Predictive Angle Tracking Algorithm Based on Extended Kalman Filter

Abstract

In the case of moving sources, various target angle tracking algorithms have been proposed and reported in the literature for multiple narrow-band targets. Yang and Kaveh proposed an iterative adaptive eigen-subspace method in conjunction with the multiple signal classification (MUSIC) algorithm to track the DOA angles of multiple targets (Yang & Kaveh, 1988). Due to the data association problem caused by multi-target tracking, the adaptive MUSIC method fails to track targets when they are moving close to each other. Although the method proposed by Sword, et al. (1990) can avoid the data association problem, errors are accumulated in each iteration, making it unable to track targets that are mutually close. Due to the nature of prediction-correction filtering process, Kalman filter (KF) can reduce estimation errors and avoid the data association problem when applied to angle tracking, as stated in several references (Javier & Sylvie, 1999; Yang, 1995; Park, et al. 1994). Rao, et al. (1994) proposed to estimate DOA angles of targets using the maximum likelihood method and feeding the results to a KF. However, it is assumed that the signal powers of the targets are all different, making the algorithm impractical. Javier and Sylvie (1999) suggested to estimate target angles using the projection approximation subspace tracking algorithm with deflation (PASTd) (Yang, 1995) and a Newton-type method (for MUSIC spectrum) for the use in the KF. It has lower computational load and better tracking performance than Rao’s algorithm, but still exhibits poor tracking success rate at low signalto-noise ratios (SNRs). Park, et al. (1994) proposed an approach, which utilizes predicted angles obtained from Sword’s method. The approach also uses the constrained least-squares criterion to confine the dynamic range of angles. The choice of relevant parameters is empirical and is not suitable for various scenarios of different moving speeds and SNRs. Besides, the tracking performance degrades seriously with an increasing number of crossing targets. Later on, to improve Park’s method, Ryu, et al. (1999, 2002) suggested to obtain the angle innovations of the targets from a signal subspace, instead of the sensor output covariance matrix, via projection approximation subspace tracking (PAST) algorithm (Yang, 1995). Chang, et al. (2005) modified Park’s algorithm by incorporating a spatial smoothing (Shan et al., 1985) technique to overcome multipath interference, and also coherent signalsubspace (CSS) (Wang & Kaveh, 1985) processing for tracking wideband targets. All of the above algorithms are based on the sample covariance matrix or signal subspace made with