To improve the performance of strapdown inertial navigation system (SINS)/ Doppler velocity logs (DVL) integrated navigation system, a new Interacting Multiple Model Feedback Particle Filter (IMMFPF) approach is proposed under the condition of complex and changeable underwater environment. In this scheme, the Interacting Multiple Model uses to deal with the model switch, while FPF approach takes each particle as a controllable stochastic system. By finding the solution about the Euler-Lagrange boundary value, the optimal feedback control law of Kullback-Leibler distance is obtained, and then the state of the particle itself is corrected and updated. Both simulations and field test illustrate that proposed IMMFPF approach can achieve a more accurate solution and make a more correct and effective response for SINS/DVL integrated navigation system.

Faster RCNN is a classic algorithm with high accuracy and a wide range of applications in the field of target detection, and Cascade RCNN is improved based on Faster RCNN. The article applies the Cascade RCNN method to the detection of marine ship targets. It improves the traditional Faster RCNN algorithm and extracts areas that may contain pedestrians through RPN. In this paper, a multi-layer cascade detector is used to distinguish and classify the target area, and an algorithm is designed to detect and verify the data set. In the end, it is concluded that the Cascade RCNN algorithm performs better than the traditional algorithm.