Solar radiation will be scattered by atmospheric molecules and aerosol particles when it transfers through the Earth atmosphere. The scattered radiance with different polarization state can be used to characterize atmospheric components. Based on the BHU-ATM presented in our previous work, an atmospheric radiative transfer model considering the polarization effects is developed in this paper, in which the parameter discretization method is used. To this end, the radiative transfer equation is adapted into the Stokes vector form, while the impacts of atmospheric molecules and aerosols on the polarization state of the scattered radiance are represented by means of the scattering phase matrix. The Curtis-Godson approximation and the two-stream approximation are used to obtain the analytical solution of the adapted radiative transfer equation. As the precise calculation of the scattering phase matrix varying with the scattering angle and the radiant wavelength is inefficient for the calculation of spectral path radiance, a novel aspect of this work is the efficient computation of the scattering phase matrix through a two-dimensional interpolation method, significantly reducing computational complexity while maintaining accuracy across a broad range of angles and wavelengths. The simulation results of the atmospheric transmittance, the spectral radiance and the degree of polarization (DOP) for an arbitrarily selected transfer path are given. As it can be seen, in the spectrum from the visible through the near infrared (VNIR), the polarization modeling showed a maximum transmittance difference of 0.0007 and a spectral radiance difference of 0.3W/m2/μm/sr. The DOP varied significantly, with a difference of up to 0.12 between urban and ocean aerosols. The developed polarization model can improve aerosol component identification in satellite-based remote sensing applications, aiding in more accurate air quality monitoring and enhancing climate models that account for aerosol scattering effects.
In this work, a cloud detection scheme is proposed to process the multispectral images of space-based Earth observational sensors. With the assumption that the spectral and the spatial characteristics of the ground covers are invariant through a relatively long period, physically-based imagery simulation model is adopted to generate clear sky images for the specified sensor under the similar observational geometry with the same scene parameters. As the spectral bands of the selected sensor locate in atmospheric windows, the atmospheric condition are arbitrarily set as typical values to simulate the clear sky images. The structure similarity (SSIM) of the measured and the simulated images are calculated in the pixel by pixel manner to generate the SSIM image, in which the pixels with smaller SSIM values indicate the higher possibility of cloudy region. The cloud mask image can be obtained via selecting a suitable SSIM threshold for binary detection. A set of data measured by the Fengyun(FY)-4B geostationary satellite is used to demonstrate the usefulness of the proposed scheme. The images of the spectral bands NO.5 (1.58~1.64μm) and NO.7 (3.50~4.00μm) are selected as examples to implement cloud detection using monochromatic image alone as well as color ratio data. The results of the cloud detection validate the usefulness and the interpretability of the proposed scheme.
High fidelity simulation of continuous correlated sea clutter with long-term space-time correlation characteristics has always been a challenge. A memoryless nonlinear transform (MNLT) based sea clutter intensity simulation followed by a continuous phase retrieval method based on alternating projections (AP) algorithm provides a kind of solution with promising performance. In this paper, a recursive algorithm is proposed which can be used to replace the fast Fourier transform (FFT) for long-term sea clutter phase retrieval under the constrain of the desired time-varying Doppler spectra. Simulation results based on the parameter estimation of Council for Scientific and Industrial Research (CSIR) Fynmeet radar data demonstrate that the proposed recursive algorithm generates complex sea clutter data with exact space-time correlation characteristics as specified, while with much less calculation.
Hyperspectral atmospheric radiative transfer model (HARTM) is an essential component for image calibration and explanation in remote sensing applications. A HARTM describes the interaction between electromagnetic radiation and the earth’s atmosphere, which affects the quality and radiative accuracy of the acquired data. The performance evaluation of the HARTM is crucial to ensure the reliability of the retrieved information from calibrated images. By comparing the simulated results with measured or other validated reference data, the accuracy of HARTM can be assessed. Currently used similarity metrics, such as the Euclidean distance (ED) and the spectral angle metric (SAM), are relatively one-sided and single-valued overall assessment in evaluating hyperspectral model. The IEEE standard 1597.1 proposed feature selective validation (FSV) method as the key mathematical tool which has been widely applied in electromagnetic model verification, validation and accreditation (VV&A). However, to the best of our knowledge, the applications of FSV method in evaluating hyperspectral similarity has not yet been proposed up to this point. This paper concentrates on developing a technique for HARTM evaluation by means of FSV. Specifically, a multi-resolution components fused evaluation is proposed to obtain a more flexible comparison between the model and the measured data. As an example, the proposed approach is applied to validate the BeiHang University-Atmospheric Transfer Model (BHU-ATM) using the Michelson Interferometer for Passive Atmospheric Sounding (MIPAS) data. Results of multi-resolution components fused evaluation show good consistency with the results of directly evaluating the original data.
