An Approach to Ground Moving Target Indication Using Multiple Resolutions of Multilook Synthetic Aperture Radar Images

Ground moving target indication (GMTI) using multiple resolutions of synthetic aperture radar (SAR) images to estimate the clutter scattering statistics is shown to outperform conventional sample matrix inversion space-time adaptive processing GMTI techniques when jamming is not present. A SAR image provides an estimate of scattering from nonmoving targets in the form of a clutter scattering covariance matrix for the GMTI optimum processor. Since the homogeneity of the scattering statistics are unknown, using SAR images at multiple spatial resolutions to estimate the clutter scattering statistics results in more confidence in the final detection decision. Two approaches to calculating the multiple SAR resolutions are investigated. Multiple resolution filter bank smoothing of the full-resolution SAR image is shown to outperform an innovative approach to multilook SAR imaging. The multilook SAR images are calculated from a single measurement vector partitioned base on synthetic sensor locations determined via eigenanalysis of the radar measurement parameters

Phase History Decomposition for Efficient Scatterer Classification in SAR Imagery

A new theory and algorithm for scatterer classification in SAR imagery is presented. The automated classification process is operationally efficient compared to existing image segmentation methods requiring human supervision. The algorithm reconstructs coarse resolution subimages from subdomains of the SAR phase history. It analyzes local peaks in the subimages to determine locations and geometric shapes of scatterers in the scene. Scatterer locations are indicated by the presence of a stable peak in all subimages for a given subaperture, while scatterer shapes are indicated by changes in pixel intensity. A new multi-peak model is developed from physical models of electromagnetic scattering to predict how pixel intensities behave for different scatterer shapes. The algorithm uses a least squares classifier to match observed pixel behavior to the model. Classification accuracy improves with increasing fractional bandwidth and is subject to the high-frequency and wide-aperture approximations of the multi-peak model. For superior computational efficiency, an integrated fast SAR imaging technique is developed to combine the coarse resolution subimages into a final SAR image having fine resolution. Finally, classification results are overlaid on the SAR image so that analysts can deduce the significance of the scatterer shape information within the image context

Propagation parameter estimation in MIMO systems

Multiple antenna techniques are in the heart of modern and next-generation wireless communications systems, such as 3GPP Long-Term Evolution (LTE), IEEE 802.16e (WiMAX), and IMT-Advanced (IMT-A). Such techniques are considered for the high link capacity gains that are achievable from spatial multiplexing, and also for the system capacity, link reliability, and coverage benefits that are possible from spatial diversity, beamforming, and spatial division multiple access techniques. Accurate spatial channel models play a key role on the characterization of the propagation environment and determination of which techniques provide higher gains in a given scenario. Such models are also fundamental tools in network planning, link and system performance studies, and transceiver development. Realistic channel models are based on measurements. Hence, there is a need for techniques that extract the relevant information from huge amount of data. This may be achieved by estimating model parameters from the data. Most estimation algorithms are based on the assumption that the channel can be modeled as a combination of a finite number of specular, highly-concentrated paths, requiring estimation of a very large number of parameters. In this thesis, estimators are derived for the parameters of the concentrated propagation paths and the diffuse scattering component that are frequently observed in Multiple-Input Multiple-Output (MIMO) channel sounding measurements. Low complexity methods are derived for efficient computation of the estimates. The derived methods are based on a stochastic channel model, leading to a lower-dimensional parameter set that allow a reduction in computational complexity and improved statistical performance compared to methods found in the literature. Simulation results demonstrate that high quality estimates are obtained. The large sample performance of the estimators are studied by establishing the Cramér-Rao lower bound (CRLB) and comparing it to the variances of the estimates. The simulations show that the variances of the proposed estimation techniques attain the CRLB for relatively small sample size for most parameters, and no bias is observed

An estimation-theoretic technique for motion-compensated synthetic-aperture array imaging

