We have coded a tomographic-style algorithm that is capable of imaging radar data obtained on a circular flight path about a 3D target zone. Our imaging algorithm is designed to image field data collected with Mireage's new Subsurface Imaging Synthetic Aperture Radar (SISAR), a ground-penetrating device operating in the spotlight mode. The SISAR algorithm operates on radar data gathered in (or converted to) the range-azimuth domain--the so-called sinogram plane. On the sinogram plane, the impulse response of a point scatterer is sinewave- shaped curve. The amplitude of the sinewave is related to the target's radial coordinate, its phase to the target's azimuthal coordinate, and its bias to the target's burial depth. When flown on a circular path about a 3D target zone, SISAR generates 3D-style sinograms. Our imaging algorithm produces 3D maps of the target zone by converting each sinewave trace on the sinogram plane to a delta function in three-space. The code is fast (in the FFT sense). Moreover, it avoids the laborious, and often inaccurate conversion of the collected radar data from cylindrical coordinates to rectangular ones, as in conventional radar imaging.

In synthetic aperture radar (SAR) imaging, generation of fine resolution imagery requires measurement and compensation for the relative translational and rotational motions between the antenna phase center (APC) and the scene being imaged. A difficulty arises when motion compensating stripmap mode signal history data from a SAR requiring a wide angle azimuth beamwidth. In general, the required compensation varies with scatterer location within the azimuth beam. The usual approach in stripmap motion compensation is to ignore this requirement and to compensate only the component of sensor motion in the nominal antenna boresight direction (usually broadside). In the widebeam system, this procedure can lead to unacceptable phase errors even if sensor motion is measured without error. In this paper, we survey the motion compensation challenge to ultra-wideband/widebeam (UWB/WB) SAR image formation. The emphasis here is on formation of fine resolution digital imagery from low frequency (VHF/UHF) UWB/WB data collected in stripmap mode by an airborne SAR system . We examine differences in motion compensation requirements and approaches between a UWB/WB system and a conventional X-band SAR system. This paper presents an improved motion compensation algorithm applicable to widebeam stripmap SAR. We demonstrate this procedure with imagery from the fine resolution ARPA/ERIM/NAWC P3 ultra-wideband radar system.

We describe an approach for the interpretation of ultra-wideband (UWB) synthetic aperture radar (SAR) imagery for object classification. The UWB sensor is used for detection of manmade objects, including those which are obscured by a forest canopy. An electromagnetic model is used to predict the backscatter received from the scene objects. We show that the 'early-time' (physical optics) portion of the backscatter is highly dependent on the object structure. Hence, backscatter from scene objects that differ in structure, such as vehicles and trees, have different sensitivity to aspect angle of the incident wave. Manmade objects exhibit specular reflections ar certain aspect angles while natural objects generally do not. Aspect- angle sensitivity of the backscatter is extracted by reconstructing the SAR image of an object over smaller subapertures of the full synthetic aperture. This multi-aperture approach provides important angular information while still maintaining detectable levels in the subsequent SAR images. However, this information alone cannot be used to accurately classify objects due to a limited range of aspect angles and an inhomogeneous obscuring media. Consequently, this information is combined with polarization information to establish a robust feature vector for object classification. These features are shown to be useful in detecting vehicles obscured by foliage. Results of our approach on real data are presented.

The incorporation of linear feature mapping into a maximum likelihood ratio test criterion makes possible a novel realizable adaptive detector of a 2D resolved signal with limited priori knowledge of the target signal pattern and the nonstationary clutter. As demonstrated by experiments on actual SAR data, the detection probability of the test depends strongly on the effective generalized signal-to-noise ratio (EGSNR), which is determined by the selected feature mapping and representation. In this paper, an analytical framework is proposed for the linear feature mapping detector (LFMD), within which various linear feature mappings, such as the short time Fourier transform (STFT), the discrete cosine transform, the discrete wavelet transform, and the discrete Gabor transform, etc. can be compared in a systematic way in terms of detection performance. The closed-form solution of the porblem is obtained and tested by using the SRI ultra wideband (SAR) data, which is single polarization with 200MHZ to 400MHz band at 1 meter resolution. The robustness of the LFMD with several linear mappings is compared for classifying multi-oriented targets in actual SAR image. Several different classes of targets including both civilian and noncivilian vehicles imaged at broadside and head-on are used for the study. Also the effect of an inaccurate priori knowledge of the target's significant features on the value of the EGSNR is investigated.

