Showing papers by "Pu Wang published in 2012"

A Hybrid CPF-HAF Estimation of Polynomial-Phase Signals: Detailed Statistical Analysis

Abstract: In this paper, we consider parameter estimation of high-order polynomial-phase signals (PPSs). We propose an approach that combines the cubic phase function (CPF) and the high-order ambiguity function (HAF), and is referred to as the hybrid CPF-HAF method. In the proposed method, the phase differentiation is first applied on the observed PPS to produce a cubic phase signal, whose parameters are, in turn, estimated by the CPF. The performance analysis, carried out in the paper, considers up to the tenth-order PPSs, and is supported by numerical examples revealing that the proposed approach outperforms the HAF in terms of the accuracy and signal-to-noise-ratio threshold. Extensions to multicomponent and multidimensional PPSs are also considered, all supported by numerical examples. Specifically, when multicomponent PPSs are considered, the product version of the CPF-HAF outperforms the product HAF (PHAF) that fails to estimate parameters of components whose PPS order exceeds three.

Persymmetric Parametric Adaptive Matched Filter for Multichannel Adaptive Signal Detection

Abstract: This correspondence considers a parametric approach for multichannel adaptive signal detection in Gaussian disturbance which can be modeled as a multichannel autoregressive (AR) process and, moreover, possesses a persymmetric structure induced by a symmetric antenna geometry. By introducing the persymmetric AR (PAR) modeling for the disturbance, a persymmetric parametric adaptive matched filter (Per-PAMF) is proposed. The developed Per-PAMF extends the classical PAMF by exploiting the underlying persymmetric properties and, hence, improves the detection performance in training-limited scenarios. The performance of the proposed Per-PAMF is examined by the Monte Carlo simulations and simulation results demonstrate the effectiveness of the Per-PAMF compared with the conventional PAMF and nonparametric detectors.

Generalised parametric rao test for multi-channel adaptive detection of range-spread targets

Abstract: This study considers the problem of detecting a multi-channel signal of range-spread targets in a homogeneous environment, where the disturbances in both test signal and training signals share the same covariance matrix. To this end, a generalised parametric Rao (GP-Rao) test is developed by modelling the disturbance as a multi-channel auto-regressive process. The GP-Rao test uses less training data and is computationally more efficient, when compared with conventional covariance matrix-based solutions. The theoretical detection performance of the GP-Rao test is characterised in terms of the asymptotic distribution under both hypotheses. Numerical results indicate that the proposed GP-Rao test attains asymptotically the constant false alarm rate property. Numerical results show that the GP-Rao test achieves better detection performance and uses significantly less training signals than the covariance matrix-based approach.

Detection With Target-Induced Subspace Interference

Abstract: 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.

Recursive moving target detection with distributed MIMO radar in clutter with non-homogeneous power

Abstract: Our previous study addresses moving target detection (MTD) using a distributed multiple-input multiple-output (MIMO) radar in clutter with non-homogeneous power. The developed detector, referred to as the MIMO-GLRT detector, assumes perfect knowledge of the clutter subspace and uses the assumed clutter subspace to construct a projection matrix which is required to compute the test statistic. In this work, we take into account uncertainties on the clutter subspace, i.e., the subspace dimension is not known a priori, and develop a recursive version of the MIMO-GLRT detector by integrating a computationally efficient updating algorithm for the subspace projection matrix from one iteration to another, together with a generalized Akaike Information criterion (GAIC) for the subspace dimension selection. Simulation results with a synthesized dataset and a general clutter dataset are provided to demonstrate the performance degradation of the standard MIMO-GLRT detector when an over-estimated or under-estimated clutter subspace is used, and show that the recursive MIMO-GLRT detector is able to mitigate such degradation by choosing a proper clutter subspace directly from the received signals.

Target velocity estimation and CRB with distributed MIMO radar in non-homogeneous AR-modeled disturbances

Abstract: In this paper, we examine the target velocity estimation with distributed multi-input multi-output (MIMO) radars in non-homogeneous environments, where the disturbance signal (clutter and noise) exhibits non-homogeneity in not only power but also covariance structure from one transmit-receive antenna pair to another as well as across different test cells. Specifically, a set of distinctive auto-regressive (AR) models are used to model such non-homogeneous disturbance signals for different transmit-receive pairs. The maximum likelihood (ML) estimator for the target velocity parameter is developed. Corresponding Cramer-Rao bounds, in both the exact and asymptotic forms, respectively, are examined to shed additional light to the problem. Numerical results are presented to demonstrate of the effectiveness of the proposed method.

Detection with target-incurring orthogonal subspace interference

Abstract: In this paper, we consider the detection of a deterministic signal with an unknown scaling amplitude in the presence of a colored noise, when there is a covariance mismatch between the null and alternative hypotheses. Specifically, we consider a scenario where the target incurs an additional subspace interference that is orthogonal to the target steering vector and only present under the alternative hypothesis. To address this problem, we apply the generalized likelihood ratio test (GLRT) principle which results in a detector involving the following steps: the observation is first projected into the interference subspace. Then, the energy of the projected signal (residue) is computed. If the residual energy is small, the GLRT 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.

Moving target detection using distributed MIMO radar in non-homogeneous clutter: A subspace approach

Abstract: Motivated by the fact that the multi-static transmit-receive configuration in a distributed multiple-input multiple-output (MIMO) radar causes non-stationary clutter, we consider the problem of moving target detection (MTD) using a distributed MIMO radar in non-homogeneous clutter environments. A new non-homogeneous clutter model, where the clutter resides in a low-rank subspace with different subspace coefficients for different transmit-receive pairs, is introduced. The subspace clutter model is effective in capturing the non-homogeneity of the clutter and, in particular, the power variations across different aspect angles and resolution cells. A generalized likelihood ratio test (GLRT), which performs local matched subspace detection, noncoherent combining using local decision variables of all transmit-receive pairs and target velocity matching, is proposed. The GLRT is shown to be a constant false alarm rate (CFAR) detector. Computer simulations are provided to verify our statistical analysis of the GLRT, and a comparison with existing detectors is conducted to evaluate the impact of model mismatch on detection performance.

Parametric multichannel adaptive signal detection: Exploiting persymmetric structure

Abstract: This paper considers a parametric approach for adaptive multichannel signal detection, where the disturbance is modeled by a multichannel auto-regressive (AR) process. Motivated by the fact that a symmetric antenna geometry usually yields a persymmetric structure on the covariance matrix of disturbance, a new persymmetric AR (PAR) modeling for the disturbance is proposed and, accordingly, a persymmetric parametric adaptive matched filter (Per-PAMF) is developed. The developed Per-PAMF, while allowing a simple implementation like the traditional PAMF, extends the PAMF by developing the maximum likelihood (ML) estimation of unknown nuisance (disturbance-related) parameters under the persymmetric constraint. Numerical results show that the Per-PAMF provides significantly better detection performance than the conventional PAMF and other non-parametric detectors when the number of training signals is limited.