Passive tracking algorithm of single sensor based on multi-hypothesis unscented Kalman filter

Considering that the motion targets are remarkably observable in the situation of bearing-only location with single sensor, a new method is presented in this paper, namely, passive tracking algorithm based on the multi-hypothesis unscented Kalman filter (UKF). The algorithm firstly divides the probable initial range interval of the target into subintervals, and for each subinterval UKF is used. Finally, the combined state estimate is obtained as weighted sums of the state estimate of each subinterval. Simulation proves that compared with other passive target motion analysis methods under the same circumstance, the method shows an apparently better performance, especially in target status accuracy and algorithm stability. Moreover, the method is advantaged for it doesn't call for single sensor measurement data, and it doesn't require the sensor platform to have particular maneuver.