LAUSR

The challenges facing Unmanned Underwater Vehicle (UUV) target state estimation are observation uncertainty, the unpredictability of the target motion model, and the complex relative motion of the moving target to the observer. Aiming at the above problems, a Convolutional Neural Network Particle Filter (CNNPF) is proposed and applied to UUV target state estimation using forward-looking sonar. Firstly, we design a target state prediction network based on Convolutional Neural Network (CNN) to describe the nonlinear measurement and non-Markov target motion process models. Then, a target state prediction dataset is established for the prediction network to build the state space of UUV target state estimation and extract target motion features from the non-sequential measurements. Moreover, particles are sampled from the observations with disturbed time series to approximate the non-Gaussian distribution of measurements. The sampling particles in the target measured state space are mapped to target predictive state space through the prediction network. Convolutional and pooling layers impart invariance to the prediction network and improve its adaptability to uncertain measurement. Finally, the predicted states of particles are used to estimate the target states. The statistical experiment, simulation cases, and hardware-in-the-loop experiment proved the applicability and performance of the CNNPF.