Showing papers by "Pu Wang published in 2010"

Multiantenna-Assisted Spectrum Sensing for Cognitive Radio

Abstract: In this paper, we consider the problem of detecting a primary user in a cognitive radio network by employing multiple antennas at the cognitive receiver. In vehicular applications, cognitive radios typically transit regions with differing densities of primary users. Therefore, speed of detection is key, and so, detection based on a small number of samples is particularly advantageous for vehicular applications. Assuming no prior knowledge of the primary user's signaling scheme, the channels between the primary user and the cognitive user, and the variance of the noise seen at the cognitive user, a generalized likelihood ratio test (GLRT) is developed to detect the presence/absence of the primary user. Asymptotic performance analysis for the proposed GLRT is also presented. A performance comparison between the proposed GLRT and other existing methods, such as the energy detector (ED) and several eigenvalue-based methods under the condition of unknown or inaccurately known noise variance, is provided. Our results show that the proposed GLRT exhibits better performance than other existing techniques, particularly when the number of samples is small, which is particularly critical in vehicular applications.

Integrated Cubic Phase Function for Linear FM Signal Analysis

Abstract: 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.

A New Parametric GLRT for Multichannel Adaptive Signal Detection

Abstract: A parametric generalized likelihood ratio test (GLRT) for multichannel signal detection in spatially and temporally colored disturbance was recently introduced by modeling the disturbance as a multichannel autoregressive (AR) process The detector, however, involves a highly nonlinear maximum likelihood estimation procedure, which was solved via a two-dimensional iterative search method initialized by a suboptimal estimator In this paper, we present a simplified GLRT along with a new estimator for the problem Both the estimator and the GLRT are derived in closed form at considerably lower complexity With adequate training data, the new GLRT achieves a similar detection performance as the original one However, for the more interesting case of limited training, the original GLRT may become inferior due to poor initialization Because of its simpler form, the new GLRT also offers additional insight into the parametric multichannel signal detection problem The performance of the proposed detector is assessed using both a simulated dataset, which was generated using multichannel AR models, and the KASSPER dataset, a widely used dataset with challenging heterogeneous effects found in real-world environments

A Bayesian Parametric Test for Multichannel Adaptive Signal Detection in Nonhomogeneous Environments

Abstract: This paper considers the problem of knowledge-aided space-time adaptive processing (STAP) in nonhomogeneous environments, where the covariance matrices of the training and test signals are assumed random and different from each other. A Bayesian detector is proposed by incorporating some a priori knowledge of the disturbance covariance matrices, and exploring their inherent block-Toeplitz structure. Specifically, the block-Toeplitz structure of the covariance matrix allows us to model the training signals as a multichannel auto-regressive (AR) process. The resulting detector is referred to as the Bayesian parametric adaptive matched filter (B-PAMF) which, compared with nonparametric Bayesian detectors, entails a lower training requirement and alleviates the computational complexity. Numerical results show that the proposed B-PAMF detector outperforms the standard PAMF test in nonhomogeneous environments.

Performance of Instantaneous Frequency Rate Estimation Using High-Order Phase Function

Abstract: The high-order phase function (HPF) is a useful tool to estimate the instantaneous frequency rate (IFR) of a signal with a polynomial phase. In this paper, the asymptotic bias and variance of the IFR estimate using the HPF are derived in closed-forms for the polynomial phase signal with an arbitrary order. The Cramer-Rao bounds (CRBs) for IFR estimation, in both exact and asymptotic forms, are obtained and compared with the asymptotic mean-square error (MSE) of the HPF-based IFR estimator. Simulations are provided to verify our theoretical results.

Cubic-phase function evaluation for multicomponent signals with application to SAR imaging

Abstract: A cubic-phase function evaluation technique for multicomponent frequency-modulated signals with non-overlapped components in the time-frequency (TF) plane is proposed. The proposed technique is based on the short-time Fourier transform. Cross-terms are removed or reduced in the same manner as in the case of the TF representation called the S-method. The proposed technique is applied for visualisation of signals in time-chirp-rate plane and parameter estimation of analytical and radar signals. In addition, a procedure for focusing SAR images by using estimated parameters is proposed in order to verify obtained results.

Persymmetric Parametric Adaptive Matched Filters for Detecting Targets Using Space-Time Adaptive Processing of Radar Signals

Abstract: A method provides space-time adaptive processing (STAP) for target detection using adaptive matched filters (AMF). A generalized likelihood ratio test (GLRT) is determined where spatial and temporal correlation matrices Q and A are assumed. Then, the correlation matrices A and Q are replaced with maximum likelihood (ML) estimates obtained only from training signals subject to a persymmetric constraint.

