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An automatic target recognition (ATR) differential game between a radar that moves with simple motion and a turn-constrained target is examined in which a radar continuously acquires information about a target until the prescribed information requirement is fulfilled. The radar strives to satisfy the information requirement in minimum time while the target strives to prolong the engagement for as long as possible where the information collection rate is dependent on the relative geometry of the system. We develop the regular equilibrium conditions and the singular solution conditions for the nine singular surfaces within the global solution. We provide illustrative examples of equilibrium state trajectories that are constructed by solving a multipoint boundary value problem where we combine regular and singular solution segments in a piecewise manner. Using the singular surfaces within the global solution of the game, we synthesize the equilibrium feedback controller for the target. The performance of alternative, subequilibrium radar controllers is tested against the synthesized controller to show the Nash equilibrium of the calculated equilibrium control strategies.

We derive several parametric detectors for the case of a ground-based distributed multiple-input multiple-output radar system with colocated transmitters and receivers. The proposed detectors are based on exact and approximate generalized likelihood ratio tests. For the transmit–receive paths, which start and end at the same node, we incorporate the constraint of spectral symmetry, meaning that the clutter returns are assumed to have a power spectral density that is symmetric around the zero Doppler, which is motivated by statistical analyses performed on measured clutter radar data. Our numerical simulations suggest that the proposed parametric detectors, which incorporate the physics-based spectral symmetry constraint on clutter returns, achieve better performance than previously investigated parametric detection approaches which do not incorporate this constraint.

This paper considers the detection of a target in a distributed radar system, wherein the received reflections from the said target are contaminated with heterogeneous clutter returns. We model this clutter, and possibly additional interference, as a superposition of an auto-regressive process and deterministic interference coming from a known subspace. Parametric adaptive detectors for this scenario are derived based on the Generalized Likelihood Ratio (GLR), Rao, and Wald tests, and are analyzed and compared with their non-adaptive, clairvoyant counterparts, which assume a priori knowledge of the auto-regressive process parameters. Using numerical simulations, the proposed detectors are shown to have detection performances that are robust to strong subspace interference.

Michels, James H., Himed, Braham, and Rangaswamy, Muralidhar, Performance of STAP Tests in Gaussian and Compound-Gaussian Clutter, Digital Signal Processing, 10(2000), 309–324.

In classical parameter estimation settings, sensor observation models are often assumed to be known. However, when the sensors themselves become unreliable, the traditional observation models may no longer hold. It is then expected that estimation performance would degrade due to the abnormal behavior of sensor observations. We formulate the estimation problem as a two-person zero-sum game and propose a mini-max estimator with the optimization goal to minimize the worst possible estimation error. We show that there exists a saddle-point solution for a single sensor observation. We then apply our result and characterize the estimation performance for networks with multiple sensors. [ABSTRACT FROM AUTHOR]

In this paper, we consider resolving over-the-horizon radar (OTHR) Doppler returns. A high-resolution time-frequency (TF) representation of the received signal is obtained by using the local polynomial Fourier transform (LPFT). From the optimally concentrated LPFT, multicomponent Doppler signatures, which are only several frequency bins apart, are extracted using an instantaneous frequency estimation method based on the Viterbi algorithm. The performance of the proposed method is validated using real data.

We present a statistical analysis of the recently proposed non-homogeneity detector (NHD) for Gaussian interference statistics. Specifically, we show that a formal goodness-of-fit test can be constructed by accounting for the statistics of the generalized inner product (GIP) used as the NHD test statistic. The normalized-GIP follows a central-F distribution and admits a canonical representation in terms of two statistically independent chi-squared distributed random variables. Moments of the GIP can be readily calculated as a result. These facts are used to derive the goodness-of-fit tests, which facilitate intelligent training data selection. We then address the issue of space–time adaptive processing (STAP) algorithm performance using the NHD as a pre-processing step for training data selection. Performance of the adaptive matched filter (AMF) method is reported using simulated as well as measured data. [Copyright &y& Elsevier]

Michels, J. H., Rangaswamy, M., and Himed, B., Performance of Parametric and Covariance Based STAP Tests in Compound-Gaussian Clutter, Digital Signal Processing12 (2002) 307–328The performance of a parametric space–time adaptive processing method is presented here. Specifically, we consider signal detection in additive disturbance containing compound-Gaussian clutter plus additive Gaussian thermal white noise. Performance is compared to the normalized adaptive matched filter and the Kelly GLRT receiver using simulated and measured data. We focus on the issues of detection and false alarm probabilities, constant false alarm rate, robustness with respect to clutter texture power variations, and reduced training data support. [Copyright &y& Elsevier]