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 Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

In this letter, we consider the detection of sparse stochastic signals withsensor networks (SNs), where the fusion center (FC) collects 1-bit data fromthe local sensors and then performs global detection. For this problem, a newlydeveloped 1-bit locally most powerful test (LMPT) detector requires 3.3Qsensors to asymptotically achieve the same detection performance as thecentralized LMPT (cLMPT) detector with Q sensors. This 1-bit LMPT detector isbased on 1-bit quantized observations without any additional processing at thelocal sensors. However, direct quantization of observations is not the mostefficient processing strategy at the sensors since it incurs unnecessaryinformation loss. In this letter, we propose an improved-1-bit LMPT (Im-1-bitLMPT) detector that fuses local 1-bit quantized likelihood ratios (LRs) insteadof directly quantized local observations. In addition, we design thequantization thresholds at the local sensors to ensure asymptotically optimaldetection performance of the proposed detector. It is shown theoretically andnumerically that, with the designed quantization thresholds, the proposedIm-1-bit LMPT detector for the detection of sparse signals requires less numberof sensor nodes to compensate for the performance loss caused by 1-bitquantization.

Distributed Detection of Sparse Stochastic Signals via Fusion of 1-bit Local Likelihood Ratios