Foliage penetrating VHF/UHF SAR can image hidden ground vehicles, but numerous false alarms produced by residual tree trunk image spikes strongly impede automatic detection. This paper demonstrates at least 30 dB reduction in tree trunk false alarms through an approach that directly uses target and clutter scattering physics in a matched filter detector. The large improvement results because scattering is measured over wide angles. Yet, because of the long wavelength, few scatterers are involved, and modeling is both simple and robust. The target is modeled by the dihedral formed between its side and the ground, whereas trees are modeled by vertical cylinders. Since input experimental data is nearly complex-Gaussian, the detector is nearly optimal, and its implementation is made computationally efficient by filtering in two steps. Although the matching is demonstrated in terms of angle diversity, frequency, and polarimetry may be included, and the method fits into an efficient overall ATR search.

The performance of an electric field integral equation (EFIE) model is discussed in the context of target detection and classification. This EFIE model, which is a modified version of the Finite Element Radiation model developed at MIT Lincoln Laboratory, was used to simulate VHF SAR signatures of ground targets. These SAR signatures were studied and compared with signatures generated from data colelcted by the CARABAS sensor during a 1993 Foliage Penetration Radar experiment. It was found that the EFIE model can provide adequate RCS values and distributions for gound target detection studies. It was also found that the simulated images have reasonable spatial appearance and may be useful for ground target classification studies.

We present an algorithm for the removal of narrow-band interference from wideband signals. We apply the algorithm to suppress radio frequency interference encountered by ultra- wideband synthetic aperture radar systems used for foliage- and ground-penetrating imaging. For this application, we seek maximal reduction of interference energy, minimal loss and distortion of wideband target responses, and real-time implementation. To balance these competing objectives, we exploit prior information concerning the interference environment in designing an estimate-and-subtract-estimation algorithm. The use of prior knowledge allows fast, near-least-squares estimation of the interference and permits iterative target signature excision in the interference estimation procedure to decrease estimation bias. The results is greater interference suppression, less target signature loss and distortion, and faster computation than is provided by existing techniques.

This paper summarizes the results of an ARPA/Army sponsored program to develop innovative approaches for reducing the effects of multiple source of radio frequency interference (RFI) on synthetic aperture radars (SARs) operating in the frequency range of 100 MHz to 1000 MHz. Since the SAR signal can be modeled as wide band noise, the approach taken to achieve the objective was to model the RFI as a collection of tones within the desired SAR signal bandwidth. RFI suppression consisted of detecting the presence of and estimating the number of these tones, estimating their amplitude, phase, and frequency, and finally reconstructing the RFI and coherently subracting it from the original corrupted signal. Central to our approach was the use of a parametric maximum likelihood (PML) algorithm for the estimation of the parameters of the RFI tones. Although most of our effort was devoted to the evaluation of the performance obtainable from the PML algorithm, a variation of band-stop filtering, which is referred to as the Notch or Adaptive Mask algorithm, was also studied. Since the focus of this program was the development of algorithms for the ultra-wideband (UWB) P-3 SAR, which is a deramp SAR, a means of applying the PML algorithm to deramped RFI was also necessary. This paper will thus briefly describe the PML algorithm and how it can be applied to a deramp SAR, and it will then discuss the preformance of both RFI suppression algorithms and their computational complexity. As a result of this one year effort, two RFI algorithms have been developed that automatically remove 90 to 95 percent of the RFI that could have corrupted a SAR image.

