Sequential pre-filter assisted cooperative scheme for localization in wireless sensor networks

In the applications of wireless sensor networks (WSNs), it is important to locate an object of interest. However, the intensive measurement noise that contaminates the observations from each sensor node, may impair the localization performance. The widely studied adaptive and cooperative schemes combat the noise via reliable cooperation and adaption strategies with the neighborhoods. However, they underestimate the smooth correlations of the object’s movements, thereby remaining space for improvement. In this paper, we focus on improving the existing cooperative schemes by prefiltering its contaminated observations on each node. By exploiting the smooth correlations of the object’s mobility, we design a sequential pre-filter, which is capable of using the previously estimated information as a priori to overcome the intensive noise. As such, it helps to derive a less-noisy observation on each node, and therefore improves the localization accuracy of the cooperative schemes. Numerical simulations demonstrate the effect of the proposed sequential pre-filter, which can indeed better the cooperative schemes and gain a more promising localization performance.