Wideband Multitarget Tracking Based on Dynamic Bayesian Network Learning in an Acoustic Sensor Array Network
| Autor: | Ming Bao, Wenqiong Zhang, Jianfei Tong, Xiaodong Li, Xiao-Ping Zhang |
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| Rok vydání: | 2022 |
| Předmět: |
Optimization problem
Computational complexity theory Computer Networks and Communications Computer science Posterior probability Estimator 020206 networking & telecommunications 02 engineering and technology Computer Science Applications Noise Hardware and Architecture Signal Processing Convergence (routing) 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Graphical model Algorithm Dynamic Bayesian network Information Systems |
| Zdroj: | IEEE Internet of Things Journal. 9:4769-4787 |
| ISSN: | 2372-2541 |
| DOI: | 10.1109/jiot.2021.3108528 |
| Popis: | The multi-target tracking (MTT) based on distributed fusion methods in an acoustic sensor array network (ASAN) is limited by the performance of measurement parameter estimators, such as the received signal strength (RSS) and the direction-of-arrival (DOA). For measurement parameters with low accuracy and resolution, the MTT may fail in subsequent steps, e.g., data association, because the loss of upstream information cannot be made up by downstream processing. Thus, we propose a new wideband MTT algorithm based on dynamic Bayesian network (DBN), which treats the ASAN as an overall extended array, and directly estimates the target states from the raw acoustic data. The DBN fuses the near-field model, the acoustic propagation model and the motion model. These sub-models can be optimized by each other, improving the final estimation. Also, for each sub-band, target signals and the precision parameters of the sensor noise are treated as hidden random variables. Based on this, the weight of each sub-band can be automatically adjusted according to the accurate hidden variables. Besides, the optimization problem of the posterior probability is transformed into a graphical model learning problem. Moreover, for non-conjugate models, a novel algorithm based on Laplace approximations (LAs) with Newton’s method (NM) is developed, i.e., DBN-LA-NM, bypassing data association. In addition, the corresponding CRLB and convergence conditions are derived. The numerical simulation results show that the proposed algorithm outperforms existing MTTs based on the near-field model in terms of accuracy, convergence and computational complexity. Field experiments further verify the feasibility of the proposed algorithm. |
| Databáze: | OpenAIRE |
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