Under Advanced Research Prjects Agency (ARPA) sponsorship, Loral Defense Systems, Arizona has developed algorithms and techniques for suppressing radio frequency interference (RFI) in radar signals. These techniques use conventional signal processing elements in an unconventional manner to localize the RFI energy in a domain where it can be easily distinguished from radar returns and removed from the radar data with minimal effect on coherent radar signal image formation processing. Significantly improved synthetic-aperture radar (SAR) images have been obtained using these techniques. Flight tests of a Loral airborne SAR system operating from 500 to 800 MHz have provided the SAR data used to develop and refine these RFI suppression techniques.

Airborne surveillance and ultra wideband radars operating at low frequencies experience co- channel (or in-band) electromagnetic RF interference (RFI) from nonhostile emitter sources (e.g. fixed and mobile communications). Although many of these interference sources will be received in the antenna sidelobe region, approximately 30-35 dB down, a significant number will be encountered in the mainbeam. This paper describes an algorithm that 'notches' out the mainbeam, in-band interference sources, while simultaneously 'equalizing' or compensating for the range-time sidelobes that results from the 'notching' process. Target resolution performance and SAR imaging integrity is maintained with the RFI suppression technique and algorithm developed. The algorithm developed by Grumman, is referred to as the minimization algorithm. This paper describes in detail the derivation of this algorithm and presents performance results of its application against numerous RFI data scenarios.

This paper describes a computationally efficient, high-performance, UWB radar interference suppression algorithm. An adaptive FIR (finite impulse response) prediction-error noise- whitening filter exhibits minimal computational complexity and achieves 30 dB interference reduction per pulse (1 microsecond(s) long) with 16-bit simulated interference. Using measured interference data digitized to 8-bits with a 6.5 effective bit digitizer, collected just north of Washington, DC at the Army Research Laboratory, the technique achieved 20 to 27 dB of reduction. To minimize the computational load, the filter weights are periodically determined from data collected during a fraction of a radar range sweep. These weights are found to be effective for hundreds of subsequent radar pulses. Previous work on an estimate-and-subtract, tone-extraction technique resulted in 20 dB average interference reduction on the same measured data with a computational load linearly related to the number of tones extracted. The adaptive filtering approach uses an over-determined system producing an FIR filter with N taps, independent of the number of interference signals. An iterative technique to reduce the range sidelobes caused by the filter's impulse response is also presented. The computational load of this iterative stage is, at worst, linearly related to the number of targets whose sidelobes are extracted. It is shown that, with a small reduction in performance, the sidelobe reduction can be accomplished with a relatively small increase in the overall computational load. The computational complexity of the proposed technique relative to the estimate-and- subtract technique depends on the signal and interference environment and on the acceptable sidelobe level. A comprehensive radio and TV interference simulator was developed to test the interference suppression algorithm. It avoids difficulties in memory requirements and code complexity typically encountered in high-sample rate, long duration, and UWB simulations. Data was generated for various population densities, sampling rates, and quantization levels. Results using the simulation data showed that the performance of the algorithm was related to the quantization level with more bits producing better results.

Synthetic aperture radar image formation algorithms typically use transform techniques that often require trading between image resolution, algorithm efficiency, and focused image scene size limits. This is due to assumptions for the data such as simplified (often straight-line) flight paths, simplified imaging geometry, and simplified models for phase functions. Many errors in such assumptions are typically untreatable due to their dependance on both data domain positions and image domain positions. The result is that large scenes often require inefficient multiple image formation iterations, followed by a mosaicking operation of the focused image patches. One class of image formation algorithms that perform favorably divides the spatial and frequency apertures into subapertures, and perhaps those subapertures into sub- subapertures, and so on, in a tiered subaperture fashion. This allows a gradual shift from data domain into image domain that allows correcting many types of errors that limit other image formation algorithms, even in a dynamic motion environment, thereby allowing large focused image patches without mosaicking. This paper presents and compared focused patch diameter limits for tiered subaperture image formation algorithms, for various numbers of tiers of subapertures. Examples are given that show orders-of-magnitude improvement in non- mosaicked focused image patch size over traditional polar format processing, and that patch size limits increase with the number of tiers of subapertures, although with diminishing returns.

