as initial measurements of walking humans. Measurement of the angular motion of moving objects is a desirable function in remote security sensing applications. Doppler radar sensors are able to measure the signature of moving humans based on micro-Doppler analysis; however, a person moving with little to no radial velocity produces negligible Doppler returns. Measurement of the angular movement of humans can be done with traditional radar techniques however the process involves either continuous tracking with narrow beamwidth or angle-of arrival estimation algorithms. Recently, the authors presented a new method of measuring the angular velocity of moving objects using interferometry. The method measures the angular velocity of an object without tracking or complex processing. The frequency shift imparted on the signal response is proportional to the angular velocity of the object as it passes through the interferometer beam pattern. The experimental system consists of a transmitter and two separate receivers with two widely spaced antennas. The received signals in each of the two channels are downconverted and digitized, and post-processed offline. Initial results of a walking person passing through the interferometer beam pattern are presented, which verify the expected operation of the receiver derived from the initial theory.

This paper presents a processing technique that can be used to detect and classify pedestrians group based on the micro- Doppler signature gathered with a millimeter wave radar. The evaluation of the number of pedestrians moving in a group can be a difficult task using a traditional micro-Doppler spectrogram because of a tendency for people to partially synchronize their steps when walking together. The new approach, based on multi-range variation as well as the micro-Doppler variations, provides promising results. The range-spectrogram processing technique was developed and tested using a database composed of hundreds of pedestrian and vehicle signatures gathered in an urban test site over a two year period in a variety of weather conditions. We associate image detections with radar detections through motion extracted from both radar and imagery. We also explain how radar and video together can produce an inexpensive alternative to 3-D imaging.

Interferometric Synthetic Aperture Radar (IFSAR or InSAR) uses multiple antenna phase centers to ultimately measure target scene elevation. Its ability to do so depends on the antenna configuration, and how the multiple phase centers are employed. We examine several different dual-phase-center antenna configurations and modalities, including a conventional arrangement where a dedicated antenna is used to transmit and receive with another to receive only, a configuration where transmit and receive operations are ping-ponged between phase centers, a monopulse configuration, and an orthogonal waveform configuration. Our figure of merit is the RMS height noise in the elevation estimation. We show that a monopulse configuration is equivalent to the ping-pong scheme, and both offer an advantage over the conventional arrangement. The orthogonal waveform offers the best potential performance, if sufficient isolation can be achieved.

A random phase signal will also have random phase differences between two independent random phases. A phase increment across a time increment is in fact a phase-rate, or frequency. A phase-rate change is in fact a frequency-hop. By controlling the phase-rate, that is the characteristics of the phase increments, we can control the spectrum of the random-phase waveform. Spectrum precision and sharpness is enhanced by holding a frequency for some ‘chip’ length. For digitally generated phase samples, this means that the chip length needs to be many samples. This is a time-bandwidth issue. The definition of ‘many’ will depend on the sharpness desired, but often several tens’ of samples will be adequate. To shape the Energy Spectral Density (ESD) of a random-phase signal, we need to control the average energy at various phase-rates. This can be done with either or a combination of 1) Controlling the likelihood of specific phase increments, and/or 2) Controlling the duration of a specific phase increment chip length. For range-Doppler images, it is the 2-dimensional Impulse Response (IPR) that is of principal concern. This will tend to average out the random effects of any single pulse.

A pulse may be divided into contiguous sequential frames, sometimes called sub-pulses. In a typical pulse-Doppler radar, receiving echo energy must be deferred until after the entire pulse waveform is transmitted. This sets a nearest possible range at which the beginning of the echo pulse can be processed. However, even when early frames or portions of frames are occluded or eclipsed by the transmit pulse, the echo from later frames may still be received and processed. This allows latter frames to be received in their entirety from nearer ranges than earlier frames or the entire pulse. As long as the latter frames still exhibit the desired resolution bandwidth, no loss of resolution is suffered by processing against only the latter frames. In this manner, a compound multi-frame pulse can be processed against a larger range swath than a more conventional pulse modulation scheme. Essentially, the traditional constraints between near-range detection and pulsewidth have been considerably loosened. Relative frame durations can be optimized to allow SNR to exceed some minimum level.

A general synthetic aperture radar (SAR) signal model is derived based on the Maxwells equation, and three numerical simulations are analyzed and discussed. With this signal model, compressive sensing is applied to get a better image.

Current commercial Ground Penetrating Radar (GPR) systems are found at a high cost and allow little interaction between the user and the system. This paper presents a low cost and flexible GPR platform attractive for use in education and research based on the Universal Software Radio Peripheral (USRP) developed by Ettus Research. A software application developed in Labview enables users to select and modify fundamental parameters of the transmission and reception stages of a GPR system. Users are able to modify parameters such as sampling and carrier frequencies, waveform shape, amplitude, and bandwidth. The programmability of the USRP in conjunction with the developed software tools provides a user-friendly GPR platform.

In June 2012, General Atomics Aeronautical Systems, Inc. (GA-ASI) Reconnaissance Systems Group participated in the NATO Unified Vision 2012 (UV12) Joint ISR (JISR) Trial at Orland Main Air Station in Brekstad, Norway. GA-ASI supplied a modified King Air 200 as a Predator B/MQ-9 Reaper Remotely Piloted Aircraft (RPA) surrogate outfitted with a Lynx Block 30 Multi-mode Synthetic Aperture Radar/Ground Moving Target Indicator (SAR/GMTI), a FLIR Star SAFIRE 3800HD Electro-optical/Infrared (EO/IR) sensor, and a L-3 Tactical Common Data Link. This airborne platform was combined with GA-ASI’s new System for Tactical Archival, Retrieval, and Exploitation (STARE) for full integration into the NATO ISR exploitation community. UV12 was an event sponsored by the NATO Joint Capability Group on Intelligence, Surveillance, and Reconnaissance (ISR) to focus on the interoperability of national ISR assets and improving JISR concept of operations. The Predator B RPA surrogate flew alongside multiple NATO ISR assets in nine missions that showcased the platform’s all-weather ISR capabilities focusing on the Lynx SAR/GMTI and Maritime Wide Area Search (MWAS) modes. The inclusion of the STARE technology allowed GA-ASI’s radar and Full Motion Video (FMV) data to be seamlessly processed and passed to joint networks where the data was fused with other NATO ISR products, resulting in a full battlefield reconnaissance picture.

Synthetic aperture radar (SAR) collections that integrate over a wide range of aspect angles hold the potentional for improved resolution and fosters improved scene interpretability and target detection. However, in practice it is difficult to realize the potential due to the anisotropic scattering of objects in the scene. The radar cross section (RCS) of most objects changes as a function of aspect angle. The isotropic assumption is tacitly made for most common image formation algorithms (IFA). For wide aspect scenarios one way to account for anistropy would be to employ a piecewise linear model. This paper focuses on such a model but it incorporates aspect and spatial magnitude filters in the image formation process. This is advantageous when prior knowledge is available regarding the desired targets’ RCS signature spatially and in aspect. The appropriate filters can be incorporated into the image formation processing so that specific targets are emphasized while other targets are suppressed. This is demonstrated on the Air Force Research Laboratory (AFRL) GOTCHA1 data set to demonstrate the utility of the proposed approach.