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Research on Parameter Estimation Algorithm for Distributed Sources

Author: ChenZuo
Tutor: WangJinKuan
School: Northeastern University
Course: Communication and Information System
Keywords: array signal processing distributed source the central DOA angular spread differential denoising estimator
CLC: TN911.23
Type: Master's thesis
Year: 2009
Downloads: 25
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Abstract


Most conventional direction-finding techniques are based on the assumption that the source energy is concentrated at discrete angles that are referred to as the source directions-of-arrival (DOAs). However, in several applications such as sonar, radar, and wireless communications, such a point source assumption can be irrelevant because signal scattering phenomena may result in angular spreading of the source energy. In such cases, a distributed sources model is more realisticthan than the point sources one. Therefore, distributed sources has been a popular topic in array signal processing.In this thesis, we analyze the cause of distributed source and its characteristic. Based on the summary of research status, pivotal problems and main algorithms, this thesis has studied several algorithms for parameter estimation. In order to improve the performance of algorithms in case of low signal-to-noise ratio (SNR), a differential denoising estimation algorithm is proposed.The noise components of the covariance matrix are removed,then the differential signal subspace is used to weight the array output vector. Therefore, all the signal components received by the array are emphasized. It has higher resolution and accuracy in case of low SNR. In addition, the integral steering vector is deduced to be Schur-Hadamard product between the steering vector of the point source and a real vector, which can avoid the integral in peak-finding searching. Simulation results show that the proposed methods are more effective than DSPE algorithm in the low SNR case.

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CLC: > Industrial Technology > Radio electronics, telecommunications technology > Communicate > Communication theory > Information Theory > Signal detection and estimation
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