矢量水听器阵列自适应子空间跟踪算法

矢量水听器能同时共点获得声场中声压和振速，与其他水听器相比，能获得更多的信息量，具有很好的应用前景。矢量水听器阵列的MUSIC算法能实现360°无模糊方位估计，然而对于方位时变的目标源，该算法很难完成对上述目标源方位进行实时跟踪估计。鉴于此，将MALASE算法和MUSIC算法相结合，提出了一种矢量水听器阵列的自适应子空间跟踪算法。仿真结果表明，该算法既保留了MUSIC算法的性能，又实现了对目标源进行实时跟踪估计，且方位估计误差仅为0．4°左右。

Adaptive subspace tracking algorithm based on acoustic vector sensor array

An acoustic vector sensor measures the acoustic pressure and all three components of the aconstic particle velocity at a single point in space. The main advantage of those acoustic vector-sensors over traditional scalar sensors is that they make full use of more available acoustic information, hence they will be widely used in the near future. Using acoustic vector sensor array, all direction of arrival （DOA） with out ambiguity can be estimated by multiple signal classification （MUSIC） algorithm. But when the source bearing is time-varying, the algorithm can not track the source bearing in real time. In order to resolve the problem, an adaptive subspace tracking algorithm using the maximum likelihood adaptive subspace estimation （MALASE） algorithm and MUSIC algorithm is proposed. Computer simulation results show that the method has good direction tracking performance with an error of only about 0.4°.