Aiming at the problem of reliable detection and recognition of targets in the short-range laser detection system under complex background and strong countermeasure environment, the broadening characteristics of pulse laser echo in the large dynamic change scene are studied, and an adaptive time discrimination algorithm is proposed. The algorithm uses the extracted pulse features to correct the echo delay, and then extracts one-dimensional high resolution range profile (HRRP). On this basis, the variation law of range image and intensity image of targets with different reflection characteristics is studied, and a laser target recognition and anti-jamming method based on prior model is proposed to improve the reliability of laser detection in complex environment and reduce the false alarm probability. Through numerical simulation analysis, the results show that the proposed method can identify the target quickly and efficiently in a complex background environment, effectively improve the efficiency and reliability of target detection, for the subsequent short-range laser detection system anti-interference provide reference for engineering design.
In order to make the visible and infrared characteristic of a camouflaged target closer to real background environment such that an effective fusion of the target and the background is achieved, it is necessary to study the hyperspectral signatures and analyze the performance for both target and background under various sun illumination at different time intervals by using imaging spectroscopy techniques. In this work, through outdoor field hyperspectral observations of six kinds of typical terrains such as grasslands, bare soils, concrete road and trees, the spectral image sequences are acquired under different solar conditions from 7:00 A.M to 15:00 P.M daily. It is found from the experiment results that the positions of the characteristic peak and valley kept stably fixed, while the reflectivity varies with noticeably different trends. The matching among the spectral data for different terrains are implemented and analyzed. It is seen that in visible band, the spectral matching values of wet soil, dry soil and bare soil are similar, while the spectral angle of wet soil is quite different from that of bare soil. On the other side, the matching values of trees and grassland are similar in both visible and nearinfrared bands. The results obtained are of great importance as a reference for designs of optical camouflage and stealth materials as well as for camouflage performance evaluation.
In this paper, a target and background driven simulation procedure is developed for optimal band analysis and performance evaluation of multispectral sensors for dim target detection, in which the observation geometry, target and background radiant characteristics, and the influence of infrared sensor system are integrated into. With the specified sensor altitude, the BeiHang University - Atmospheric Transfer Model (BHU-ATM) is adopted to calculate spectral irradiances of space-variant sky background. When a target with an assumed altitude exists in one line of sight (LOS), the corresponding nadir angle is used to calculate the distance between the target and the sensor, which impacts the target spectral irradiance at the sensor aperture. To analyze the optimal band for target detection, a set of spectral response functions with different central wavelengths and bandwidths are designed to calculate the target-to-background contrasts as well as the signal-to-noise ratios (SNRs). To demonstrate the usefulness of the developed procedure, typical sensor parameters are used to analyze the optimal band for aircraft detection. The band achieving the highest SNR is selected and used in the radiant image simulation for performance evaluation. The results show that the detection performance is related to spectral band as well as the LOS direction.
Sea-land segmentation in synthetic aperture radar (SAR) images is a challenge due to the high complexity of littoral environment and speckle noise. In this work, we focus on develop a new procedure for sea-land segmentation of SAR images based on multi-feature fused boundary clustering. Multi-feature fusion, which combines strong scattering and high gradient features, is adopted to achieve fragmented boundaries of the original SAR images. Multi-direction clustering combined with possible geographic information is used to distinguish the real coastlines from the fragmented boundaries. Space-borne SAR image are processed to validate the proposed method. The results demonstrate that the multi-feature fusion technique can improve the accuracy in low-scattered land discrimination and the integrality in coastline detection.
Conventionally, for the fully polarimetrical calibration of a general non-reciprocal radar system, at least two calibrators such as a dihedral corner reflector plus a sphere are required. A new polarimetric passive radar calibrator for fully polarimetric calibration is proposed in this paper. With the new design, a single calibrator is enough for the same purpose. The electromagnetic scattering characteristic of the new calibrator is calculated by method of moment (MOM), demonstrating that the new calibrator is suitable for fully polarimetric calibration applications.
