Maximum a Posteriori Algorithm for Joint Systematic Bias Estimation and Track-to-track Fusion

In practice, under communication bandwidth constraints, raw measurements are not generally sent to the fusion center. Therefore, it is a very real problem that how to generate correct decentralized estimation of target state and sensor systematic error when one use track outputs of multiple sensors without bias calibration to finish distributed fusion. Based on Bayesian filtering equations and equivalent measurements, it is presented that maximum a posteriori algorithm for joint systematic bias estimation and track-to-track fusion. It is applicable to the situation that sensors only output target tracks, quickly completed by QR orthogonal decomposition.