Braham Himed

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.

Sparsity-based DOA estimation using co-prime arrays

In this paper, we propose co-prime arrays for effective direction-of-arrival (DOA) estimation. To fully utilize the virtual aperture achieved in the difference co-array constructed from a co-prime array structure, sparsity-based spatial spectrum estimation technique is exploited. Compared to existing techniques, the proposed technique achieves better utilization of the co-array aperture and thus results in increased degrees-of-freedom as well as improved DOA estimation performance.

Transmit Subaperturing for MIMO Radars With Co-Located Antennas

We present a transmit subaperturing (TS) approach for multiple-input multiple-output (MIMO) radars with co-located antennas. The proposed scheme divides the transmit array elements into multiple groups, each group forms a directional beam and modulates a distinct waveform, and all beams are steerable and point to the same direction. The resulting system is referred to as a TS-MIMO radar. A TS-MIMO radar is a tunable system that offers a continuum of operating modes from the phased-array radar, which achieves the maximum directional gain but the least interference rejection ability, to the omnidirectional transmission based MIMO radar, which can handle the largest number of interference sources but offers no directional gain. Tuning of the TS-MIMO system can be easily made by changing the configuration of the transmit subapertures, which provides a direct tradeoff between the directional gain and interference rejection power of the system. The performance of the TS-MIMO radar is examined in terms of the output signal-to-interference-plus-noise ratio (SINR) of an adaptive beamformer in an interference and training limited environment, where we show analytically how the output SINR is affected by several key design parameters, including the size/number of the subapertures and the number of training signals. Our results are verified by computer simulation and comparisons are made among various operating modes of the proposed TS-MIMO system.

STAP with angle-Doppler compensation for bistatic airborne radars

We study issues associated with applying space-time adaptive processing (STAP) techniques in bistatic airborne applications. We consider the performance of several STAP approaches in different scenarios. Specific consideration is given to the effects of bistatic clutter spectral dispersion on covariance estimation and the algorithms resulting clutter rejection capability. Our prime focus emphasizes adaptive processing methods capable of high performance with efficient utilization of training data. A deterministic two-dimensional spectral compensation is used to align the clutter spectral centers and thus enhance the performance of the proposed approaches. Algorithm performance is assessed using the output signal-to-interference-plus-noise ratio (SINR) compared to that of the matched filter with known covariance.

Integrated Cubic Phase Function for Linear FM Signal Analysis

In this paper, an integrated cubic phase function (ICPF) is introduced for the estimation and detection of linear frequency-modulated (LFM) signals. The ICPF extends the standard cubic phase function (CPF) to handle cases involving low signal-to-noise ratio (SNR) and multi-component LFM signals. The asymptotic mean squared error (MSE) of an ICPF-based estimator as well as the output SNR of an ICPF-based detector are derived in closed form and verified by computer simulation. Comparison with several existing approaches is also included, which shows that the ICPF serves as a good candidate for LFM signal analysis.

Moving Target Detection Using Distributed MIMO Radar in Clutter With Nonhomogeneous Power

In this paper, we consider moving target detection using a distributed multiple-input multiple-output (MIMO) radar on stationary platforms in nonhomogeneous clutter environments. Our study is motivated by the fact that the multistatic transmit-receive configuration in a distributed MIMO radar causes nonstationary clutter. Specifically, the clutter power for the same test cell may vary significantly from one transmit-receive pair to another, due to azimuth-selective backscattering of the clutter. To account for these issues, a new nonhomogeneous clutter model, where the clutter resides in a low-rank subspace with different subspace coefficients (and hence different clutter power) for different transmit-receive pair, is introduced and the relation to a general clutter model is discussed. Following the proposed clutter model, we develop a generalized-likelihood ratio test (GLRT) for moving target detection in distributed MIMO radar. The GLRT is shown to be a constant false alarm rate (CFAR) detector, and the test statistic is a central and noncentral Beta variable under the null and alternative hypotheses, respectively. Simulations are provided to demonstrate the performance of the proposed GLRT in comparison with several existing techniques.

Interference Mitigation Processing for Spectrum-Sharing Between Radar and Wireless Communications Systems

The theoretical feasibility is explored of spectrum-sharing between radar and wireless communications systems via an interference mitigation processing approach. The new approach allows radar and wireless systems to operate at the same carrier frequency if the radar possesses a multiple-input multiple-output (MIMO) structure. A novel signal processing approach is developed for coherent MIMO radar that effectively minimizes the arbitrary interferences generated by wireless systems from any direction, while operating at the same frequency using cognitive radio technology. Various theoretical aspects of the new approach are investigated, and its effectiveness is further validated through simulation.

Parametric GLRT for Multichannel Adaptive Signal Detection

This paper considers the problem of detecting a multichannel signal in the presence of spatially and temporally colored disturbance. A parametric generalized likelihood ratio test (GLRT) is developed by modeling the disturbance as a multichannel autoregressive (AR) process. Maximum likelihood (ML) parameter estimation underlying the parametric GLRT is examined. It is shown that the ML estimator for the alternative hypothesis is nonlinear and there exists no closed-form expression. To address this issue, an asymptotic ML (AML) estimator is presented, which yields asymptotically optimum parameter estimates at reduced complexity. The performance of the parametric GLRT is studied by considering challenging cases with limited or no training signals for parameter estimation. Such cases (especially when training is unavailable) are of great interest in detecting signals in heterogeneous, fast changing, or dense-target environments, but generally cannot be handled by most existing multichannel detectors which rely more heavily on training at an adequate level. Compared with the recently introduced parametric adaptive matched filter (PAMF) and parametric Rao detectors, the parametric GLRT achieves higher data efficiency, offering improved detection performance in general.

Complex multitask Bayesian compressive sensing

An effective complex multitask Bayesian compressive sensing (CMT-BCS) algorithm is proposed to recover sparse or group sparse complex signals. The existing multitask Bayesian compressive sensing (MT-CS) algorithm is powerful in recovering multiple real-valued sparse solutions. However, a large class of sensing problems deal with complex values. A simple approach, which decomposes a complex value into independent real and imaginary components, does not take into account the group sparsity of these two components and thus yields poor recovery performance. In this paper, we first introduce the CMT-BCS algorithm that jointly treats the real and imaginary components, and then derive a fast and accurate algorithm for the estimation of the prior parameters by solving a surrogate convex function. The proposed CMT-BCS algorithm achieves effective complex sparse signal recovery and outperforms MT-CS and complex group Lasso.

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.