No abstract

Energy consumption and tracking accuracy are two significant issues for collaborative tracking in distributed wireless sensor networks (DWSNs). To obtain a benefit from those issues, most of the recent work tends to reduce the spatial redundancy, while ignoring utilizing the attribute of time redundancy. In this paper, a novel energy-efficient framework of collaborative signal and information fusion is proposed for acoustic target tracking. The proposed fusion algorithm is based on neural network aggregation model and Gaussian particle filtering (GPF) estimation. And the neural network based aggregation (NNBA) can reduce spatial and time redundancy. Furthermore, a fresh cluster head (CH) selection method demanding less task handover is also presented to decrease energy consumption. The analyzed framework coupled with simulations demonstrates its excellent performance in tracking accuracy and energy consumption.

Mobile localization is a significant issue for wireless sensor networks (WSNs). However, it is a problem for the indoor localization using received signal strength (RSS) measurements that the signal is contaminated by the anisotropy fading and interference due to walls and furniture. Standard schemes such as Kalman filter are inadequate as the random transition of line-of-sight (LOS)/non-line-of-sight (NLOS) conditions occurs frequently. This paper proposes an indoor mobile localization scheme with RSS measurements in a mixed LOS and NLOS environment. First, a new efficient composite measurement model is induced and validated, which approximates the complex effects of LOS and NLOS channels. Second, a greedy anchor sensor selection strategy is adopted to break through the constraints of hardware consistency and the multipath interference. Third, for the Markov transition between LOS and NLOS conditions, an effective unscented Kalman filter (UKF) based interactive multiple model (IMM) is proposed to estimate not only the posterior model probabilities but also a weighted-sum position estimation with the aid of likelihood function. To evaluate the proposed algorithm, a complete hardware and software platform for mobile localization has been constructed. The numerical study, relying on the actual experiments, illustrates that the proposed UKF based IMM achieves a substantial gain in precision and robustness, compared with other works.

No abstract