Tracking maneuvering targets with multiple biased sensors

When tracking maneuvering targets with multiple sensors, the communication of measurements between the sensors or central-level tracker provides the best responsiveness to target maneuvers. However, when sensors are biased and the biases are not statistically insignificant, the maneuver detection and response in the central-level tracker is degraded. Due to the Markov modeling of the maneuver process in interacting multiple model (IMM) estimator, this degradation of the central-level tracking is acute. In this paper, the challenge of using an IMM estimator for central-level tracking of maneuvering targets with biased sensors is illustrated. In order to reduce the effects of sensor biases on the central-level tracks, the mode likelihoods are evaluated at the sensor and communicated to the central-level tracker along with the measurements. The benefits of using sensor-level mode likelihood in the central-level tracker is demonstrated via Monte Carlo simulations.