Cooperative localization and tracking in wireless sensor networks

Summary Cooperative localization has attracted great attention in recent years. However, in some scenarios, localization precision is challenging and does not meet the application requirements. In this paper, Kalman and Particle filters (KF and PF) are considered for cooperative localization scenarios purpose. We propose to apply these techniques to cooperative localization approaches that we investigated in previous papers: Evolved Variational Message Passing algorithm (E‐VMP) and Cooperative Robust Geometric Positioning Algorithm (C‐RGPA). The main added value of distributed tracking filters is to guarantee dynamic versions of these two algorithms. The proposed techniques are evaluated and compared by means of real heterogeneous measurements carried out using ZigBee and OFDM devices and where location‐dependent parameters such as RSSI and RTD are exploited. Experiments and realistic simulations reveal that the proposed techniques exhibit better localization accuracy for very low complexity and cost. Moreover, the comparative study shows that distributed particle filter (DPF) provides better performance than KF in terms of positioning accuracy and root‐mean square error. In this paper, Kalman and Particle filters are applied on cooperative localization approaches based on Evolved Variational Message Passing algorithm and Cooperative Robust Geometric Positioning Algorithm. The proposed techniques are evaluated and compared using real heterogeneous measurements carried out using ZigBee and OFDM devices. Experiments and realistic simulations reveal that the proposed techniques exhibit better localization accuracy for very lower complexity and cost.