Passive sensor based dynamic object association with particle filtering

This paper develops and evaluates the threshold based algorithm proposed in [1] for dynamic data association in wireless sensor networks. The sensor node incorporates RFID reader and acoustic sensor where the signals are fused for tracking and associating multiple objects. The RFID tag is used for object identification and acoustic sensor is used for estimating object movement. For the better data association, we apply the particle littering for the prediction of an object. The algorithm with the particle filtering has an effect on increasing the association case where even objects overlap. The simulation result is compared to that using only the original algorithm. The association performance under single node coverage and multiple node coverage is evaluated as a function of sampling time.