Tracking with multisensor out-of-sequence measurements with residual biases

In multisensor target tracking systems measurements from different sensors on the same target exhibit, typically, biases. These biases can be accounted for as fixed random variables by the Schmidt-Kalman filter. Furthermore, measurements from the same target can arrive out of sequence. This “out-of-sequence” measurement (OOSM) problem was recently solved and a procedure for updating the state with a multistep-lag measurement using the simpler “1-step-lag” algorithm was developed for the situation without measurement biases. The present work presents the solution to the combined problem of handling biases from multiple sensors when their measurements arrive out of sequence.