Many synthetic aperture radar (SAR) image formation algorithms require the computation of a multidimensional Fourier transform of irregularly sampled or unequally spaced data samples. We apply a recently developed algorithm, the unequally spaced FFT (USFFT), to SAR image formation and compare its accuracy and complexity to a conventional algorithm. We find that the USFFT algorithm allows comparable accuracy to traditional approaches at a slightly reduced computational cost. We briefly discuss extensions of the USFFT algorithm to multiresolution SAR imaging.

We present an adaptive FIR filtering approach, which is referred to as the APES (amplitude and phase estimation of a sinusoid), for complex spectral estimation. We compare the APES algorithm with other FIR filtering approaches including the Welch and Capon methods. We also describe how to apply the FIR filtering approaches to target range signature estimation and synthetic aperture radar (SAR) imaging. We show via both numerical and experimental examples that the adaptive FIR filtering approaches such as Capon and APES can yield more accurate spectral estimates with much lower sidelobes and narrower spectral peaks than the FFT method, which is also a special case of the FIR filtering approaches. We show that although the APES algorithnm yields somewhat wider spectral peaks than the Capon method, the former gives more accurate overall spectral estimates and SAR images that the latter and the FFT method.

High-definition imaging (HDI) is a model-based approach to SAR image generation derived from modern spectral-estimation (superresolution) techniques. It provides improved resolution and substantial reduction in sidelobes and clutter by exploiting distinguishing characteristcs of target responses, such as waveform and polarization, as well as their spatial separation, as was demonstrated in a paper presented at SPIE last year. This paper presents additional results of the application of HDI to SAR data. It is shown that the improved resolution, clutter, and sidelobe reduction previosly demonstrated with UHF data can also be obtained at Ka-band, even in the presence of foliage. The broadside flash characteristic of ground vehicles is exploited to obtain information about their length and orientation. Finally, polarimetric signatures are exploited to classify the scattering centers on a vehicle.

With the invention of the ImSyn^TM Processor, an optoelectronic device capable of image formation from a wide range of sensors, Essex Corporation made possible high-speed, high-peformance image reconstruction on inexpensive workstations. The ImSyn Processor will facilitate SAR phase history analysis and support research in other frequency domain imaging techniques. During image reconstruction, the processor permits flexible interaction with the process steps, enabling processes like autofocus and motion compensation. Examples of SAR images from breadboard tests and descriptions of procesing steps are presented.

An automatic taget recognition (ATR) technique developed by the authors features analytically derived object models which are formed from entire image suites, yet are compact and allow a direct target recognition and pose determination procedure. In contrast to the pose-invariant information used to form the models in conventional approaches, view-dependent information is retained in the formation of the compact models for this new approach. All model-based ATR systems are confronted with the problem of image variation as a function of viewing angle. This problem can be addressed by use of an exhaustive library of views, at the expense of a large suite of literal images and a computationally intensive search-based recognition process. Means for overcoming these storage and processing obstacles have traditionally invloved some type of view-independent target representation, often developed from some composite view of the target over the viewing angles of interest. This results in a much more compact target model, and a more direct recognition process. Unfortunately, the gains in storage and computational requirements of these invariant algorithms come at the price of diminished target discrimination capability. The new algorithm incorporates pose as a fundamental parameter which is solved for as part of the recognition process, and does not discard the pose-related information which is relevant to target recognition. In this paper, the newly developed technique is applied to synthetic aperture radar images to develop receiver operating characteristic curves in the presence of both multiplicative noise and clutter. Comparative curves are also developed for a conventional generalized quandratic classifier ATR system.

Neural network models of early visual computation have been adapted for processing single polarization (VV channel) SAR imagery, in order to assess their potential for enhanced target detection. In particular, nonlinear center-surround shunting networks and multi-resolution boundary contour/feature contour system processing has been applied to a spotlight sequence of tactical targets imaged by the Lincoln ADT sensor at 1 ft resolution. We show how neural processing can modify the target and clutter statistics, thereby separating the poplulations more effectively. ROC performance curves indicating detection versus false alarm rate are presented, clearly showing the potential benefits of neural pre-processing of SAR imagery.

