Joint Estimation Algorithm of Target State and Measurement Bias for Doppler Sensor

In recent years, Doppler-only sensors have demonstrated an excellent estimation performance for target tracking. Most of the existing tracking algorithms that utilize Doppler sensors rely on the assumption that sensors capture only measurement errors, disregarding measurement biases. However, in many practical situations, there can be significant measurement biases, which can severely compromise the performance of target state estimation. Recognizing this issue, the study proposes a new joint estimation algorithm that is exclusively reliant on Doppler-only sensors. The proposed algorithm not only provides the estimated results of the target state, but also the measurement bias of the Doppler sensor in use. The approach unfolds in two stages: the first stage is to estimate the target state without considering the measurement bias; the second is to perform bias compensation using the least squares method, and the target state and measurement bias are jointly estimated by linearization of the measurement equation. To validate the efficacy of this method, we analyzed the Cramer-Rao lower bound (CRLB) for measurement bias estimation and designed simulations under both static and moving sensor scenarios to assess its performance. The results indicate that the proposed algorithm can effectively estimate the target state, outperforming Kalman filter (KF) in both the moving and static sensor scenario. The root mean square error (RMSE) of the bias estimation can approach the CRLB.