Performance Research on Multi-Target Detection in Different Noisy Environments

Yin, Yuhong ^[3] ; Jia, Qian ^[1] ; Xu, Huiqi ^[2] ; Fu, Guanglei ^[2]
1. [1] Xi'an University of Finance and Economics
  
  Xi'an University of Finance and Economics
  
  China
2. [2] Northwestern Polytechnical University
  
  Northwestern Polytechnical University
  
  China
3. [3] School of Electronic Engineering, Xi'an Aeronautical Institute, Xi'an, China
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Localización: ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal, ISSN-e 2255-2863, Vol. 13, Nº. 1, 2024
Idioma: inglés
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Resumen
- This paper studies five classic multi-target detection methods in different noisy environments, including Akaike information criterion, ration criterion, Rissanen's minimum description length, Gerschgorin disk estimator and Eigen-increment threshold methods. Theoretical and statistical analyses of these methods have been done through simulations and a real-world water tank experiment. It is known that these detection approaches suffer from array errors and environmental noises. A new diagonal correction algorithm has been proposed to address the issue of degraded detection performance in practical systems due to array errors and environmental noises. This algorithm not only improves the detection performance of these multi-target detection methods in low signal-to-noise ratios (SNR), but also enhances the robust property in high SNR scenarios.