A decorrelation algorithm based on virtual array extension

Duan, Hongliang; Huang, Dengshan; Zhou, Liyang; Chen, Haihua; Shi, Jinglin

doi:10.1109/chinacom.2012.6417505

Cited by 4 publications

(1 citation statement)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this case, preprocessing techniques such as spatial smoothing [12] and matrix reconstruction [13] methods are needed. These techniques have to reduce the rank of data covariance matrix as a precondition which means that the accuracy or the array aperture would be lost [14]. Furthermore, the resolution of MUSIC and ESPRIT decreases greatly when Signal-to-Noise Ratio (SNR) gets lower for both coherent and noncoherent signals cases.…”

Section: Introductionmentioning

confidence: 99%

A Low Computational Complexity SML Estimation Algorithm of DOA for Wireless Sensor Networks

Gong

Chen

et al. 2015

International Journal of Distributed Sensor Networks

View full text Add to dashboard Cite

We address the problem of DOA estimation in positioning of nodes in wireless sensor networks. The Stochastic Maximum Likelihood (SML) algorithm is adopted in this paper. The SML algorithm is well-known for its high resolution of DOA estimation. However, its computational complexity is very high because multidimensional nonlinear optimization problem is usually involved. To reduce the computational complexity of SML estimation, we do the following work. (1) We point out the problems of conventional SML criterion and explain why and how these problems happen. (2) A local AM search method is proposed which could be used to find the local solution near/around the initial value. (3) We propose an algorithm which uses the local AM search method together with the estimation of DML or MUSIC as initial value to find the solution of SML. Simulation results are shown to demonstrate the effectiveness and efficiency of the proposed algorithms. In particular, the algorithm which uses the local AM method and estimation of MUSIC as initial value has much higher resolution and comparable computational complexity to MUSIC.

show abstract

Section: Introductionmentioning

confidence: 99%