An efficient central DOA tracking algorithm for multiple incoherently distributed sources

Abstract

In this paper, we develop a new tracking method for the direction of arrival (DOA) parameters assuming multiple incoherently distributed (ID) sources. The new approach is based on a simple covariance fitting optimization technique exploiting the central and noncentral moments of the source angular power densities to estimate the central DOAs. The current estimates are treated as measurements provided to the Kalman filter that model the dynamic property of directional changes for the moving sources. Then, the covariance-fitting-based algorithm and the Kalman filtering theory are combined to formulate an adaptive tracking algorithm. Our algorithm is compared to the fast approximated power iteration-total least square-estimation of signal parameters via rotational invariance technique (FAPI-TLS-ESPRIT) algorithm using the TLS-ESPRIT method and the subspace updating via FAPI-algorithm. It will be shown that the proposed algorithm offers an excellent DOA tracking performance and outperforms the FAPI-TLS-ESPRIT method especially at low signal-to-noise ratio (SNR) values. Moreover, the performances of the two methods increase as the SNR values increase. This increase is more prominent with the FAPI-TLS-ESPRIT method. However, their performances degrade when the number of sources increases. It will be also proved that our method depends on the form of the angular distribution function when tracking the central DOAs. Finally, it will be shown that the more the sources are spaced, the more the proposed method can exactly track the DOAs.Keywords: Tracking DOAs, Incoherently distributed sources, Covariance fitting technique, Kalman filter Review IntroductionThe most commonly considered system model in the direction of arrival (DOA)-finding techniques is the point source model where the signals are assumed to be generated from far-field point sources [1][2][3][4]. However, in real surroundings, especially in modern wireless communication systems, local scattering in the source vicinity causes angular spreading. Therefore, the researchers considered a more realistic signal model called spatially distributed source model. Depending on the nature of scattering, distributed sources have been classified into two types: coherently and incoherently distributed (CD and ID) sources [5,6]. For ID source model contrary to the CD case, the rank of the noise-free covariance matrix is different to the number of sources. Moreover, it increases with the angular spread. Therefore, traditional subspace-based methods become not applicable in this case. To deal with *Correspondence: sonia.benhassen@yahoo.fr 1 Tunisia Polytechnic School, B.P. 743-2078, La Marsa, Tunisia Full list of author information is available at the end of the article this problem, efforts have been directed to specifically design new techniques to estimate the angular parameters for ID sources. Some of them that are able to handle a single ID source were developed in [7][8][9][10][11][12][13]. Many other estimators were also developed to estimate the angular parame...

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