English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Multi-source localization based on received signal strength (RSS) has drawngreat interest in wireless sensor networks. However, the shadow fading termcaused by obstacles cannot be separated from the received signal, which leadsto severe error in location estimate. In this paper, we approximate thelog-normal sum distribution through Fenton-Wilkinson method to formulate anon-convex maximum likelihood (ML) estimator with unknown shadow fading factor.In order to overcome the difficulty in solving the non-convex problem, wepropose a novel algorithm to estimate the locations of sources. Specifically,the region is divided into $N$ grids firstly, and the multi-source localizationis converted into a sparse recovery problem so that we can obtain the sparsesolution. Then we utilize the K-means clustering method to obtain the roughlocations of the off-grid sources as the initial feasible point of the MLestimator. Finally, an iterative refinement of the estimated locations isproposed by dynamic updating of the localization dictionary. The proposedalgorithm can efficiently approach a superior local optimal solution of the MLestimator. It is shown from the simulation results that the proposed method hasa promising localization performance and improves the robustness formulti-source localization in unknown shadow fading environments. Moreover, theproposed method provides a better computational complexity from $O(K^3N^3)$ to$O(N^3)$.

RSS-based Multiple Sources Localization with Unknown Log-normal Shadow Fading