Object Tracking Over Distributed WSNs With Consensus on Estimates and Missing Data

Abstract

Wireless sensor network (WSN) technologies are used to provide mobile object tracking due to advantages such as mobility, scalability, and flexibility. However, wireless interaction between the network nodes is often accompanied by missing data, which requires robustness from the estimator. This paper develops an iterative distributed unbiased finite impulse response (dUFIR) filtering algorithm for object tracking via WSNs with consensus on estimates and shows that it has higher robustness than the distributed Kalman filter (dKF). The tracking problem is viewed as a real-time position estimation of an unmanned ground vehicle (UGV). The extensive simulations are provided using real sensor parameters and measurements of the UGV position with missing data. Two different scenarios are considered when: 1) each sensor is capable of measuring the UGV position and 2) sensors have different time-varying noise variances, as in practical WSNs. The higher robustness of the dUFIR against the dKF is demonstrated under diverse operation conditions.

Keywords

• Distributed wireless sensor network
• object tracking
• unbiased FIR filter
• Kalman filter
• robustness
• consensus on estimates