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.
Highlights
The 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]
2 Conclusions In this paper, we developed a new method for tracking the central DOAs assuming multiple incoherently distributed (ID) sources
This method is based on a simple covariance fitting optimization technique to estimate the central DOAs in each observed time interval
Summary
1.1 Introduction The 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]. The researchers proposed in [25,26,27] new tracking algorithms based on some modifications of the Kalman filter and the existing subspace tracking techniques All these tracking algorithms are limited to the point source model. To deal with the problem of estimating the time-varying DOAs in scattering channels, a simple DOA tracking method based on the TLS-ESPRIT [16] and subspace updating via FAPI algorithm has been recently proposed in [28] for ID sources. We derive a new method that outperforms the method derived in [28] This method is based on a simple covariance fitting optimization technique as developed in [19] to estimate the central DOAs in each observed time interval.
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