TY - JOUR
T1 - Distributed Detection of Sparse Stochastic Signals via Fusion of 1-bit Local Likelihood Ratios
AU - Li, Chengxi
AU - He, You
AU - Wang, Xueqian
AU - Li, Gang
AU - Varshney, Pramod K.
N1 - Funding Information:
Manuscript received August 25, 2019; revised September 28, 2019; accepted September 28, 2019. Date of publication October 2, 2019; date of current version October 31, 2019. This work was supported in part by the National Natural Science Foundation of China under Grants 61790551 and 61790554 and in part by the National Science Foundation of USA under Grant ENG 60064237. The associate editor coordinating the review of this manuscript and approving it for publication was Prof. Marco F. Duarte. (Corresponding author: You He.) C. Li, X. Wang, and G. Li are with the Department of Electronic Engineering, Tsinghua University, Beijing 100084, China.
Funding Information:
This work was supported in part by the National Natural Science Foundation of China under Grants 61790551 and 61790554 and in part by the National Science Foundation of USA under Grant ENG 60064237.
Publisher Copyright:
© 1994-2012 IEEE.
PY - 2019/12
Y1 - 2019/12
N2 - In this letter, we consider the detection of sparse stochastic signals with sensor networks (SNs), where the fusion center (FC) collects 1-bit data from the local sensors and then performs global detection. For this problem, a newly developed 1-bit locally most powerful test (LMPT) detector requires 3.3Q sensors to asymptotically achieve the same detection performance as the centralized LMPT (cLMPT) detector with Q sensors. This 1-bit LMPT detector is based on 1-bit quantized observations without any additional processing at the local sensors. However, direct quantization of observations is not the most efficient processing strategy at the sensors since it incurs unnecessary information loss. In this letter, we propose an improved-1-bit LMPT (Im-1-bit LMPT) detector that fuses local 1-bit quantized likelihood ratios (LRs) instead of directly quantized local observations. In addition, we design the quantization thresholds at the local sensors to ensure asymptotically optimal detection performance of the proposed detector. It is shown theoretically and numerically that, with the designed quantization thresholds, the proposed Im-1-bit LMPT detector for the detection of sparse signals requires less number of sensor nodes to compensate for the performance loss caused by 1-bit quantization.
AB - In this letter, we consider the detection of sparse stochastic signals with sensor networks (SNs), where the fusion center (FC) collects 1-bit data from the local sensors and then performs global detection. For this problem, a newly developed 1-bit locally most powerful test (LMPT) detector requires 3.3Q sensors to asymptotically achieve the same detection performance as the centralized LMPT (cLMPT) detector with Q sensors. This 1-bit LMPT detector is based on 1-bit quantized observations without any additional processing at the local sensors. However, direct quantization of observations is not the most efficient processing strategy at the sensors since it incurs unnecessary information loss. In this letter, we propose an improved-1-bit LMPT (Im-1-bit LMPT) detector that fuses local 1-bit quantized likelihood ratios (LRs) instead of directly quantized local observations. In addition, we design the quantization thresholds at the local sensors to ensure asymptotically optimal detection performance of the proposed detector. It is shown theoretically and numerically that, with the designed quantization thresholds, the proposed Im-1-bit LMPT detector for the detection of sparse signals requires less number of sensor nodes to compensate for the performance loss caused by 1-bit quantization.
KW - 1-bit quantization
KW - Distributed detection
KW - locally most powerful tests
KW - sparse signals
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U2 - 10.1109/LSP.2019.2945193
DO - 10.1109/LSP.2019.2945193
M3 - Article
AN - SCOPUS:85077756520
SN - 1070-9908
VL - 26
SP - 1738
EP - 1742
JO - IEEE Signal Processing Letters
JF - IEEE Signal Processing Letters
IS - 12
M1 - 8854811
ER -