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Collaborative Multi-Target Detection in Cognitive Frequency Hopping Systems
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AI
DOI：10.1109/TCCN.2026.3656289.png)
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摘要
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In cognitive frequency hopping (CFH) systems, signal detection in complex electromagnetic environments is a key challenge to the conventional algorithms. Due to multi-target signals, dynamically varying noise, shadowing effects and the hidden terminal phenomenon, most existing signal detection methods are limited in applications. Motivated by these, in this paper, we propose a wideband frequency fitting algorithm based on the Kolmogorov-Smirnov second-order test, that can detect multi-target signals in dynamic environments effectively. Subsequently, a collaborative wideband frequency fitting algorithm is proposed, which utilizes Fisher’s combined probability test to integrate detecting outcomes from multiple distributed nodes, thereby substantially improving detection reliability. Performance analysis including computational complexity and the effect of channel impairments is further provided. Simulation results validate the efficiency and robustness of the proposed methods in practical CFH systems under complex electromagnetic conditions, not only maintaining an unchanged detection probability in such environments but also demonstrating a detection probability improvement of 3–7% at low signal-to-noise ratio compared to recent wideband blind detection methods under the specified simulation conditions.
Keyword:
Cognitive frequency hopping
dynamic noise
multi-target detection
collaborative detection
blind detection
期刊
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IF:
7
论文数:
1.6K
被引数:
5.5K
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