APO-ELM Model for Improving Azimuth Correction of Shipborne HFSWR

Abstract

Shipborne high-frequency surface wave radar (HFSWR) has a wide range of applications and plays an important role in moving target detection and tracking. However, the complexity of the sea detection environment causes the target signals received by shipborne HFSWR to be seriously disturbed by sea clutter. Sea clutter increases the difficulty of azimuth estimation, resulting in a challenging problem for shipborne HFSWR. To solve this problem, a novel azimuth correction method based on adaptive boosting error feedback dynamic weighted particle swarm optimization extreme learning machine (APO-ELM) is proposed to improve the azimuth estimation accuracy of shipborne HFSWR. First, the sea clutter is modeled and simulated. Then, we study its characteristics and analyze the influence of its characteristics on the first-order clutter spectrum and target detection accuracy, respectively. In addition, the proposed improved particle swarm optimization (PSO) and adaptive neuron clipping algorithm are used to optimize the input parameters of the ELM network. Then, the network performs error feedback based on the optimized parameter performance and updates the feature matrix, which can give a minimum clutter-error estimation. After that, it iteratively trains multiple weak learners using the adaptive boosting (AdaBoost) algorithm to form a strong learner and make strong predictions. Finally, after error compensation, the best azimuth estimation results are obtained. The sample sets used for the APO-ELM network are obtained from field shipborne HFSWR data. The network training and testing features include the wind direction, sea current, wind speed, platform speed, and signal-to-clutter ratio (SCR). The experimental results show that this method has a lower root-mean-square error than the back-propagation neural network and support vector regression (SVR) azimuth correction methods, which verifies the effectiveness of the proposed method.