Filter Back-Projection(FBP) algorithm is usually used to reproduce the target image based on polar coordinate format data. The traditional method achieves higher imaging resolution by increasing bandwidth and enlarging the target rotation angle. In practical applications, limited echo data can be obtained due to the reasons from the equipment and the detection targets. Spectral estimation algorithms such as Apes has been widely used in Radar imaging, which can obtain complex spectral estimation with more narrow spectral peaks and lower side-lobes compared with FFT methods. Thus, this paper proposes a technique to achieve higher resolution which using spectral estimation instead of the filtering process in FBP. Simulation results show the efficiency and the accuracy of the presented approach.
The amplitude and phase estimation (APES) algorithm is widely used in modern spectral analysis. Compared with conventional Fourier transform (FFT), APES results in lower sidelobes and narrower spectral peaks. However, in synthetic aperture radar (SAR) imaging with large scene, without parallel computation, it is difficult to apply APES directly to super-resolution radar image processing due to its great amount of calculation. In this paper, a procedure is proposed to achieve target extraction and parallel computing of APES for super-resolution SAR imaging. Numerical experimental are carried out on Tesla K40C with 745 MHz GPU clock rate and 2880 CUDA cores. Results of SAR image with GPU parallel computing show that the parallel APES is remarkably more efficient than that of CPU-based with the same super-resolution.
Cloud coverage is a significant clutter source impacting on space-based infrared remote sensing sensors. In most existing literature, cloud coverage was simply assumed to be homogeneous with Lambertian surface. However, real-world clouds are inhomogeneous. In this work, we focus on the reflection and transmission of solar radiation from inhomogeneous cloud coverage. The paper use Mie scattering theory and radiative transfer theory to calculate bidirectional distribution functions of reflection and transmission. The spectral distributions of transmission and reflection functions within spectral band from 0.4μm to 14μm for stratocumulus cloud are calculated. Results are compared with that of two-stream approximation, demonstrating the effectiveness and accuracy.
Multiple-input multiple-output (MIMO) radar is getting more and more applications over the last decade. In near field imaging using a linear MIMO array, the azimuth sampling is non-uniform, resulting in spatially variant point spread function (PSF) over a large imaging zone. In this work, an azimuth sidelobe suppression technique is proposed where apodization or complex amplitude weighting is applied to the multiple channel data prior to image reconstruction. For best sidelobe suppression, the optimal channel weights wopt are obtained through mathematical optimization. The overall process mainly includes three steps. Firstly, the expression of PSF in azimuth is acquired by the azimuth focusing process; Secondly, based on the fact that, for an ideal PSF the maximum value of the mainlobe should be one and the values of sidelobes should be zeros, the problem of finding wopt is mathematically fomulated as an optimization problem; Lastly, by setting proper mainlobe width and sidelobe level, the optimal weights can be solved through convex optimization algorithm. Simulations of a MIMO radar system where channel amplitude-phase error and antenna elements position deviation exist are presented and the performance of the proposed technique is studied.
Polarimetric active radar calibrator (PARC) is one of the most important calibrators with high radar cross section (RCS) for polarimetry measurement. In this paper, a new double-antenna polarimetric active radar calibrator (DPARC) is proposed, which consists of two rotatable antennas with wideband electromagnetic polarization filters (EMPF) to achieve lower cross-polarization for transmission and reception. With two antennas which are rotatable around the radar line of sight (LOS), the DPARC provides a variety of standard polarimetric scattering matrices (PSM) through the rotation combination of receiving and transmitting polarization, which are useful for polarimatric calibration in different applications. In addition, a technique based on Fourier analysis is proposed for calibration processing. Numerical simulation results are presented to demonstrate the superior performance of the proposed DPARC and processing technique.
The intensive emission of earth limb in the field of view of sensors contributes much to the observation images. Due to the low signal-to-noise ratio (SNR), it is a challenge to detect small targets in earth limb background, especially for the detection of point-like targets from a single frame. To improve the target detection, track before detection (TBD) based on the frame sequence is performed. In this paper, a new technique is proposed to determine the target associated trajectories, which jointly carries out background removing, maximum value projection (MVP) and Hough transform. The background of the bright earth limb in the observation images is removed according to the profile characteristics. For a moving target, the corresponding pixels in the MVP image are shifting approximately regularly in time sequence. And the target trajectory is determined by Hough transform according to the pixel characteristics of the target and the clutter and noise. Comparing with traditional frame-by-frame methods, determining associated trajectories from MVP reduces the computation load. Numerical simulations are presented to demonstrate the effectiveness of the approach proposed.
