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Acoustic proposed target detection and distance estimation method for homosexual environment
[ Instrument R&D of Instrumentation Network ] Space-time Adaptive Detection (STAD) is a signal processing technology based on space-time joint processing and aimed at target detection. Compared with simple time-domain or space-dimensional dimensional processing , With stronger target detection and interference suppression capabilities.
The existing STAD technology is still far from practical application, mainly for the following reasons: one is that the inverse matrix of the high-dimensional covariance matrix needs to be solved in the calculation, and the calculation is huge; the second is that the target is usually assumed to have an ideal sampling model, and there is no energy leakage. It is seriously inconsistent with the actual situation; the third is that the existing research generally assumes isotropic environmental conditions, and rarely considers the actual non-isotropic environmental conditions represented by partially isotropic environments (PHE).
In order to solve the above problems, the team of Hao Chengpeng, the Key Laboratory of Information Technology for Underwater Vehicles, Institute of Acoustics, Chinese Academy of Sciences, conducted a collaborative study with Italian counterparts, and proposed three parameterized space-time adaptive detection methods suitable for some isotropic environmental conditions. They are the modified parametric generalized likelihood ratio test (P-GLRT-PHE) and the modified parametric adaptive matched filter for PHE (MP-GLRT-PHE). AMF-PHE) and improved Wald detection (the modified parametric Wald for PHE, MP-Wald-PHE) can realize the comprehensive utilization of background interference structured information and target energy leakage information, effectively reducing the amount of calculation and leakage loss At the same time, accurately estimate the target distance parameters.
When designing the detection method, this study considered the target energy leakage in samples at close distances and modeled the background interference as a multi-channel autoregressive Gaussian process. The research results show that MP-AMF-PHE and MP-Wald-PHE have the same detection statistics and they are equivalent; the proposed MP-GLRT-PHE and MP-AMF-PHE are better than the existing improved adaptive correlation estimation Modified adaptive coherence estimator (M-ACE), normalized parameter AMF (normalized parametric AMF, NPAMF) and scale-invariant parametric Rao (scale-invariant parametric Rao, SI-PRao) methods have obvious performance advantages, and It has smaller distance estimation error than M-ACE. In the follow-up research, the researchers will further extend the proposed method to non-Gaussian interference background or consider the presence of noise-like active interference.
Related results were published online in IEEE Transactions on Aerospace and Electronic Systems. The research was supported by the National Natural Science Foundation of China.