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In this article, we consider the problem of estimating the location and velocity of a moving source using a distributed passive radar sensor network. We first derive the maximum likelihood estimator (MLE) using direct sensor observations, when the source signal is unknown and modeled as a deterministic process. Since the MLE obtains the source location and velocity estimates through a search process over the parameter space and is quite computationally intensive, we also developed an efficient algorithm to solve the problem using a two-step approach. The first step finds the time difference of arrival (TDOA) and frequency difference of arrival (FDOA) estimates for each sensor with respect to a reference sensor by using a two-dimensional fast Fourier transform and interpolation, while the second step employs an iterative reweighted least square (IRLS) approach with a varying weighting matrix to determine the source location and velocity. To benchmark the performance of the proposed methods, a constrained Cramér–Rao bound (CRB) for the considered source localization problem is derived. Numerical results show that the IRLS approach has a lower signal-to-noise ratio threshold phenomenon compared with several recent TDOA/FDOA-based methods, especially when the source is considerably farther away from some sensors than others, creating a larger disparity in the quality of sensors observations. [ABSTRACT FROM AUTHOR]

This paper deals with the problem of detecting a subspace signal in the presence of spatially and temporally colored disturbance. A new subspace parametric signal model that takes into account a multi-rank subspace structure for the target signal and employs a multi-channel auto-regressive process for the disturbance signal is proposed. Following this model, a new subspace parametric Rao detector (SP-Rao) is developed for training-limited scenarios. Unlike conventional parametric detectors that are designed for only rank-one signal detection, the SP-Rao has a new multi-rank structure with a pairwise successive spatio-temporal whitening and cross-correlation process between the observed signal and each subspace basis vector. Additionally, a non-parametric subspace detector (NSD) is derived based upon a frequency-domain representation of the SP-Rao test statistic. The NSD is distinctively different from conventional subspace detectors, in which the former involves pairwise whitening and cross-correlation between the test signal and each subspace basis vector but the latter employs the whole subspace matrix. Numerical results are presented to illustrate the performance of the proposed subspace detectors in comparison with several leading existing methods, especially in the case of limited data. [ABSTRACT FROM AUTHOR]

A new form of space-time adaptive processing (STAP) is presented that leverages additional training data obtained from waveform-diverse pulse compression filters possessing low cross-correlation with the primary waveform that is used for traditional airborne and space-based ground moving target indication. In contrast to traditional training data in which clutter and targets are “focused” in range via pulse compression of the primary waveform, this new set of training data possesses a “smeared” range response that better approximates the identically distributed assumption made during sample covariance estimation. The Multi-Waveform STAP (MuW-STAP or simply $\mu $-STAP) formulation is shown for both multiple-input multiple-output and single-input multiple-output configurations, with the former retaining the spatially-focused primary emission supplemented by low-power secondary emissions that illuminate sidelobe clutter and the latter a special case of the former. In simulation, signal to interference plus noise ratio analysis reveals enhanced robustness to nonstationary interference compared to standard STAP training data. [ABSTRACT FROM AUTHOR]

This paper deals with the problem of detecting a moving range-spread target in distributed MIMO radar. A new knowledge-aided (KA) model that takes into account the nonhomogenous characteristics of the disturbance (clutter and noise) in distributed MIMO radar is proposed. Specifically, the disturbance covariance matrices corresponding to different transmit-receive (Tx-Rx) pairs are modeled as random matrices. These covariance matrices share a prior covariance matrix structure but with different power levels to model the nonhomogeneous clutter powers across different Tx-Rx pairs. Two cases are considered, involving either no range training (i.e., when the disturbance is highly nonhomogeneous) or some range training data. For the first case, we develop a KA generalized likelihood ratio test (GLRT) for range-spread target detection, along with a simplified version of the KA-GLRT for point-like target detection. For the second case, the KA-GLRT becomes computationally intractable, a simple ad-doc KA detector is introduced to take advantage of training data for range-spread target detection. Simulation results are presented to illustrate the performance and effectiveness of the proposed detectors in nonhomogeneous environments. [ABSTRACT FROM AUTHOR]