Knowledge of sea clutter is of great significance in marine target detection and discrimination. In this paper, the wideband backscattering electromagnetic (EM) fields of two-dimensional (2-D) sea surfaces are numerically calculated employing the weighted curvature approximation (WCA) method. Monte Carlo trials are performed to investigate the influences of radar parameters on the statistical characteristics of the rang-resolved sea clutter. It is found that the sea clutter tends to be spikier with finer radar resolution, lower grazing angle, narrower beam width, and in the upwind direction. Meanwhile, the Pareto distribution is demonstrated to describe the statistics of the sea clutter intensities very well.
In this work, a fast calculation method of the scattered radiance for scenario involving both the earth surface and the earth-limb regions is proposed. The single scattering equation of typical two-stream approximate is adapted to compute atmospheric radiative transfer under the spherical-parallel atmosphere assumption. With specified atmospheric profiles, spectral band and observation geometry, a two-dimensional (2-D) matrix of the scattered radiance varying with incident zenith and viewing zenith angles are then calculated. Finally, the earth disk images are generated for different spectral bands by interpolating the calculated radiance matrices. Simulation results of multispectral earth disk images for space-based earth observation sensors are presented to demonstrate the usefulness of the proposed technique for high fidelity scene generation where both the earth surface and the earth-limb regions are observed.
An infrared (IR) radiation generation model of stars and planets in celestial background is proposed in this paper. Cohen's spectral template1 is modified for high spectral resolution and accuracy. Based on the improved spectral template for stars and the blackbody assumption for planets, an IR radiation model is developed which is able to generate the celestial IR background for stars and planets appearing in sensor's field of view (FOV) for specified observing date and time, location, viewpoint and spectral band over 1.2μm ~ 35μm. In the current model, the initial locations of stars are calculated based on midcourse space experiment (MSX) IR astronomical catalogue (MSX-IRAC) 2 , while the initial locations of planets are calculated using secular variations of the planetary orbits (VSOP) theory. Simulation results show that the new IR radiation model has higher resolution and accuracy than common model.
In this work, we focus on developing the infrared (IR) sensor and performance analysis model for space-based IR
systems which are designed for detection of space targets in the earth and the earth-limb background. Corresponding to
the sensor observation geometry, a simplified transmittance calculation scheme applicable to large-scale scenes as well
as a mathematical model for pixel-by-pixel irradiance calculation is proposed. By defining the apparent contrast of
targets in simulated IR images, a model for detection performance analysis is developed for sensors operating in different
spectral bands. Typical simulation examples are presented to validate the current model and methodology.
KEYWORDS: Radar, Data modeling, Radar signal processing, Signal processing, Image processing, Scattering, Image fusion, Matrices, Data fusion, Radar imaging
A novel technique is proposed for ultra-wideband imagery of space objects from distributed multi-band radar data. The complex exponential (CE) model is used for representation of ultra-wideband radar signals, where an iterative procedure is developed for optimized model parameter estimation. A subband coherent processing technique is developed which combines the de-noising cross-correlation (DNCC) algorithm with statistical method to obtain the phase and amplitude incoherent parameters (ICP) between subbands. Ultra-wideband data fusion via two-dimensional gapped-data state space approach (2-D GSSA) is then applied to multiple subband signals for supper-resolution imagery. Experiments using computational electromagnetic data from the method of moment (MoM) as well as anechoic chamber measurement data are used to validate the proposed technique and demonstrate its applications.
A procedure is proposed to reconstruct the radar cross section (RCS) of interested targets from synthetic aperture radar
(SAR) images. Key factors in imaging are considered for exact RCS reconstruction, including image defocusing from
target motion, system over-sampling, window function, zero-padding and image calibration. Experimental results for
both numerically calculated inverse SAR (ISAR) and spaceborne SAR image demonstrate the effectiveness and accuracy
of the proposed technique.
