Han Wang

Joint two dimensional DOA and multipath time delay estimation in rectangular planar array

In this paper, two low complexity yet hight accuracy algorithms, modified TST-MUSIC(MTST-MUSIC) and 3D JADE- ESPRIT are presented to estimate two dimensional angles and time delays in rectangular planar array(RPA). A 3D ESPRIT-like shift-invariance technique is used to separate and estimate the 2D DOAs and time-delays in the 3D JADE-ESPRIT algorithm. The basic idea of the MTST-MUSIC method is to group and isolate the signal of each incoming ray using the space-time characteristics of the multiray wireless channel. Simulation results demonstrate that the MTST-MUSIC algorithm outperforms the 3D JADE-ESPRIT algorithm in performance.

Robust Least Squares Constant Modulus Algorithm to Signal Steering Vector Mismatches

The linearly constrained least squares constant modulus algorithm (LSCMA) may suffer significant performance degradation and lack robustness in the presence of the slight mismatches between the actual and assumed signal steering vectors, which can cause the serious problem of desired signal cancellation. To account for the mismatches, we propose a doubly constrained robust LSCMA based on explicit modeling of uncertainty in the desired signal array response and data covariance matrix, which provides robustness against pointing errors and random perturbations in detector parameters. Our algorithm optimizes the worst-case performance by minimizing the output SINR while maintaining a distortionless response for the worst-case signal steering vector. The weight vector can be optimized by the partial Taylor-series expansion and Lagrange multiplier method, and the optimal value of the Lagrange multiplier is iteratively derived based on the known level of uncertainty in the signal DOA. The proposed implementation based on iterative minimization eliminates the covariance matrix inversion estimation at a comparable cost with that of the existing LSCMA. We present a theoretical analysis of our proposed algorithm in terms of convergence, SINR performance, array beampattern gain, and complexity cost in the presence of random steering vector mismatches. In contrast to the linearly constrained LSCMA, the proposed algorithm provides excellent robustness against the signal steering vector mismatches, yields improved signal capture performance, has superior performance on SINR improvement, and enhances the array system performance under random perturbations in sensor parameters. The on-line implementation and significant SINR enhancement support the practicability of the proposed algorithm. The numerical experiments have been carried out to demonstrate the superiority of the proposed algorithm on beampattern control and output SINR enhancement compared with linearly constrained LSCMA.

Robust Constrained-LMS Algorithm

The performances of the existing adaptive array algorithms are known to degrade substantially in the presence of even slight mismatches between the actual and presumed array responses to the desired signal. Similar types of performance degradation can take place when the signal array response is known precisely but the training sample size is small. In this paper, on the basis of the constrained-LMS (CLMS) algorithm, we propose a robust constrained-LMS (RCLMS) algorithm. Our robust constrained-LMS algorithm provides excellent robustness against signal steering vector mismatches, offers fast convergence rate and makes the mean output array SINR consistently close to the optimal one. Computer simulations show better performance of our RCLMS algorithm as compared with the classical CLMS algorithm.

Robust recursive least squares adaptive beamforming algorithm

When adaptive arrays are applied to practical problems, the performance degradation of adaptive beamforming techniques may become even more pronounced than in ideal cases because some of the 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 fact, the performances of existing adaptive array algorithms are known to degrade substantially in the presence of even slight mismatches between the actual and presumed array responses to the desired signal. Similar types of degradation can take place when the signal array response is known precisely but the training sample size is small. On the basis of the recursive least squares (RLS) algorithm, we propose a novel approach to robust adaptive beamforming. Our robust RLS adaptive beamforming algorithm provides excellent robustness against signal steering vector mismatches and small training sample size, offers faster convergence rate, makes the mean output array SINR consistently close to the optimal one, and improves the unitary mismatch. Computer simulations demonstrate a visible performance gain of the proposed robust RLS algorithm.