We present a detection concept for initial target screening based on features that are derived from a multiresolution decomposition of synthetic aperture radar (SAR) data. The physical motivation of the multiresolution feature-based approach is the exploitation of signature oscillations produced by the interference between prominant scatterers in cultural objects when resolution is varied. We develop a generalized likelihood ratio test detector which differentiates between first order autoregressive multiresolution processes attributed to different spatial areas. We then derive two special cases of this detector motivated by arguments regarding the clutter statistics. We show that these schemes significantly outperform a standard energy detector operating on the finest available SAR resolution only.

Two new polarimetric subspace target detectors are developed based on a dihedral signal model for bright peaks within a spatially extended target signature. The first is a coherent dihedral target detector based on the exact Huynen model for a dihedral. The second is a noncoherent dihedral target detector based on the Huynen model with an extra unknown phase term. Expressions for these polarimetric subspace target detectors are developed for both additive Gaussian clutter and more general additive spherically invariant random vector clutter including the K-distribution. For the case of Gaussian clutter with unknown clutter parameters, constant false alarm rate implementations of these polarimetric subspace target detectors are developed. The performance of these dihedral detectors is demonstrated with real millimeter-wave fully polarimetric SAR data. The coherent dihedral detector which is developed with a more accurate description of a dihedral offers no performance advantage over the noncoherent dihedral detector which is computationally more attractive. The dihedral detectors do a better job of separating a set of tactical military targets from natural clutter compared to a detector that assumes no knowledge about the polarimetric structure of the target signal.

This work develops and tests a new target prescreening algorithm based on 2D gamma kernels. The key feature of the new kernel set is the existence of a free parameter that determines the size of its region of support. We show that the scale affects the false alarm rate of the two parameter CFAR test. We also show that a linear discriminant funtion composed from the linear and quadratic terms of the intensity in the test cell neighborhood improves the false alarm rate when compared with the two parameter CFAR.

This paper evaluates the performance of the recently published wavelet-based algorithm for speckle reduction of SAR images. The original algorithm, based on the theory of wavelet thresholding due to Donoho and Johnstone, has been shown to improve speckle statistics. In this paper, we give more extensive results based on tests performed at Lincoln Laboratory (LL). The LL benchmarks show that the SAR imagery is significantly enhanced perceptually. Although the wavelet processed data results in an increase in the number of natural clutter false alarms, an appropriately modified CFAR detector (i.e., by clamping the estimated clutter standard deviation) eliminates the extra false alarms. The paper also gives preliminary results on the performance of the new and improved wavelet denoising algorithm based on the shift invariant wavelet transform. By thresholding the shift invariant discrete wavelet transform we can further reduce speckle to achieve a perceptually superior SAR image with ground truth information significantly enhanced. Preliminary results on the speckle statistics of this new algorithm is improved over the classical wavelet denoising algorithm. Finally, we show that the classical denoising algorithm as proposed by Donoho and Johnstone and applied to SAR has the added benefit of achieving about 3:1 compression with essentially no loss in image fidelity.

We develop and extensively test a new algorithm for discriminating man-made objects from natural clutter in synthetic-aperture radar (SAR) imagery. The novel feature of our approach is its exploitation of the characteristically distinct variations in speckle pattern for imagery of man-made objects and of natural clutter, as image resolution is varied from coarse to fine. We treat these characteristics using stochastic framework, specifically tailored for multiresolution random processes and fields. Within the framework, we build a pair of multiscale models: one for SAR imagery of natural clutter and another for imagery of man-made objects. We then use these models to define a multiresolution discriminant as the likelihood ratio for distinguishing between the two image types, given a multiresolution sequence of images of a region of interest (ROI). We incorporate this likelihood ratio into an existing, established discriminator that was developed at Lincoln Laboratory (LL) as part of a complete system for automatic target recognition (ATR). To classify a given ROI, we merge the information provided by our likelihood ratio with the measured values of a small number of size and brightness features. We have applied the resulting new discriminator to an extensive data set of 0.3-meter resolution, HH polarization imagery gathered with the LL millimeter-wave SAR. The detection results are extremely good. In particluar, the new discriminator achieves a significant improvement in receiver operating characteristics, compared to an optimized version of the standard discriminator that is traditionally used in the LL ATR system. This result conclusively demonstrates that multiresolution methods have an effective and important role to play in SAR ATR algorithms.