Bayesian parametric approach for multichannel adaptive signal detection

Abstract: This paper considers the problem of space-time adaptive processing (STAP) in non-homogeneous environments, where the disturbance covariance matrices of the training and test signals are assumed random and different with each other. A Bayesian detection statistic is proposed by incorporating the randomness of the disturbance covariance matrices, utilizing a priori knowledge, and exploring the inherent Block-Toeplitz structure of the spatial-temporal covariance matrix. Specifically, the Block-Toeplitz structure of the covariance matrix allows us to model the training signals as a multichannel auto-regressive (AR) process and hence, develop the Bayesian parametric adaptive matched filter (B-PAMF) to mitigate the training requirement and alleviate the computational complexity. Simulation using both simulated multichannel AR data and the challenging KASSPER data validates the effectiveness of the B-PAMF in non-homogeneous environments.

Instantaneous frequency estimation of polynomial phase signals using local polynomial Wigner-Ville distribution

Abstract: This paper makes use of local polynomial Wigner-Ville distribution (LPWVD), originally designed for nonparametric instantaneous frequency (IF) estimation of transient signals, to propose a parametric IF estimation for polynomial phase signals (PPSs). Statistical performance such as asymptotic bias and variance of the LPWVD-based parametric IF estimator is derived in closed-form. Based on the analytical results, we extend the statistical efficiency of the Wigner-Ville distribution (WVD) for a second-order PPS only to that of the LP-WVD for an arbitrary order, when the IF is estimated at the middle of sample observations. Simulation results verify the analytical performance and comparisons with the polynomial Wigner-Ville distribution (PWVD) show that the LPWVD-based parametric IF estimator can provide better performance.

Moving target estimation using distributed MIMO radar in non-homogeneous clutter

Abstract: In this paper, we consider parameter estimation for moving target detection using a distributed MIMO radar, where the multi-static transmit-receive configuration causes non-homogeneous clutter. Specifically, the clutter power for the same resolution cell may vary significantly from one transmit-receive pair to another, due to azimuth-selective backscattering. Moreover, the clutter power may also vary across resolution cells in the neighborhood of the test cell. In this work, the non-homogeneous clutter is modeled using a subspace approach, whereby the subspace is spanned by a few Fourier bases and the non-homogeneity of the clutter is captured by coefficients that vary with different transmit-receive pairs and/or different resolution cells. The choice of the Fourier bases is based the fact that the clutter Doppler spectrum is bandlimited. We develop a maximum likelihood (ML) estimator for the parameters associated with the target and clutter. The Crame´r-Rao bound (CRB) for velocity estimation is also derived. Numerical results show that the proposed estimator outperforms an alternative solution that ignores the non-homogeneous nature of the clutter.

Cubic phase function for two-dimensional polynomial-phase signals

Abstract: A cubic phase function for two-dimensional polynomial-phase signals of the third order (CPF 2-D) is proposed. The CPF 2-D based estimator is able to obtain all unknown parameters by using reduced number of phase differences, compared to the classical Francos-Friedlander (FF) approach. Statistical analysis shows that the proposed CPF 2-D based estimator is asymptotically unbiased and gives low mean squared error (MSE). Simulation results demonstrate that the proposed approach outperforms the FF approach.

Knowledge-aided parametric GLRT for space-time adaptive processing

Abstract: In this paper, we consider knowledge-aided space-time adaptive processing (KA-STAP) with a parametric approach, where disturbances in both test and training signals are modeled as a multichannel auto-regressive (AR) model. The a priori knowledge is incorporated into the detection problem through a stochastic signal model, where the spatial covariance matrix of the disturbance is assumed random. According to this model, a Bayesian version of the parametric generalized likelihood ratio test (PGLRT) is developed in a two-step approach, which is referred to as the KA-PGLRT. Interestingly, the KA-PGLRT employs a colored loading approach for estimation of the spatial covariance matrix of the test signal. Simulation results show that the KA-PGLRT can obtain better detection performance over other parametric detectors.

Two-Step Low-Complexity Space-Time Adaptive Processing (STAP)

Abstract: This work proposes a low-complexity space-time adaptive processing (STAP) algorithm for sensing applications built on a moving platform in the presence of strong clutters. The proposed algorithm achieves low-complexity computation via two steps. First, it utilizes improved fast approximated power iteration methods to compress the data into a much smaller subspace. To further reduce the computational complexity, a progressive singular value decomposition (SVD) approach is employed to update the inverse of the covariance matrix of the compressed data. As a result, the proposed low-complexity STAP algorithm can achieve order-of-magnitude computational complexity reduction as compared to conventional STAP algorithms. Simulation results are shown to confirm the validity of the proposed algorithm.