Yanbo Xue

Wavelet packets-based direction-of-arrival estimation

Wavelet packets-based MUSIC (WP-MUSIC) is proposed to improve the performance of classical MUSIC in scenarios of closely spaced DOA and low signal-to-noise ratio (SNR). With WP-MUSIC the fullband signal is decomposed into several subbands by wavelet packets, and then MUSIC is applied to each subband. The computational savings of WP-MUSIC compared to MUSIC are proven. Some simulation results, proving the validity and improved performance of the proposed approach, are presented.

Non-mapping back SB-ESPRIT for coherent signals

Subband-based eigenstructure method for bearing estimation (Xue, Wang and Liu (2004), Xue and Wang (2004)) require a mapping back manipulation after the subband frequency is estimated. In this paper, we propose a non-mapping back method to estimate the fullband bearings in the subband domain. The proposed method enhances the signal energy and reduces the root mean square error (RMSE). Simulations results show the decorrelation capability of coherent sources with appropriate subband division.

Application of Wavelet Array Denoising to ESPRIT Algorithm

ESPRIT algorithm has been widely used in direction-of-arrival (DOA) estimation problem. It degrades greatly in the presence of spatially correlated noises and closely spaced DOAs, and/or at low SNR. Filters are often used to harness the performance degradation because they can recover the signal from its noisy observation. Though Wiener filter is the optimal filter, it requires the knowledge of second-order statistics of the signals and noises, which is difficult to obtain in non-wide-sense stationary signals and noises. Wavelet array denoising has been introduced to ESPRIT method in this paper to provide a possible filtering method in spatially correlated noises. The proposed approach denoises the snapshots of each sensor in parallel and applies conventional ESPRIT algorithm to the denoised data matrix. Simulation results show that by using the proposed approach, we can gain advantages of estimation root mean square error (RMSE) reduction and resolution enhancement.

Direction of arrival estimation of coherently distributed sources based on unitary ESPRIT in MIMO channel models

In MIMO channel, the presence of scatters results in angular spread. The point source model corresponding to discrete direction of arrival (DOA) becomes restrictive and leads to DOA estimation errors. To compensate for this effect, the distributed source model is proposed. In this paper, the coherently distributed source model is analysed. Based on that model, the accurate rotation matrix between the bases of the two signal subspaces is deduced. So an algorithm for the central DOA estimation of distributed sources is proposed based on unitary ESPRIT, which has an ESPRIT-like structure except for the fact that it is formulated in terms of real-valued computations throughout. The algorithm that has lower computational cost can be used for sources with different types of distribution function in MIMO channel. Simulations clearly demonstrate that the proposed method is not only effective, but also increases estimation accuracy compared with standard ESPRIT.

A neural minor component analysis algorithm for robust beamforming

A novel minor component analysis (MCA) learning rule is presented which includes a penalty term on the self-stabilizing MCA learning rule. After a presentation of convergence and steady-state analysis, it is shown how the novel MCA learning rule can be used for realizing robust constrained beamforming. Constrained beamformer power optimization principle is employed, which allows to improve the performance of the beamforming algorithm by emphasizing white noise sensitivity control and prior knowledge about the disturbances. Computer simulations show the novel MCA learning rule has strong stability, resembled convergence rates and real-time signal tracking ability, compared with the first minor component analysis (FMCA) learning rule.

Reduced wavelet packet and its application to beamspace ESPRIT algorithm in correlated signals

Subbanding method is one of the most successful applications of wavelet theory in direction-of-arrival (DOA) estimation problem. This paper illustrates the use of reduced wavelet packet filters for ESPRIT method, namely RWP-ESPRIT. Though we use the modified wavelet packet filters, the rotational invariance of the filtered data matrix holds. The proposed approach filters the signals into different subbands and then applies standard ESPRIT to the subband signals. Mapping method from the subband to the fullband is also formulated. Experimental results show that in sense of root mean square error (RMSE) reduction and output gain enhancement, RWP-ESPRIT outperforms ESPRIT in scenarios of highly correlated signals and/or low signal-to-noise ratio (SNR).

A Recursive Algorithm to Robust Adaptive Beamforming and Diagonal Loading

When adaptive arrays are applied to practical problems, the performance degradation of adaptive beamforming techniques may become even more pronounced than in the ideal case because some of underlying assumptions on the environment, sources, or sensor array can be violated and this may cause a mismatch between the presumed and actual signal steering vectors. In the practical environment, complete knowledge of signal characteristics is not available and the environment is time varying. In these cases, the recursive algorithms to robust adaptive beamforming are required. In this paper, we propose robust constrained-LMS algorithm based on constrained-LMS algorithm and explicit modeling of uncertainties in the desired signal array response, which belongs to the class of diagonal loading approaches. Our proposed robust constrained-LMS algorithm provides excellent robustness against the signal steering vector mismatches, enhances the array system performance under nonideal conditions and makes the mean output array SINR consistently close to the optimal one. Computer simulations demonstrate a visible performance gain of the proposed robust constrained-LMS algorithm

Improving the performance of cyclic ESPRIT via real-valued decomposition

By exploiting the real-valued eigendecomposition (EVD) of the centro-Hermitian matrix, a novel cyclic ESPRIT algorithm with signal selective property is presented by introducing a new forward backward smoothed covariance matrix. Compared with Cyclic ESPRIT algorithm, the proposed approach has a better performance in the presence of multipath propagation. In addition, this approach not only reduces the computational complexity, but also allows to select desired signals and to ignore interferences by exploiting the cyclostationarity property of signals of interest (SOIs). Simulation results that illustrate the performance of this approach in conjunction with cyclic ESPRIT algorithm are described.

Direction of Arrival Estimation Based on Distributed Sources Model in MIMO Channel Environment

In MIMO channel, the presence of scatters results in angular spread. The point source model corresponding to discrete direction of arrival (DOA) becomes restrictive and leads to DOA estimation errors. To compensate for this effect, the distributed source model is proposed. A first order Taylor series expansion of the steering vector is derived, which is similar to the point source model. The proposed method is a possible alternative to MUSIC for the central DOA estimation. The algorithm that has lower computational cost can be used for sources with different distribution functions in MIMO channel. Simulations clearly demonstrate that the proposed method is not only effective, but also enjoys better SNR performance compared with MUSIC

On A Real-valued EVD Approach to Cyclic DOA Estimation

By exploiting the real-valued eigen decomposition (EVD) of the centro-Hermitian matrix, a novel cyclic MUSIC algorithm with signal selective property is presented by introducing a new forward backward smoothed covariance matrix. Compared with cyclic MUSIC algorithm, the proposed approach has a better performance in the presence of multipath propagation. In addition, this approach not only reduces the computational complexity, but also allows to select desired signals and to ignore interferences by exploiting the cyclostationarity property of signals of interest (SOIs). Simulation results that illustrate the performance of this approach in conjunction with cyclic MUSIC algorithm are described