Tracking maneuvering targets with multiple sensors: Does more data always mean better estimates?

When the problem of tracking maneuvering targets with multiple sensors is considered in the literature, the number and type of sensors that support a given target track is usually fixed with respect to a given target location However, in many multisensor systems, the number and type of sensors supporting a particular target track can vary with time due to the mobility, type, and resource limitations of the individual sensors. This variability in the configuration of the sensor system poses a significant problem when tracking maneuvering targets because of the uncertainty in the target motion model, A Kalman filter is often employed to filter the position measurements for estimating the position, velocity, and acceleration of a target. When designing the Kalman filter, the process noise (acceleration) variance Q(k) is selected such that the 65 to 95% probability region contains the maximum acceleration level of the target. However, when targets maneuver, the acceleration changes in a deterministic manner Thus, the white noise assumption associated with the process noise is violated and the filter develops a bias in the state estimates during maneuvers. If a larger Q is chosen, the bias in the state estimates is less during a maneuver, but then Q poorly characterizes the target motion when the target is not maneuvering and the filter performance is far from optimal. Here, the problem of tracking maneuvering targets with multiple sensors is illustrated through an example involving target motion in a single coordinate in which it is shown that with two sensors one can have (under certain conditions that include perfect alignment of the sensors) worse track performance than a single sensor, The Interacting Multiple Model (IMAI) algorithm is applied to the illustrative example to demonstrate a potential solution to this problem of track filter performance.