Sparse Multi-target Localization and Tracking in Wireless Sensor Network

In our paper, novel methods are addressed for multi-target localization and tracking in wireless sensor network(WSN). Adaptive iteration bayesian compressive sensing localization (AIBCSL) is applied for localization, which based on the assumption that noise variance is stationary. Compared wtih traditional compressive sensing, there´s no need of knowing target numbers as prior in our algorithm. Take that the targets are sparse distributed into account, target tracking is formulated to be an optimization process and can be solved by dynamic programming. Target´s sparsity and continuous moving are basic assumptions in tracing problem.