Abstract
This letter presents a recursive least-squares (RLS) optimization process to solve the positioning problem for wireless sensor networks, where a recursive-in-time cost function is first defined and then an iterative decentralized algorithm is derived. It is shown that the RLS scheme is equivalent to the incremental subgradient method with an appropriate variable step size for each iteration. With this, we further replace each gradient value by its "sign" to form a reduced-complexity RLS (RCRLS) scheme. Simulation results show that RCRLS has some performance degradation as compared to RLS, but both of them outperform previous related methods.
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