The signal model of wideband monopulse radar is developed according to spotlight synthetic aperture radar (SAR)
geometry to study the three-dimensional (3-D) imaging for ship targets on sea surface. An efficient 3-D monopulse
imaging algorithm based on multi-frame one-dimensional (1-D) high resolution range profiles (HRRP) is proposed,
where no complex phase compensation is needed. Simulation results validate the algorithm and show that the
reconstructed 3-D images represent more detailed backscattering characteristics of the targets, demonstrating promising features for radar recognition.
Estimation accuracy of sea clutter plays an important role in target detection and location. In this paper, a generalized likelihood ratio test (GLRT)-entropy joint location approach for low radar cross section (RCS) target in heavy sea clutter is proposed, which takes use of both estimated probability and localized entropy of the range image. After performing detection of target based on GLRT, the proposed approach identifies the target range bin by comparing localized entropies, which are obtained before and after clutter suppression respectively. Simulation results demonstrate performance advantages of the proposed approach over the one where only probability or entropy is used.
A procedure for synthetic aperture radar (SAR) raw data generation for moving ships on the ocean is proposed, which
combines the raw data simulation of the time evolving sea and the moving ships. The raw data of the ocean and the ship
are simulated under the uniform coordinate system, respectively. The desired SAR signal is obtained by vector
summation. For the ocean SAR raw data simulation, the dynamics and time-variant reflectivity function are taken into
account. Moreover, an efficient and accurate algorithm with time-domain integration along range dimension is adopted.
For the ship raw data simulation, the ship's six degrees of freedom movement driven by the time-varying ocean waves as
well as the translation of the ship on sea surface are considered. Simulation results are presented to demonstrate the
validity and applicability of the proposed techniques.
In this paper, a novel technique for accurate velocity measurement for high speed space objects using linear frequency
modulation (LFM) radar signatures is presented. The proposed method utilizes the phase slope of the received
intermediate frequency (IF) signals from the moving object to estimate the object's kinematic parameters. Constant false
alarm rate (CFAR) detection and finite impulse response (FIR) filtering are subsequently exploited to enhance the
accuracy of the velocity estimates. Then polynomial fitting is incorporated into the phase slope analysis to further reduce
estimation errors. Simulation results demonstrate that the phase slope method is computationally efficient and accurate
for velocity estimation.
Conventional linear frequency modulation (LFM) synthetic aperture radar (SAR) is incapable of countering deceptive
repeat jamming. In this paper, a new SAR signal based on chaotic coded orthogonal frequency division multiplexing
(COFDM) is studied. The fact that chaotic codes are sensitive to the initial values allows generating a large number of
different chaotic sequences to form SAR transmitting waveforms, where all the signal sequences are orthogonal to each
other, enabling COFDM-SAR countering not only active noise but also deceptive repeat jamming. The procedures for
COFDM waveform generation and SAR anti-jamming processing are discussed. Comparative studies of the electronic
counter-countermeasure performance (ECCM) between COFDM-SAR and conventional LFM-SAR are made.
Simulation results are presented to demonstrate the superior performance of COFDM-SAR in countering repeat
deception as well as active noise jamming.
Synthetic aperture radar (SAR) systems are getting more and more applications in both civilian and military remote
sensing missions. With the increasing deployment of electronic countermeasures (ECM) on modern battlefields, SAR
encounters more and more interference jamming signals. The ECM jamming signals cause the SAR system to receive
and process erroneous information which results in severe degradations in the output SAR images and/or formation of
phony images of nonexistent targets. As a consequence, development of the electronic counter-countermeasures (ECCM)
capability becomes one of the key problems in SAR system design. This paper develops radar signaling strategies and
algorithms that enhance the ability of synthetic aperture radar to image targets under conditions of electronic jamming.
The concept of SAR using chaotic carrier frequency agility pulses (CCFAP-SAR) is first proposed. Then the imaging
procedure for CCFAP-SAR is discussed in detail. The ECCM performance of CCFAP-SAR for both depressive noise
jamming and deceptive repeat jamming is analyzed. The impact of the carrier frequency agility range on the image
quality of CCFAP-SAR is also studied. Simulation results demonstrate that, with adequate agility range of the carrier
frequency, the proposed CCFAP-SAR performs as well as conventional radar with linear frequency modulation (LFM)
waveform in image quality and slightly better in anti-noise depressive jamming; while performs very well in anti-deception
jamming which cannot be rejected by LFM-SAR.