Thesis (Sc.D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2000.Vita.Includes bibliographical references (p. 351-354).Synthetic-Aperture Radar (SAR) is an imaging technique that achieves high azimuth resolution by using coherent processing to exploit the relative motion between an airborne or spaceborne radar antenna and the imaged target field (effectively synthesizing the effect of a larger aperture array). From an estimation-theoretic perspective, this thesis addresses the following limitations of conventional imaging techniques for the spotlight-mode version of SAR: sidelobe imaging artifacts and loss of resolution for stationary SAR scenes containing high-amplitude scatterers, and blurring and object-displacement artifacts in the presence of moving targets. First, this thesis presents a generalized estimation-theoretic SAR imaging framework which exploits the idea of L1-norm regularization. Some results are included which demonstrate the utility of this approach for reducing sidelobes and improving resolution for stationary SAR images. A parameterized L-norm-based moving-target imaging technique is also presented. For the case of a single moving target, this technique is able to compensate for the blurring due to temporally-constant velocity rigid-body motion (even if the target scatterers are closely-spaced). However, the motion-induced object-displacement compensation performance of this technique is significantly affected by velocity estimation errors. This thesis also presents an estimation-theoretic moving-target SAR imaging framework which uses a multi-dimensional matched-filter for computing a set of scatterer-velocity estimates which are used as initial conditions for an L1-norm-based estimation algorithm which assumes that the target scatterers have temporally-constant spatially-independent velocities. Therefore, this framework is able to image a moving target and nearby high-amplitude stationary clutter simultaneously. This framework also shows potential for imaging targets with non-rigid body motion. However, the motion-induced object-displacement compensation performance of this approach is significantly affected by cross-scatterer interference effects.by Cedric Leonard Logan.Sc.D

Adaptive radar detection in the presence of textured and discrete interference

Under a number of practical operating scenarios, traditional moving target indicator (MTI) systems inadequately suppress ground clutter in airborne radar systems. Due to the moving platform, the clutter gains a nonzero relative velocity and spreads the power across Doppler frequencies. This obfuscates slow-moving targets of interest near the "direct current" component of the spectrum. In response, space-time adaptive processing (STAP) techniques have been developed that simultaneously operate in the space and time dimensions for effective clutter cancellation. STAP algorithms commonly operate under the assumption of homogeneous clutter, where the returns are described by complex, white Gaussian distributions. Empirical evidence shows that this assumption is invalid for many radar systems of interest, including high-resolution radar and radars operating at low grazing angles. We are interested in these heterogeneous cases, i.e., cases when the Gaussian model no longer suffices. Hence, the development of reliable STAP algorithms for real systems depends on the accuracy of the heterogeneous clutter models. The clutter of interest in this work includes heterogeneous texture clutter and point clutter. We have developed a cell-based clutter model (CCM) that provides simple, yet faithful means to simulate clutter scenarios for algorithm testing. The scene generated by the CMM can be tuned with two parameters, essentially describing the spikiness of the clutter scene. In one extreme, the texture resembles point clutter, generating strong returns from localized range-azimuth bins. On the other hand, our model can also simulate a flat, homogeneous environment. We prove the importance of model-based STAP techniques, namely knowledge-aided parametric covariance estimation (KAPE), in filtering a gamut of heterogeneous texture scenes. We demonstrate that the efficacy of KAPE does not diminish in the presence of typical spiky clutter. Computational complexities and susceptibility to modeling errors prohibit the use of KAPE in real systems. The computational complexity is a major concern, as the standard KAPE algorithm requires the inversion of an MNxMN matrix for each range bin, where M and N are the number of array elements and the number of pulses of the radar system, respectively. We developed a Gram Schmidt (GS) KAPE method that circumvents the need of a direct inversion and reduces the number of required power estimates. Another unavoidable concern is the performance degradations arising from uncalibrated array errors. This problem is exacerbated in KAPE, as it is a model-based technique; mismatched element amplitudes and phase errors amount to a modeling mismatch. We have developed the power-ridge aligning (PRA) calibration technique, a novel iterative gradient descent algorithm that outperforms current methods. We demonstrate the vast improvements attained using a combination of GS KAPE and PRA over the standard KAPE algorithm under various clutter scenarios in the presence of array errors.Ph.D