Target Tracking Based on Incremental Center Differential Kalman Filter With Uncompensated Biases

To mitigate the negative effects of the sensor measurement biases for the maneuvering target, a novel incremental center differential Kalman filter (ICDKF) algorithm is proposed. Based on the principle of independent incremental random process, the incremental measurement equation is modeled to preprocess the sensor measurement biases. Then, a general ICDKF algorithm is proposed by augmenting the process and measurement noises into the state vector to mitigate the negative effects of the sensor biases. For the system with additive noises, an additive ICDKF algorithm is derived by introducing the incremental measurement equation to reduce the measurement biases. Numerical simulations for four types of sensor biases are designed to demonstrate that the proposed ICDKF can effectively mitigate the measurement biases compared to the CDKF.