Multi-aperture SAR (MASAR) is an extension of conventional SAR imagery that allows anisotropic returns from man-made objects to be exploited for detection. In this paper, we propose a MASAR ATD algorithm based on hidden Markov models (HMMs). We show that this algorithm economically locates anisotropic target returns. Using simulated L-band MASAR imagery containing M35 trucks, generated by the Xpatch-es software written by Loral, we derive HMM structures that efficiently model the sub-aperture radar return trajectories for target, grass, and tree pixels. We compare HMM detection results for the simulated MASAR imagery over a 105 degree angle of integration with two alternative methods: MASAR split-aperture (SA) change detection and conventional SAR two-parameter CFAR detection. To obtain our results we group detected pixels into target-sized clusters using a clustering algorithm. The results show that HMM detection far outperforms CFAR detection while requiring 1/10th as many FLOPS per pixel. Further, HMM ATD is nearly as accurate as SA change detection while requiring less than 1/500th as many FLOPS per pixel. Finally, for a more practical 45 degree angle of integration, we show that HMM detection and SA chnage detection have equivalent performance, while HMM detection requires less than 1/185th as many FLOPS per pixel.

Interferometric moving target focusing (IMTF) is an interferometric SAR technique which enables high resolution detection, tracking, and imaging of moving ground targets. While IMTF is able to reliably extract clutter canceled moving ground target signatures from three channel interferometric data, traditional SAR autofocusing techniques are unable to reliably focus moving ground target imagery. Because moving ground target motion includes undesired target roll, pitch, and yaw, and for some vehicles, nonrigid body characteristics, more sophisticated autofocusing techniques are needed. This paper briefly presents IMTF and high resolution IMTF imagery. The paper describes promising techniques for the improved focusing of moving ground target imagery. Image improvement is demonstrated by comparing a modified phase gradient autofocusing technique with the unmodified version.

A method for determining the relative motion between an antenna and a target using SAR data is presented. The motion is broken into two parts: translation and rotation. The translation is relative range motion between the antenna and a point fixed with respect to the target. The rotation is defined by an axis of rotation vector with origin at the point and target rotation about the vector. The net result is that four functions of time completely describe the motion, one for translation and three for rotation. For an isolated scatterer the negative of the phase (in cycles) is twice the range to the scatterer divided by the radar wavelength. For real scenes the phase is corrupted by neighboring scatterers and so provides only approximate information about the motion functions. The procedure is to locate strong scatterers, obtain maximum likelihood estimates of the motion from the phase, adjust the pulses according to the motion, and repeat until convergence is achieved. Arbitrary translational motion can be found in this way. However, even with a rigid target assumption, general rotational motion can not be found. The rotation must be restricted in some way. An example where the rotation is restricted to rotation about a fixed axis is included.

Motion of a target induces phase errors in a synthetic-aperture (SAR) signal (phase) history that cause the image of the moving target to be smeared in azimuth. We can detect the presence of a moving target by sensing the smearing, as follows. We divide the complex image of a scene that includes a moving target into a grid of patches, and apply a phase-error correction algorithm to each patch. We have used the shear averaging phase-error correction algorithm. Patches that have large phase errors are likely to contain the image of a moving target. We also compute the sharpness of the image in the patch before and after phase-error correction; an increase in sharpness indicates the presence of a moving target, and it is a better indicator than the magnitude of the phase error. This approach requires just a single-antenna conventional SAR system. It is highly sensitive to the azimuth component of target velocity and to radial acceleration.

