Shunjun Wu

Computationally efficient direction-of-arrival estimation based on partial a priori knowledge of signal sources

A computationally efficient method is proposed for estimating the directions-of-arrival (DOAs) of signals impinging on a uniform linear array (ULA), based on partial a priori knowledge of signal sources. Unlike the classical MUSIC algorithm, the proposed method merely needs the forward recursion of the multistage Wiener filter (MSWF) to find the noise subspace and does not involve an estimate of the array covariance matrix as well as its eigendecomposition. Thereby, the proposed method is computationally efficient. Numerical results are given to illustrate the performance of the proposed method.

Optimal mismatched filter bank design for MIMO radar via convex optimization

In this paper, a mismatched filter bank is designed for suppressing the autocorrelation peak sidelobe level (PSL) and the peak cross-correlation level (PCCL) of an orthogonal polyphase sequence set applied in a multiple-input multiple-output (MIMO) radar system. The mismatched filter bank is obtained by minimizing a weighted maximum of the PSL and PCCL on the basis of the convex optimization. Compared with the iteratively reweighted least squares (IRLS) method, the proposed convex method can get the optimal mismatched filter bank with the minimum PSL and PCCL, and can also control the system signal-to-noise ratio loss (SNRL). Numerical examples show that the optimal mismatched filter bank at the cost of a slight SNRL can achieve a good improvement of the PSL and a moderate improvement of the PCCL, if the filter length P and the weighting factor w between the PSL and PCCL are appropriately chosen.

Super-resolution ISAR imaging via statistical compressive sensing

Developing compressed sensing (CS) theory has been applied in radar imaging by exploiting the inherent sparsity of radar signal. In this paper, we develop a super resolution (SR) algorithm for formatting inverse synthetic aperture radar (ISAR) image with limited pulses. Assuming that the target scattering field follows an identical Laplace probability distribution, the approach converts the SR imaging into a sparsity-driven optimization in Bayesian statistics sense. We also show that improved performance is achieved by taking advantage of the meaningful spatial structure of the scattering field. To well discriminate scattering centers from noise, we use the non-identical Laplace distribution with small scale on signal components and large on noise. A local maximum likelihood estimator combining with bandwidth extrapolation technique is developed to estimate the statistical parameters. Experimental results present advantages of the proposal over conventional imaging methods.

Low-complexity ESPRIT method for direction finding

A low-complexity ESPRIT method for direction-of-arrival (DOA) estimation is proposed in this paper. Unlike the conventional subspace based methods for DOA estimation, the proposed method only needs the training data of one signal to perform the forward recursions of the multi-stage Wiener filter (MSWF), does not involve the estimate of the covariance matrix or its eigendecomposition. Thus, the proposed method is computationally advantageous. Numerical results are given to illustrate the performance of the proposed method.

A novel MUSIC algorithm for direction-of-arrival estimation without the estimate of covariance matrix and its eigendecomposition

A new MUSIC algorithm for direction-of-arrival (DOA) estimation is developed, based on the multi-stage Wiener filter (MSWF). Unlike the classical MUSIC algorithm, the proposed method only involves the forward recursions of the MSWF to find the noise subspace, even in the case of coherent signals, and does not require the estimate of an array covariance matrix or its eigendecomposition. Therefore, the proposed method is computationally advantageous over the classical MUSIC algorithm that resorts to computing the sample covariance matrix and its eigenvectors. The performance of the proposed method is demonstrated by numerical results.

Low-complexity method of weighted subspace fitting for direction estimation

In this paper, we consider a low-complexity method of weighted subspace fitting (WSF) for direction-of-arrival (DOA) estimation. With the properties of the multi-stage Wiener filter (MSWF), we derive a novel criterion function for the WSF method without the estimate of an array covariance matrix and its eigendecomposition. A new approach for noise variance estimation is also proposed. Numerical results indicate that by selecting a specific weighting matrix, the low-complexity WSF estimator can provide the comparable estimation performance with the conventional WSF method.

The Principle and Performance Analysis of Profile Clutter map

The paper discusses the principle of radar profile clutter map and its corresponding algorithm. The performances of false alarm control and the detection performance of clutter are analyzed under the condition of Rayleigh distribution. When the system parameters are determined, the paper analyzes the affection to the detection probability corresponding to the number of repeat impulses, iterative coefficient and the average clutter-to-noise ratio in different false alarm probability, and so, the realization of building profile clutter map in low clutter-to-noise ratio is available. The simulation examples can demonstrate the validity of algorithm

Fast and Robust GSC Beamformer based on Variable Diagonal Loading

In this paper, a fast and robust GSC beamforming algorithm based on variable diagonal loading is proposed using multistage Wiener filter techniques. Firstly, the loading level can be accurately determined through MSWF decomposition. In conjunction with match filter loading method, the efficient technique achieves the advantages of robust capabilities. The whole algorithm does not requiring computing the covariance matrix and its eigen-decomposition, so the computation load is greatly saved. Simulations demonstrate its effectiveness and robustness

Angle estimation via a computationally efficient SSF method

A computationally efficient method of signal subspace fitting (SSF) for angle estimation is proposed in this paper. Given the training data of one desired signal, the proposed method finds direction-of-arrival (DOA) parameters of all signals with much lower computational complexity than the classical weighted subspace fitting (WSF) method. Simulations are given to show that the proposed method provides the comparable estimation accuracy with the weighted subspace fitting estimator for uncorrelated and coherent signals.

A new method for noise subspace estimation based on the spatial smoothing Lanczos algorithm

A new method is proposed to estimate a noise subspace. It is shown that the redundant prefilters of the multistage Wiener filter (MSWF) are capable of creating an orthogonal basis for the noise subspace. Based on the classical spatial smoothing technique and the Lanczos algorithm, a novel technique is presented to obtained the noise subspace in the case of coherent signals. The new estimator outperforms its counterparts in terms of computational complexity. Finally, the theoretical observations are illustrated by numerical results.