Integrated Sensing and Communication Target Detection Framework and Waveform Design Method Based on Information Theory

Target detection is a core function of integrated sensing and communication (ISAC) systems. The traditional likelihood ratio test (LRT) target detection algorithm performs inadequately under low signal-to-noise ratio (SNR) conditions, and the performance of mainstream orthogonal frequency division m...

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Vydáno v:Sensors (Basel, Switzerland) Ročník 25; číslo 2; s. 465
Hlavní autoři: Miao, Qilong, Shen, Xiaofeng, Xie, Chenfei, Gao, Yong, Chen, Lu
Médium: Journal Article
Jazyk:angličtina
Vydáno: Switzerland MDPI AG 15.01.2025
MDPI
Témata:
Algorithms
Codes
Communication
Design techniques
Entropy
Fourier transforms
Hypotheses
Hypothesis testing
Information theory
integrated sensing and communication
Optimization
OTFS
Random variables
Signal processing
target detection
Unmanned aerial vehicles
waveform design
integrated sensing and communication
target detection
information theory
waveform design
OTFS
ISSN:1424-8220, 1424-8220
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Shrnutí:Target detection is a core function of integrated sensing and communication (ISAC) systems. The traditional likelihood ratio test (LRT) target detection algorithm performs inadequately under low signal-to-noise ratio (SNR) conditions, and the performance of mainstream orthogonal frequency division multiplexing (OFDM) waveforms declines sharply in high-speed scenarios. To address these issues, an information-theory-based orthogonal time frequency space (OTFS)-ISAC target detection processing framework is proposed. This framework adopts the OTFS waveform as its fundamental signal. The target detection is implemented through a relative entropy test (RET) comparing echo signals against target presence/absence hypotheses. Furthermore, to enhance the system’s target detection capability, the iterative OTFS-ISAC waveform design (I-OTFS-WD) method which maximizes the relative entropy is proposed. This method utilizes the minorization-maximization (MM) algorithm framework and semidefinite relaxation (SDR) technique to transform the non-convex optimization problem into an iterative convex optimization problem for resolution. The simulation results demonstrate that, under sufficient sample conditions, the RET algorithm achieves a 9.12-fold performance improvement over LRT in low-SNR scenarios; additionally, the optimized waveform reduces the sample requirements of the RET algorithm by 40%, further enhancing the target detection capability of the OTFS-ISAC system.
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ISSN:1424-8220
1424-8220
DOI:10.3390/s25020465