Wideband multiple target tracking
Aprameya Satish, R.L. Kashyap · 2002
We propose a new scheme involving autoregressive parameter estimation and pattern classification with maximum likelihood (ML) direction of arrival (DOA) estimation to track multiple targets moving in the far-field. The targets are sources of wideband signals which impinge on a uniform linear array of passive sensors. These wideband signals are modeled as vector autoregressive (AR) models so that the spectral densities of the targets are characterized by a finite number of parameters. Defining each target as a 'class', we use ML estimates of AR parameters and DOA as components to form a feature vector for a particular class. A Bayes classifier is employed to decide which DOA estimate should be associated with which target. Target tracking is achieved by the combined process of spectral density estimation, estimation of DOA and classification at regular time intervals. Experimental results illustrate performance of the proposed method.>