Multisensor bias estimation using local tracks without a priori association

This paper provides a solution for sensor bias estimation based on local tracks at a single time without a priori association for a decentralized multiple sensor tracking system. Each local tracker generates its own local state estimates ignoring the bias. The fusion center then performs track-to-track fusion occasionally after estimating the sensor biases based on the common targets tracked by different sensors. The likelihood function of the bias in a multisensor-multitarget scenario is derived. Using this likelihood, it is shown that the difference of the local estimates is the sufficient statistic for estimating the biases. A least squares solution of the bias estimates and corresponding Cramer-Rao Lower Bound (CRLB) are presented assuming uncorrelatedness as well as accounting for the crosscorrelation between the local estimation errors. Two approaches to estimate the sensor biases in the absence of known track-to-track association, namely, the Maximum Likelihood estimator combined with Probabilistic Data Association (ML-PDA) and an estimator based on soft data association, are proposed. These methods are compared with the baseline solution with known (perfect) track-to-track association by Monte Carlo simulations. The experimental results indicate that the bias estimator based on the soft data association provides nearly optimal performance and has less computational load than the one using ML-PDA.