Polarimetric Matched Filter Research Articles

The Polarimetric Detection Optimization Filter and its Statistical Test for Ship Detection

Ship detection via synthetic aperture radar (SAR) has been demonstrated to be very useful as polarimetric information helps discriminate between targets and sea clutter. Among the available polarimetric detectors, optimal polarimetric detection (OPD) theoretically provides the best detection performance under the assumption that the fully developed speckle hypothesis stands. This study proposes a polarimetric detection optimization filter (PDOF). The target clutter ratio (TCR) over the speckle variation was maximized using a matrix transform to derive the PDOF. The objective function based on a matrix transform instead of a vector transform is optimized to obtain synthetic effects by combining a polarimetric whitening filter (PWF) and a polarimetric matched filter (PMF). Subspace form of the PDOF (SPDOF) is also proposed, which gives performance comparable to the PDOF. Assuming a Wishart distribution, the exact and approximate expressions of the closed-form probability density function (PDF) of the PDOF are derived. The probability of false alarm (PFA) was derived in a closed-form expression, which allows obtaining the PDOF threshold analytically. Moreover, the gamma model is extended to a generalized gamma distribution ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\text{G}\Gamma \text{D}$ </tex-math></inline-formula> ) to adapt complicated resolutions and sea states. Experiments with simulated and real data validate the correctness and effectiveness of the results. The PDOF detector achieves the best performance in most virtual and real-world environments, especially in cases where the target statistics and clutter are not Wishart-distributed.

Preliminary Results of Multichannel SAR-GMTI Experiments for Airborne Quad-Pol Radar System

Much research from open literature shows that polarization diversity can provide another dimension which may be exploited to improve the performance in ground moving target indication (GMTI), compared with space–time adaptive processing (STAP). In this article, we report the multichannel synthetic aperture radar (SAR)-based GMTI (SAR-GMTI) experiment and its preliminary results with a N-SAR system which is an airborne quadrature-polarimetric (quad-pol) radar system. First, the joint polarization-space adaptive processing (JPolSAP) is performed in the image level, but two suboptimal versions of JPolSAP, where the polarimetric matched filter (PMF) vector and the full-one vector are exploited to substitute for the polarimetric steering vector, respectively, are evaluated since the polarimetric steering vector of the moving target is unknown precisely in practical applications. Then, considering the computational complexity and lack of secondary data in a inhomogeneous environment due to high degrees of freedom of the JPolSAP processor, two cascade processors are evaluated, including the polarization enhancement that uses PMF and noncoherence integration detection (NCID) technique. Furthermore, we utilize the polarization information to accomplish SAR terrain classification, and subsequently secondary data from the same scattering type clutter can be obtained for clutter suppression under the guidance of polarization classification results as a priori knowledge. Finally, the experimental results demonstrate that: 1) the suboptimal JPolSAP processor with PMF steering vector can effectively enhance GMTI performance about 13 dB (or even up to 5 dB) relative to the worst (or best) single-polarization (S-pol) processor case and has the best robustness compared with the one with full-one steering vector; 2) polarization enhancement using PMF also obtains a good output gain of polarization filter, especially for quad-pol enhancement, which gains up to 2–3 dB with respect to the best output of S-pol processor, and the NCID technique can obtain good performance of moving-target detection; and 3) under the guidance of polarization classification results, the capability of clutter suppression can improve even up to 15 dB with respect to the one without classification.

Differential Faraday Rotation and Polarimetric SAR

The propagation of linearly polarized electromagnetic waves through the Earth's ionosphere is accompanied by Faraday rotation (FR), which is due to gyrotropy of the ionospheric plasma in the magnetic field of the Earth. FR may cause distortions of the images taken by spaceborne polarimetric synthetic aperture radar (SAR). We show that the mechanism of those distortions is related to the variation of the FR angle within the bandwidth of the interrogating signals sent by the radar. This effect has not been analyzed previously in the context of SAR imaging. We also propose a special matched filter that we call the polarimetric matched filter (PMF). The PMF helps correct the FR-induced distortions and provides a provably superior SAR performance compared to the case of the traditional polarimetric SAR signal processing.

Optimal polarization for contrast enhancement in polarimetric synthetic aperture radar using a genetic algorithm

Polarimetric scattering information provided by fully polarimetric synthetic aperture radar (SAR) is useful for discriminating between different terrain covers. Conventionally, eigenanalysis is used to synthesize a polarimetric matched filter (PMF) that maximizes the contrast between the features of interest, but this involves many algebraic operations, and only one contrast ratio for any two classes can be obtained at one time. This paper describes the application of a genetic algorithm to the contrast enhancement. When it is used, all the contrast ratios characterizing the best discrimination for any two of all possible classes can be obtained simultaneously, providing a new and more convenient way to solve the problem of the optimization of polarimetric contrast enhancement. Two independent sets of L-band fully polarimetric SAR images are used as test data; the results show that the proposed method is very effective and promising.

Studies of target detection algorithms that use polarimetric radar data

Algorithms are described which make use of polarimetric radar information in the detection and discrimination of targets in a ground clutter background. The optimal polarimetric detector (OPD) is derived. This algorithm processes the complete polarization scattering matrix (PSM) and provides the best possible detection performance from polarimetric radar data. Also derived is the best linear polarimetric detector, the polarimetric matched filter (PMF), and the structure of this detector is related to simple polarimetric target types. New polarimetric target and clutter models are described and used to predict the performance of the OPD and the PME. The performance of these algorithms is compared with that of simpler detectors that use only amplitude information to detect targets. The ability to discriminate between target types by exploring differences in polarimetric properties is discussed. >

Optimal polarizations for achieving maximum contrast in radar images

There is considerable interest in determining the optimal polarizations that maximize contrast between two scattering classes in polarimetric radar images. In this paper a systematic approach is presented for obtaining the optimal polarimetric matched filter, i.e., that filter which produces maximum contrast between two scattering classes. The maximization procedure involves solving an eigenvalue problem where the eigenvector corresponding to the maximum contrast ratio is optimal polarimetric matched filter. To exhibit the physical significance of this filter, it is transformed into its associated transmitting and receiving polarization states, written in terms of horizontal and vertical vector components. For the special case where the transmitting polarization is fixed, the receiving polarization which maximizes the contrast ratio is also obtained. Polarimetric filtering is then applied to synthetic aperture radar images obtained from the Jet Propulsion Laboratory. It is shown, both numerically and through the use of radar imagery, that maximum image contrast can be realized when data is processed with the optimal polarimetric matched filter.