Knowledge-Aided Parameter Estimation Based on Conjugate Gradient Algorithms

Silvio Fernando Bernardes Pinto, Rodrigo C. de Lamare

DOI: 10.14209/sbrt.2017.16

Evento: XXXV Simpósio Brasileiro de Telecomunicações e Processamento de Sinais (SBrT2017)

Keywords:

Abstract

The performance of many parameter estimation algorithms used for direction finding and localization techniques depends on the accuracy of the signal covariance matrix estimate. For a small number of sensors, the commonly used sample covariance matrix estimation procedure may only provide a poor estimate of the unknown true covariance matrix. In scenarios with low signal-to-noise ratio, stationary and non-stationary signal sources, a more accurate estimate of the signal covariance matrix can be achieved by incorporating a priori knowledge about the direction of arrival (DOA) of dominant signals. In this paper, we combine the weighted sample covariance matrix and a weighted knowledge-aided (KA) covariance matrix. We present a KA-Conjugate Gradient (KA-CG) algorithm that processes the enhanced covariance matrix estimate. Simulation results show that the proposed KA-CG algorithm substantially improves the probability of resolution of unknown close sources in the system, especially at middle low signal-to-noise ratios (SNR), requiring a reasonable number of samples for this aim.

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