Sound field reconstruction technology is used to provide accurate primary reference signals for active noise control systems by reconstructing the interior sound field. Traditionally, time-domain noise signal-based reconstruction modeling has certain deficiencies, such as large data volume, noise reconstruction model complexity and considerable time consumption. Hence, a novel Signal compression optimisation-based BP network for passenger head position signal reconstruction (CBHSR) algorithm is proposed. Based on compressed sensing, the proposed algorithm converts raw multi-source signals into the compressed domain to implement compressed sampling. The signal reconstruction model is created by regarding the optimal fitness value as the initial weight and the threshold of the signal reconstruction BP network, and training with the compressed multi-source data. The recovery compression signal method realizes the time-domain signal reconstruction of the passenger head position. The effectiveness of the proposed CBHSR algorithm is validated using noise signal sources collected from a vehicle. Compared with the reconstruction model of the BP algorithm, the proposed algorithm is superior in reconstruction accuracy and time consumption.

声场重建技术通过重建室内声场，为主动噪声控制系统提供准确的初级参考信号。传统上，基于时域噪声信号的重构建模存在一定的不足，如数据量大、噪声重构模型复杂、耗时等。因此，提出了一种新的基于信号压缩优化的 BP 网络，用于乘客头部位置信号重建 (CBHSR) 算法。该算法基于压缩感知，将原始多源信号转换为压缩域，实现压缩采样。信号重建模型是通过将最优适应值作为信号重建BP网络的初始权重和阈值来创建的，并使用压缩的多源数据进行训练。恢复压缩信号方法实现了乘客头部位置的时域信号重构。使用从车辆收集的噪声信号源验证了所提出的 CBHSR 算法的有效性。与BP算法的重建模型相比，所提算法在重建精度和时间消耗方面具有优越性。