The authors focus on the problem of tracking the direction-of-arrival (DOA) of multiple moving targets. The targets are assumed to be moving with constant accelerations subject to minor random perturbations and emitting narrow band signals that impinge on an array of passive sensors. Estimates of the DOA vector of the targets are obtained from the sensor data based on the maximum likelihood (ML) principle in such a way that the association between the estimates made at different time points is maintained. At each stage, the current ML estimates of DOA are treated as measurements and refined via a Kalman filter; tracking is accomplished without the need to perform unduly heavy computations. An efficient strategy for dealing with closely spaced targets is also presented. Finally, the performance of the tracking algorithm is illustrated via computer simulations.
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