MHFlexDT: A Multivariate Branch Fuzzy Decision Tree Data Stream Mining Strategy Based on Hybrid Partitioning Standard

Because of the inability to take a multi-pass scanning algorithm for random access to fast data streams and traditional data mining algorithms can’t sample all samples of the data stream, research of data stream mining algorithm based on fuzzy decision tree theory that fuzzy decision tree combines the understandability of decision tree and the ability of representation of fuzzy set to deal with the fuzziness and uncertainty information is very valuable to improve the accuracy of data mining. This paper presents a fuzzy decision tree data mining strategy based on hybrid partitioning standard for the problem that the method has a low accuracy when we deal with low-membership samples with missing values by dividing the samples into leaf nodes according to their membership. The multivariate branch fuzzy decision tree data stream mining strategy based on hybrid partitioning standard(MHFlexDT) is used to construct the multivariate branch fuzzy tree structure. The data fitting problem is solved by adding temporary branches to the uncertain data. At the same time, the decision tree depth is effectively limited by using the McDiarmid bound threshold. The experimental results show that MHFlexDT strategy compared with fuzzy decision tree data mining strategy is more effective in large-scale data stream mining to reduce system computation, control decision tree depth, and ensure a high accuracy when we deal with missing values, data over-fitting and noisy data problems.

Robust constrained constant modulus algorithm

In practical applications, the performance of the linearly constrained constant modulus algorithm (CMA) is known to degrade severely in the presence of even slight signal steering vector mismatches. To account for the mismatches, a novel robust CMA algorithm based on double constraints is proposed via the oblique projection of signal steering vector and the norm constraint of weight vector. To improve robustness, the weight vector is optimized to involve minimization of a constant modulus algorithm objective function by the Lagrange multipliers method, in which the parameters can be precisely derived at each iterative step. The proposed robust constrained CMA has a faster convergence rate, provides better robustness against the signal steering vector mismatches and yields improved array output performance as compared with the conventional constrained CMA. The numerical experiments have been carried out to demonstrate the superiority of the proposed algorithm on beampattern control and output SINR enhancement.

Robust blind beamforming algorithm via the oblique projection method

A novel robust least squares constant modulus algorithm (LSCMA) is proposed for blind adaptive beamforming, which is based on explicit modeling of uncertainty in the desired signal array. To improve robustness, the weight vector is optimized to involve minimization of cost function, while imposing the oblique projection constraint on the weight vector and maintaining a distortionless response for the worst-case signal steering vector. The proposed algorithm appears to be an appealing technique for blind adaptive beamforming that combines excellent robustness with low computational complexity. The numerical experiments have been carried out to demonstrate the superiority of the proposed algorithm on beampattern control and output SINR enhancement.

Robust constrained-LMS adaptive beamforming algorithm

The existing adaptive beamforming algorithms are known to degrade if some of underlying assumptions on the environment, sources, or sensor array become violated and this may cause even slight mismatches between the actual and presumed array responses to the desired signal. Similar types of degradation can take place when the signal array response is known precisely but the training sample size is small. In this paper, on the basis of constrained-LMS (CLMS) algorithm, we propose a robust constrained-LMS (RCLMS) algorithm. Our robust constrained-LMS algorithm provides excellent robustness against the desired signal mismatches, offers fast convergence rate and makes the mean output array SINR consistently close to the optimal one. Computer simulations show better performance of our RCLMS algorithm as compared with the classical CLMS algorithm.

Azimuth/elevation angle estimation via TST-ESPRIT technique

In this paper, a based on TST-ESPRIT algorithm with low complexity yet high accuracy is proposed of jointly estimate azimuth and elevation of directions of plane waves of impinging on rectangular planar array (RPA). This method provides estimates of the signal 2-D DOA based only on eigendecomposition and no searching over the parameter space is necessary. It is realized that the incoming rays are grouped, isolated and estimated by some one-dimensional (1-D) ESPRIT. In contrast to the previous methods estimating the 2-D angle of signal waves, the proposed algorithm has lower computational complexity and higher accuracy, furthermore the pairing of the azimuth and elevation is automatically determined. Finally some computer simulations confirm the validity of the proposed method.