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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(More)
This paper addresses target detection in passive multiple-input multiple-output (MIMO) radar networks comprised of non-cooperative transmitters and multichannel receivers. A generalized likelihood ratio test is derived, and approximate test statistic distributions are presented for both hypotheses under common scenario conditions. Analysis and simulation(More)
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(More)
In order to improve upon automated sensor performance for security applications in public and private settings, numerous alternative sensor designs have been developed to provide affordable and effective detection and identification performance. Radio frequency (RF) sensors offer a balanced approach to system design for a wide variety of geome-tries and(More)
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(More)
This paper examines moving target detection in distributed multi-input multi-output radar with sensors placed on moving platforms. Unlike previous works which were focused on stationary platforms, we consider explicitly the effects of platform motion, which exacerbate the location-induced clutter non-homogeneity inherent in such systems and thus make the(More)
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(More)