Phase fluctuation is one of the inherent characteristics for complex radar targets.The primary objective of this work is to
compare the phase fluctuation characteristics of ships on sea surface with that of the sea clutter based on complex high
resolution range profiles (HRRPs). The statistics of the HRRP phase gradient are studied using alpha-stable distribution.
Numerical simulation results show that the HRRP phase gradient of a ship on sea surface behave significantly different
from that of the sea clutter, suggesting that the statistics of HRRP phase gradient provide useful information for ship
discrimination from sea clutter.
When a space object in spin stabilization, three-axis stabilization or roll-motion is running on orbit, heat transfer such as
radiation and conduction takes place, which makes the temperature field on the object in an unsteady state. Besides, the
methods of stabilization on orbit also affect the temperature distribution as well as projected area to a spaceborne sensor,
thus influence IR signatures of the object. In this paper, by developing the attitude dynamics as well as the heat transfer
models of an orbital object, the infrared characteristics are calculated and analyzed. Results indicate that temperature
demonstrates periodical variation, and stabilization states of the space object have important impact on the temperature
distribution and IR radiant intensity.
Modeling of the electromagnetic (EM) scattering mechanisms from two-dimensional (2-D) time-evolving sea surfaces is
a particularly complicated problem. The intricate structure of surface waves and the scattering models noticeably
influence the simulated radar signatures. Scattering calculation and Doppler spectra from the sea surfaces have been
intensively studied, experimentally as well as theoretically in the past decades. However, to author's knowledge, very
few results can be found in literatures for Doppler spectra from two-dimensional time-evolving nonlinear sea surfaces.
In this work we focus on the Doppler spectral characteristics from 2-D time-evolving nonlinear sea surfaces. Based on
Creamer's sea surface model, the first-order small slope approximation (SSA) method is applied to solve the 3-D
scattering problem. The Doppler spectra of the backscattered signals from 2-D time-evolving sea surfaces are studied for
different incident angles (from normal to grazing) as well as wind directions (from upwind to crosswind). The impacts of
the nonlinearity on the Doppler shifts and spectral widths of backscattered signals are analyzed.
By introducing the concept of distinct wave propagation vector (DWPV), this paper proposes formulations of near-field
version of Physical Optics (NFPO) and Michaeli's equivalent edge currents (NFEEC) to be adaptable for near-field
electromagnetic (EM) scattering computation. Moreover, this method can be easily extended to other high-frequency
scattering prediction techniques, which is attractive for applications such as target-seeker encounter simulation and
others. We arrive at exactly the same formula for deriving the DWPV as yielded in Legault's work. While Legault
presented more rigorous mathematical formulation and phase error analysis, this paper provides an alternative
interpretation of the key formula based on the DWPV concept, which is much easier to understand.
KEYWORDS: Radar, Image processing, Autoregressive models, Scattering, Radar imaging, Signal to noise ratio, Detection and tracking algorithms, Data modeling, Image resolution, Signal processing
The problem of radar imagery from multiple sparse frequency subbands initially incoherent to each other is of practical
importance for radar target discrimination. In this paper, a new coherent processing technique based on probability
density analysis of the subband data is proposed, which is applicable for radar imaging from measurements of two or
more initially incoherent radar subbands. The coherence parameters (CPs) for both amplitude and phase are obtained by
combining modern spectral analysis with probability density estimation (PDE). The major advantage of the current
technique lies in that unlike existent techniques, it enables more robust cohering for the sparse subband data of realworld
complex targets.
In this paper, an automatic target recognition (ATR) system based on synthetic aperture radar (SAR) is proposed. This
ATR system can play an important role in the simulation of up-to-data battlefield environment and be used in ATR
research. To establish an integral and available system, the processing of SAR image was divided into four main stages
which are de-noise, detection, cluster-discrimination and segment-recognition, respectively. The first three stages are
used for searching region of interest (ROI). Once the ROIs are extracted, the recognition stage will be taken to compute
the similarity between the ROIs and the templates in the electromagnetic simulation software National Electromagnetic
Scattering Code (NESC). Due to the lack of the SAR raw data, the electromagnetic simulated images are added to the
measured SAR background to simulate the battlefield environment8. The purpose of the system is to find the ROIs which
can be the artificial military targets such as tanks, armored cars and so on and to categorize the ROIs into the right
classes according to the existing templates. From the results we can see that the proposed system achieves a satisfactory result.
