Joint Bernoulli Filtering and MIMO Processing for Detection of Moving Targets in Shallow Ocean Environments
| Autor: | Zhongyong Chen, Peng Zhang, Xiang Pan, Zhongdi Liu, Huangyu Dai |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Beamforming
spatial diversity General Computer Science Computer science business.industry moving target MIMO General Engineering sea trial Antenna diversity Sonar TK1-9971 MIMO detection Bernoulli's principle Integrator Clutter General Materials Science Computer vision Artificial intelligence Electrical engineering. Electronics. Nuclear engineering Cluster analysis business Bernoulli filtering Computer Science::Information Theory |
| Zdroj: | IEEE Access, Vol 9, Pp 126307-126318 (2021) |
| ISSN: | 2169-3536 |
| Popis: | A joint multiple-input multiple-output (MIMO) processing framework is proposed to exploit spatial diversity and moving cues for the enhancement of target detection in a shallow ocean environment. Orthogonal signals are transmitted to illuminate different aspects of a target and beamforming operation is carried out over the received data for estimating target bearing. The target range is achieved by a replica correlation integrator where the beamformer outputs are matched with transmitted signals. After meanshift clustering algorithm is carried out over the bearing-range spectrums to generate the clutter centers, the potential trajectories of targets can be tracked by an improved Bernoulli filter with inputs of these centers. The at-sea experimental results have shown the effectiveness of the joint processing framework in MIMO detection of moving targets. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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