A Vessel Azimuth and Course Joint Re-Estimation Method for Compact HFSWR

摘要

Small-aperture compact high-frequency surface wave radar (HFSWR) suffers from low azimuth accuracy for target detection due to its wide beamwidth. Multitarget tracking (MTT) algorithms, when applied to the raw target detection data of HFSWR, fail to effectively filter the target azimuths, and thus, resulting in inaccurate target tracks and courses. In this article, a vessel azimuth and course joint re-estimation method by exploring Doppler velocity and the information accumulated from consecutive observations is presented. It begins with applying an MTT algorithm to a measured target states data sequence acquired by HFSWR to establish initial target tracks, from which the measured range, azimuth, and radial velocity data sequences are obtained. Then, the azimuth trend is extracted from the obtained azimuth data sequence as roughly corrected azimuth estimates, with which the target locations are roughly corrected. Subsequently, target speeds and initial courses are estimated based on the roughly corrected location data sequence, followed by a data selection procedure based on proposed control parameter rules to select the qualified data for calculating the projected angles in terms of speed and direction, separately. Eventually, the target azimuth data sequence is further refined using a linear azimuth error model, whose parameters are obtained by minimizing the difference between the projected angles using a constrained optimization method. Experimental results from field data demonstrate that the proposed method can estimate the target azimuths with significantly improved accuracy. The deviations of the corrected target locations are considerably reduced, and the accuracy of course estimation is enhanced.