Images of suitable quality can be used for detection, classification, identification, and precision targeting. Synthetic aperture radar (SAR) systems are capable of providing such images from standoff ranges. These systems, however, typically image stationary targets and have limited or no capability to image moving targets. Moving target imaging (MTIm) provides a means to focus movers in SAR imagery. Proof of concept was demonstrated by Grumman as early as 1991 using a long range, high resolution radar platform to image moving targets. The radar platform is a key factor in any imaging experiment. Various radar parameters such as range, PRF, resolution, and phase center separation must be analyzed to fully appreciate the advantages as well as the potential caveats associated with a given platform. The results of theoretical analysis and numerical simulations used to evaluate the effect of system parameters on imaging are described and studies regarding the challenges specific to long range MTIm are presented.

Under the sponsorship of the Advanced Research Projects Agency, Lincoln Laboratory has been investigating techniques for creating ultra-high resolution imagery of moving targets for the past year. One application for such a capability would be in extending the time available for engaging high value military targets that are mobile. One such target is a missile-carrying transporter-errector-launcher (TEL).

One approach to obtaining topographic information from SAR imagery is to utilize a multiple- image data collection scheme to collect stereo SAR images. The resulting topographic map, however, is generally of considerably lower resolution than the images themselves. This paper describes a new approach to the construction of topomaps that combines the lower resolution topomap with the SAR intensity images within a variational formulation. The result is a single formulation combining higher spatial resolution than were previously possible.

Interferometric synthetic aperture radars (IFSARs) are powerful tools for topographic mapping. The systems' characteristics: wide swath coverage, automated processing, all- weather operation, and predicted accuracies, give them a unique set of capabilities. IFSAR has been demonstrated from aircraft and satellites, and high-resolution IFSARs achievable from aircraft offer more promise for feature mapping than the satellite systems. We discuss the unique processing steps that are required in the formation of IFSAR terrain data images. Recent work has been done on the automatic recognition of terrain features from IFSAR data. We describe the methods used and predict the variation in the applicability of these methods.

Fast data weighting algorithms applicable to synthetic aperture radar (SAR) image forming are proposed. The problem of image enhancement for non-focused signal processing is discussed. The approach to weighting ftmction hardware implementation and/or its digital realization is presented. SAR ambiguity function (response) characteristics are investigated and optimized. Other techniques of SAR image quality improving are analized. Keywords: SAR image forming, response characteristics improvement, weighting procedures, phase error compensation.

The problem of radar imaging of an object which is moving relative to the coherent bistatic radar is under consideration. Such a radar system is arbitrarily spatially situated. Radar image is formed due to inversed aperture synthesis by phase-coherent accumulation of the reflected signal. In the report the expression for the function characterizing the resolution of the system is obtained. The analysis of this expression for either active and passive radar systems showed the possibility to subdivide the effect for 2 parts. The first part is associated with the aperture synthesis in plane P of the transmitter, the receiver, and the object. THe second part is associated with the synthesis in the ortholognal plane. Careful analysis of the expression was made for the linear uniform movement of the object. It is shown the best resolution in the direction orthogonal to plane P takes place when the velocity of the object is orthogonal to plane P. The appropriate expression for the resolution ability is obtained.

This paper is dedicated to presentation of the Earth's surface polarized radar images treaty technique for the purposes of environmental subsurface survey. The principal idea is based on calculation of anisotropy of electrical constant effective value of terrestrial layer in accordance with radar polarization imaging data. Demands for radar wave length, image on-surface resolution, and sounding beam incident angles range are discussed. Presented techniques could be used for analysis of nondeep dislocated and overlapped by soil tectonic structures and their dynamic detection.

