Distributed Direct Localization Suitable For Dense Networks
IEEE Transactions on Aerospace and Electronic Systems(2020)
摘要
Traditional network localization algorithms contain ranging and localization steps, which have systematic disadvantages. We propose an algorithm dubbed direct particle filter based distributed network localization (DiPNet). A node's location is directly estimated from the received signals, incorporating location uncertainty of neighboring nodes. The propagation effects on DiPNet become insignificant for dense networks, due to the massive-link collective physical layer processing. DiPNet achieves a near-optimal performance with low complexity, which is particularly attractive for realtime dense-network localization.
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关键词
Distance measurement,Gold,Complexity theory,Uncertainty,Estimation,Data mining,Signal to noise ratio,Direct position estimation (DPE),distributed particle filter,Fisher information,network localization
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