On the ordering of the sensors in the iterated-corrector probability hypothesis density (PHD) filter

This paper considers the effect of sensor ordering on the iterated-corrector PHD update. It is known that changing the order of the updates results in different PHDs, however, these are usually not significantly different. This paper considers a multisensor scenario using a single poor quality sensor in combination with good sensors, where the bad sensor is modelled using a low probability of detection. It is shown that the quality of the updated PHD varies significantly depending on whether the sensor is used first or last in the iterated-corrector update. The degradation in performance of the iterated PHD filter is illustrated using a comparison of different multisensor configurations. The OSPA error is shown to be greatest when a sensor with low probability of detection is used in the final update of the iterated form of the PHD filter. The performance of the productmultisensor PHD filter is also considered. The product multisensor filter is shown to perform significantly better due to invariance to sensor ordering.