KEYWORDS: Sensors, Infrared sensors, Thermography, Staring arrays, Near field optics, Electro optical modeling, Infrared radiation, Infrared imaging, Cryogenics, Signal to noise ratio
The sensitivity of a sensor system and its optical aperture size are two key parameters commonly used to characterize the
performance of a remote sensing or space-borne surveillance system. In this work, a sensitivity model for space-borne
staring IR sensor systems which are mainly used for point-source detection and identification is developed. Different
noise components, including the photon noise from background radiation and near-field thermal radiation of optics, the
electronic noise of sensors, as well as the nonuniformity noise of an infrared focal plane array (FPA), are considered.
Based on the published parameters of the Multispectral Thermal Imager (MTI) electro-optic sensor system, the
feasibility and validity of the model are demonstrated, with emphasis on the prediction of the cryogenic temperature
impact on the sensor sensitivity and the optical aperture size requirement in a space-borne multispectral infrared (IR)
system.
In ultra wideband (UWB) radar imagery, there are often cases where the radar's operating bandwidth is interrupted due
to various reasons, either periodically or randomly. Such interruption produces phase history data gaps, which in turn
result in artifacts in the image if conventional image reconstruction techniques are used. The higher level artifacts
severely degrade the radar images. In this work, several novel techniques for artifacts suppression in gapped data
imaging were discussed. These include: (1) A maximum entropy based gap filling technique using a modified Burg
algorithm (MEBGFT); (2) An alternative iteration deconvolution based on minimum entropy (AIDME) and its modified
version, a hybrid max-min entropy procedure; (3) A windowed coherent CLEAN algorithm; and (4) Two-dimensional
(2-D) periodically-gapped Capon (PG-Capon) and APES (PG-APES) algorithms. Performance of various techniques is
comparatively studied.
Modeling of the electromagnetic (EM) scattering and synthetic aperture radar (SAR) images of a ship over sea surface is
a great challenge due to the extremely complicated scattering mechanisms between the complex target and the
dynamically variable sea surface. In this work, we propose an approximate but practical technique for high-frequency
EM scattering prediction and SAR image modeling of ships over sea surface, where major scattering mechanisms from
both the ship's hardbody and the multipath interaction between the ship and the sea surface are considered.
Computational examples for radar cross section (RCS) and SAR images are presented, demonstrating the validity and
usefulness of the current technique.
Random noise radar is rapidly emerging as a promising technique for high-resolution probing and imaging of obscured objects and interfaces. The University of Nebraska-Lincoln has developed and field-tested coherent ultra wideband polarimetric random noise radar systems that show great promise in their ability to estimate Doppler and image target and terrain features. Theoretical studies and extensive field tests using these systems confirm their ability to respond to and utilize phase information from the received signals. This paper summarizes our recent developments in coherent random noise radar imaging and discusses future research directions in this area.
This paper presents a 3D interferometric inverse-synthetic- aperture-radar (In-ISAR) imaging technique for high- frequency electromagnetic scattering diagnosis of complex radar targets. The high-frequency electromagnetic scattering data are obtained from both anechoic chamber measurements and theoretical predictions. The 2D ISAR images are obtained from the frequency-azimuth space data, and the 3D In-ISAR images are derived from two ISAR images at different incidental altitudes by interferometrical processing. Computational and experimental imaging results are shown for both simple point-like targets and complex aircraft models.
In the paper, we first present a general electromagnetic scattering model on the Doppler modulated features, i.e. the secondary modulation defined in the paper, resulting from the rotor rotation, propeller blades rotation and JEM. In addition, the airframe backscattering is given by dynamic facets modeling method for simulating the radar Doppler echoes. The processing of the measuring data shows the validity of the simulated radar echoes with the secondary modulation. A modified autocorrelation function is defined and used as the preliminary feature extraction of the secondary modulation, which is corresponding to the spectral analysis in frequency domain. Moreover, wavelet packets analysis performs the further feature extraction. Compared with 1D time domain feature extraction, 2D feature plane of wavelet packets decomposition show the higher correct recognition probability with the real-time classifier of fuzzy pattern comparator.
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