Rail SAR data was analyzed to determine the statistical correlation of background clutter between different UHF frequencies and between passes taken on different days. The purpose of this study was to determine the potential gain from change detection algorithms and multifrequency clutter cancellation algorithms using a high-quality near-ideal SAR data set. The polarimetric radar data used for this study spans a frequency range from 300 to 1200 MHz and had been processed into separate channels with 400 MHz bandwidth. The data is very low noise because of time integration to reduce radio frequency interference (RFI). In this paper, the rail SAR correlation coefficients calculated on data taken on two separate days at the same center frequencies and bandwidths. Secondly, the data was used to explore frequency-to-frequency correlations with correlations calculated for all possible frequency combinations of data taken simultaneously. Both overlapping and nonoverlapping frequency bands were processed. Finally, subtraction studies were performed whereby data from one pass is subtracted from a second pass at the same frequency and data between two frequencies were subtracted using weighted differencing processing. Generally, pass-to-pass correlations on successive days are highest at low frequencies. Correlations approaching .96 between two passes for the lowest frequency band were achieved. These high correlations mean that change detection algorithms can be used and that differencing will result in processing gain. Registration problems will exist with airborne acquired data, that are simplified with rail SAR data. The individual HH and VV polarizations are more highly correlated than the cross polarizations, probably because of higher signal levels relative to noise. Significant clutter reduction (over 10 db) and whitening were demonstrated on data taken in two passes two days apart. The frequency-to-frequency correlations were found to be lower than the pass-to-pass correlations with the level of correlation decreasing with increasing frequecny separation. Some minor whitening was demonstrated for differencing of registered SAR images in nonoverlapping frequencies but no overall clutter reduction was seen.

Using a linear feature mapping framework, a detector and classifier were developed for detecting and recognizing 2D target patterns in data collected by the SRI ultra wideband synthetic aperture radar (SAR), where limited prior knowledge of the target pattern and clutter statistics was known. Preliminary results on 0.1 square kilometers of the illustrative example site show that this linear feature mapping detector (LFMD) improves the receiver operating characteristic (ROC) curve by 1.5 orders of magnitude over the amplitude only 2-parameter constant false alarm rate (CFAR) detector, and the linear feature mapping classifier (LFMC) was able to classify 100% of the targets with no false alarms. In this report, the performances of both LFMD and LFMC are evaluated using ROC curves for 54 square kilometers of UHF SAR clutter data. Extensive experimental performance analysis using the large clutter data set shows that the LFMD and LFMC are very effective in detecting and classifying targets embedded in intense clutter background in UHF SAR data. Performance evaluation on 54 square kilometers of clutter data shows that the LFMD has only 0.3 false alarm per square kilometer and approximately 3 orders of magnitude improvement over the amplitude-only 2- parameter CFAR detector at probability of detection P_d equals 100%. The LFMC yields 100% correct classification by perfectly discriminating against the detected potential targets from the clutter data as nontargets.

Cross-track interferometric SAR can provide 3D radar images. The technique relies on determining the target elevation from the difference in slant range observed by two antennas having a cross-track separation. The range difference is estimated very precisely using the phase difference observed in an interferogram obtained from the two complex images. A key problem is that the range difference can only be determined to within a multiple of the wavelength, as the phase difference is measured modulo 2(pi) . This paper discusses two different methods to determine the unknown multiple of 2(pi) : 1) the split-spectrum algorithm, and 2) the residual delay estimation algorithm. The split-spectrum algorithm utilizes the carrier frequency dependence of the interferometric phase, as subdividing the available range bandwidth into two bands provides two slightly different interferograms. The phase difference of the interferograms corresponds to an interferogram obtained with a system having a carrier frequency which is the difference between the two band centers. THe residual delay estimation method is based on the full bandwidth, one-look images used to form the interferogram and involves precision interpolation and coregistration steps. Principles are presented, along with possible implementations of the algorithms. Principle error sourses, as well as advantages and disadvantages from a processor design and implementation point of view are also discussed.

New shift-invariant distortion-invariant SAR filters for detection and classification are described. They employ new modified MINACE filters. Test results are provided for real SAR data with 360 degree aspect views of 2 different objects and for 100 real SAR clutter chips (natural and man-made) that passed the second stage of the Lincoln Laboratory's SAR processor. We obtained P_D equals 100% and P_FA <EQ 0.82%. The shift-invariance of the filters allow their use as adjuncts for all stages (detection, discrimination, recognition) of a SAR image processor.