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 problem significantly more challenging. Two new detectors are proposed. The first is a sparsity based detector which, by exploiting a sparse representation of the clutter in the Doppler domain, adaptively estimates from the test signal the clutter subspace, which is in general distinct for different transmit/receive pairs and, moreover, may spread over the entire Doppler bandwidth. The second is a fully adaptive parametric detector which employs a parametric autoregressive clutter model and offers joint model order selection, clutter estimation/mitigation, and target detection in an integrated and fully adaptive process. Both detectors are developed within the generalized likelihood ratio test (GLRT) framework, obviating the need for training signals that are indispensable for conventional detectors but are difficult to obtain in practice due to clutter non-homogeneity. Numerical results indicate that the proposed training-free detectors offer improved detection performance over covariance matrix based detectors when the latter have a moderate amount of training signals. [ABSTRACT FROM PUBLISHER]

We examine the adaptive detection problem in the presence of colored noise with an unknown covariance matrix, by exploiting a persymmetric structure in the received signal. The persymmetric adaptive matched filter (PS-AMF) is used to address this problem, which can significantly alleviate the requirement of secondary data. In G. Pailloux et al. “Persymmetric adaptive radar detectors,” (2011) the probability of false alarm of the PS-AMF has been obtained in terms of the Gaussian hypergeometric function. In this paper, finite-sum expressions for the probability of false alarm of the PS-AMF are derived, which are more convenient to use in calculating the detection threshold. Moreover, the detection probabilities of the PS-AMF for both nonfluctuating and fluctuating target models are derived. In the fluctuating model, the amplitude of the target echoes is described by a generalized Chi distribution that involves the Rayleigh distribution as a special case. These theoretical results are all confirmed using Monte Carlo (MC) simulations. [ABSTRACT FROM PUBLISHER]

Based on persymmetric structures in received signals, we consider the adaptive detection problem in colored Gaussian noise with unknown persymmetric covariance matrix in a multiple-input, multiple-output (MIMO) radar with spatially dispersed antennas. To this end, a set of secondary data for each transmit-receive pair is assumed to be available. A MIMO version of the persymmetric generalized likelihood ratio test (MIMO-PGLRT) detector is proposed. A closed-form expression for the probability of false alarm of this detector is derived. In addition, a MIMO version of the persymmetric sample matrix inversion (MIMO-PSMI) detector is also developed. Compared to the MIMO-PGLRT detector, MIMO-PSMI has a simpler form and is computationally more efficient. Numerical examples are provided to demonstrate that the proposed two detection algorithms can significantly alleviate the requirement of the amount of secondary data and allow for a noticeable improvement in detection performance. [ABSTRACT FROM AUTHOR]

It is known that the probabilities of false alarm (PFAs) of several celebrated adaptive detectors including the adaptive matched filter (AMF) and the adaptive coherence estimator (ACE) can be expressed as integral forms. Nevertheless, it is inconvenient to set the detection thresholds by using these integral expressions. Here, we propose two computationally efficient schemes to calculate the thresholds of the AMF and ACE. In the first method, approximate expressions, in forms of elementary functions, for the PFAs of the AMF and ACE are derived. The thresholds of the AMF and ACE can be numerically computed by using these elementary expressions instead of the integrals, for reducing computational complexity. In the second approach, further approximations are employed to lead to highly simple expressions for the thresholds of the AMF and ACE, which enable us to directly compute the thresholds for a given PFA. Compared to the first one, the second scheme is more computationally efficient, but at the cost of a slight loss in accuracy. Numerical results verify the effectiveness of the two proposed schemes. [ABSTRACT FROM AUTHOR]

In this letter, we consider the detection of a multichannel signal with an unknown amplitude in colored noise, when there is a covariance mismatch between the null and alternative hypotheses. Specifically, the covariance mismatch is caused by a target-induced subspace interference that is present only under the alternative hypothesis. According to the signal model, we propose a detector involving the following steps. The observation is first projected to the orthogonal complement of the signal to be detected, followed by a second projection to the interference subspace. Then, the energy of the doubly projected signal (residual) is computed. If the residual energy is small, the proposed detector reduces to the standard matched filter (MF), which ignores the subspace interference; otherwise, a modified test statistic is employed for additional interference cancellation. Simulation results are presented to demonstrate the effectiveness of the proposed detector. [ABSTRACT FROM PUBLISHER]