Muralidhar Rangaswamy

Parametric adaptive matched filter for airborne radar applications

The parametric adaptive matched filter (PAMF) for space-time adaptive processing (STAP) is introduced via the matched filter (MF), multichannel linear prediction, and the multichannel LDU decomposition. Two alternative algorithmic implementations of the PAMF are discussed. Issues considered include sample training data size and constant false alarm rate (CFAR). Detection test statistics are estimated for airborne phased array radar measurements, and probability of detection is estimated using simulated phased array radar data for airborne surveillance radar scenarios. For large sample sizes, the PAMF performs close to the MF; performance degrades slightly for small sample sizes. In both sample size ranges, the PAMF is tolerant to targets present in the training set.

Space-time adaptive processing: a knowledge-based perspective for airborne radar

This article provides a brief review of radar space-time adaptive processing (STAP) from its inception to state-of-the art developments. The topic is treated from both intuitive and theoretical aspects. A key requirement of STAP is knowledge of the spectral characteristics underlying the interference scenario of interest. Additional issues of importance in STAP include the computational cost of the adaptive algorithm as well as the ability to maintain a constant false alarm rate (CFAR) over widely varying interference statistics. This article addresses these topics, developing the need for a knowledge-based (KB) perspective. The focus here is on signal processing for radar systems using multiple antenna elements that coherently process multiple pulses. An adaptive array of spatially distributed sensors, which processes multiple temporal snapshots, overcomes the directivity and resolution limitations of a single sensor.

MIMO Radar Waveform Design With Constant Modulus and Similarity Constraints

We consider the problem of waveform design for Multiple-Input Multiple-Output (MIMO) radar in the presence of signal-dependent interference embedded in white Gaussian disturbance. We present two sequential optimization procedures to maximize the Signal to Interference plus Noise Ratio (SINR), accounting for a constant modulus constraint as well as a similarity constraint involving a known radar waveform with some desired properties (e.g., in terms of pulse compression and ambiguity). The presented sequential optimization algorithms, based on a relaxation method, yield solutions with good accuracy. Their computational complexity is linear in the number of iterations and trials in the randomized procedure and polynomial in the receive filter length. Finally, we evaluate the proposed techniques, by considering their SINR performance, beam pattern as well as pulse compression property, via numerical simulations.

Statistical analysis of the nonhomogeneity detector for non-Gaussian interference backgrounds

We derive the nonhomogeneity detector (NHD) for non-Gaussian interference scenarios and present a statistical analysis of the method. The non-Gaussian interference scenario is assumed to be modeled by a spherically invariant random process (SIRP). We present a method for selecting representative (homogeneous) training data based on our statistical analysis of the NHD for finite sample support used in covariance estimation. In particular, an exact theoretical expression for the NHD test statistic probability density function (PDF) is derived. Performance analysis of the NHD is presented using both simulated data and measured data from the multichannel airborne radar measurement (MCARM) program. A performance comparison with existing NHD approaches is also included.

Signaling Strategies for the Hybrid MIMO Phased-Array Radar

The hybrid MIMO phased array radar (HMPAR) is a notional concept for a multisensor radar architecture that combines elements of traditional phased-array radar with the emerging technology of multiple-input multiple output (MIMO) radar. A HMPAR comprises a large number, MP, of T/R elements, organized into M subarrays of P elements each. Within each subarray, passive element-level phase shifting is used to steer transmit and receive beams in some desired fashion. Each of the M subarrays are in turn driven by independently amplified phase-coded signals which could be quasi-orthogonal, phase-coherent, or partially correlated. Such a radar system could be used in an airborne platform for concurrent search, detect, and track missions. This paper considers various signaling strategies which could be employed in the notional HMPAR architecture to achieve various objectives quantified by transmit beampatterns and space-time ambiguity functions. First, we propose a method to generate multiple correlated signals for uniform linear and rectangular arrays that achieve arbitrary rectangular transmit beampatterns in one and two dimensions, while maintaining desirable temporal properties. Examples of the range of transmit beampatterns possible with this technique are illustrated for an array of MP=900 elements, arranged using different values of M and P. Then the space-time, or MIMO, ambiguity function that is appropriate for the HMPAR radar system is derived. Examples of ambiguity functions for our signals using a one-dimensional HMPAR architecture are given, demonstrating that one can achieve phased-array-like resolution on receive, for arbitrary transmit beampatterns.

A parametric multichannel detection algorithm for correlated non-Gaussian random processes

This paper addresses the problem of adaptive multichannel signal detection in additive correlated non-Gaussian noise using a parametric model-based approach. The adaptive signal detection problem has been addressed extensively for the case of additive Gaussian noise. However, the corresponding problem for the non-Gaussian case has received limited attention. The additive non-Gaussian noise is assumed to be modeled by a spherically invariant random process (SIRP). The innovations based detection algorithm for the case of constant signal with unknown complex amplitude is derived. The resulting receiver structure is shown to be equivalent to an adaptive matched filter compared to a data dependent threshold. Performance analysis of the derived receiver for the case of a K-distributed SIRV is presented.

Waveform-agile sensing for tracking

Waveform-agile sensing is fast becoming an important technique for improving sensor performance in applications such as radar, sonar, biomedicine, and communications. The paper provided an overview of research work on waveform-agile target tracking. From both control theoretic and information theoretic perspectives, waveforms can be selected to optimize a tracking performance criterion such as minimizing the tracking MSE or maximizing target information retrieval. The waveforms can be designed directly based on their estimation resolution properties, selected from a class of waveforms with varying parameter values over a feasible sampling grid in the time-frequency plane, or obtained from different waveform libraries.

Statistical analysis of the non-homogeneity detector for STAP applications

Abstract : We present a statistical analysis of the recently proposed non-homogeneity detector (NHD) for Gaussian interference statistics. We show that a formal goodness-of-fitness test can be constructed by accounting for the statistics of the generalized inner product (GIP) used as the NHD test statistic. Specifically, the Normalized-GIP is shown to follow 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. Additionally, we address the issue of space-time adaptive processing (STAP) algorithm performance using the NHD as a pre-processing step for training data selection. Performance results for the adaptive matched filter (AMF) method are reported using simulated as well as measured data.

Performance of STAP Tests in Gaussian and Compound-Gaussian Clutter

Abstract 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. The performance of a recently proposed model-based space–time adaptive processing detection method is considered here and compared with several candidate algorithms. Specifically, we consider signal detection in additive disturbance consisting of compound-Gaussian clutter plus Gaussian thermal white noise. Consideration is given to both detection and constant false alarm rate robustness with respect to clutter texture power variations. Finally, the performance of the new test is assessed using small training data support size.

Performance of Parametric and Covariance Based STAP Tests in Compound-Gaussian Clutter

Abstract Michels, J. H., Rangaswamy, M., and Himed, B., Performance of Parametric and Covariance Based STAP Tests in Compound-Gaussian Clutter, Digital Signal Processing12 (2002) 307–328 The 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.