Efficient space-time adaptive processing for airborne MTI-mode radar

Abstract:One main interest of information geometry is to study the properties of statistical models that do not depend on the coordinate systems or model parametrization; thus, it may serve as an analytic tool for intrinsic inference in statistics. In this paper, under the framework of Riemannian geometry and dual geometry, we revisit two commonly-used intrinsic losses which are respectively given by the squared Rao distance and the symmetrized Kullback-Leibler divergence (or Jeffreys divergence). For an exponential family endowed with the Fisher metric and α-connections, the two loss functions are uniformly described as the energy difference along an α-geodesic path, for some α ∈ {−1, 0, 1}. Subsequently, the two intrinsic losses are utilized to develop Bayesian analyses of covariance matrix estimation and range-spread target detection. We provide an intrinsically unbiased covariance estimator, which is verified to be asymptotically efficient in terms of the intrinsic mean square error. The decision rules deduced by the intrinsic Bayesian criterion provide a geometrical justification for the constant false alarm rate detector based on generalized likelihood ratio principle.

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“…which is recognized as the sample covariance matrix of the data set [38]. Assume that n > p; then, S is positive definite with probability one.…”

Section: Covariance Estimationmentioning

confidence: 99%

Intrinsic Losses Based on Information Geometry and Their Applications

Rong

Tang

Zhou

2017

Entropy

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“…Consider a ULA consisting of J channels spaced equally at d meters apart. A CPI for this system will consist of data collected over K slow time pulses with a sampling period of T seconds and L fast time range bins 1 . We shall assume that the system is narrowband, i.e., the 1 Slow time refers to the relatively long intervals between successive pulses from a coherent radar.…”

Section: Signal Modelmentioning

confidence: 99%

“…A CPI for this system will consist of data collected over K slow time pulses with a sampling period of T seconds and L fast time range bins 1 . We shall assume that the system is narrowband, i.e., the 1 Slow time refers to the relatively long intervals between successive pulses from a coherent radar. Fast time refers to the time scale at which the electromagnetic pulse travels across the scene of interest, which is the same as range up to a scale factor.…”

Section: Signal Modelmentioning

confidence: 99%

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A Bayesian perspective on sparse regularization for STAP post-processing

Parker

Potter

2010

2010 IEEE Radar Conference

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Abstract-Traditional Space Time Adaptive Processing (STAP) formulations cast the problem as a detection task which results in an optimal decision statistic for a single target in colored Gaussian noise. In the present work, inspired by recent theoretical and algorithmic advances in the field known as compressed sensing, we impose a Laplacian prior on the targets themselves which encourages sparsity in the resulting reconstruction of the angle/Doppler plane. By casting the problem in a Bayesian framework, it becomes readily apparent that sparse regularization can be applied as a post-processing step after the use of a traditional STAP algorithm for clutter estimation. Simulation results demonstrate that this approach allows closely spaced targets to be more easily distinguished.

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“…The clutter is generated near the target zone, and the existence of a wideband Gaussian-distributed barrage-jammer is introduced. For clutter and jammer suppression, the sample matrix inversion (SMI) method has been used; this includes the clutter and jammer covariance matrix with the subspace-based Digital BeamForming (DBF) algorithm (Guerci, 2013). The DBF algorithm is employed to cover a detection area of a long range (2000 m) and an angular orientation of [90 o , 35.26 o ] with respect to the RADAR platform flying in an airplane under the airborne scenario.…”

Section: Introductionmentioning

confidence: 99%

Automatic Target Classification in GMTI Airborne Scenario

Gupta¹,

Bhaskar²,

Bera³

2016

IJTech

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Ground moving radar target classification is one of the recent research issues that has arisen in an airborne ground moving target indicator (GMTI) scenario. This work presents a novel technique for classifying individual targets depending on their radar cross section (RCS) values. The RCS feature is evaluated using the Chebyshev polynomial. The radar captured target usually provides an imbalanced solution for classes that have lower numbers of pixels and that have similar characteristics. In this classification technique, the Chebyshev polynomial's features have overcome the problem of confusion between target classes with similar characteristics. The Chebyshev polynomial highlights the RCS feature and is able to suppress the jammer signal. Classification has been performed by using the probability neural network (PNN) model. Finally, the classifier with the Chebyshev polynomial feature has been tested with an unknown RCS value. The proposed classification method can be used for classifying targets in a GMTI system under the warfield condition.

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Efficient space-time adaptive processing for airborne MTI-mode radar

Cited by 84 publications

References 7 publications

Intrinsic Losses Based on Information Geometry and Their Applications

Intrinsic Losses Based on Information Geometry and Their Applications

A Bayesian perspective on sparse regularization for STAP post-processing

Automatic Target Classification in GMTI Airborne Scenario

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