A Method for Maritime Target Tracking Based on Kernelized Spectral Filter

Abstract

Maritime target tracking can be applied in the fields of intelligent marine transportation and resource protection. The spectral filter tracking method focuses on the adaptability of the local appearance changes of targets, with the graph representation. Considering the complexity and diversity of the marine environment, we propose a maritime target tracking algorithm based on kernelized spectral filter. The spectral filtering is modeled as a tracking framework based on kernel regression. According to graph signal processing and spectral graph theory, based on the description of kernel regression on graph signals, we deduce and discuss the construction of the filter, the calculation of kernel matrix, the solution of kernel regression model, and the prediction of tracking position. With the help of a nonlinear model, it can effectively improve the precision under complex tracking conditions. The experimental results on the benchmark dataset verify the effectiveness of the proposed method, especially when the target tracking is affected by waves or wakes.