A Vessel Azimuth and Course Joint Re-Estimation Method for Compact HFSWR
A Vessel Azimuth and Course Joint Re-Estimation Method for Compact HFSWR
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DOI：
10.1109/tgrs.2019.2943065
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发表时间：
2020-02
影响因子：
8.2
通讯作者：
Weifeng Sun;Weimin Huang;Yonggang Ji;Yongshou Dai;Peng Ren;Peng Zhou;Xianfeng Hao
中科院分区：
文献类型：
--
作者：
Weifeng Sun;Weimin Huang;Yonggang Ji;Yongshou Dai;Peng Ren;Peng Zhou;Xianfeng Hao
小口径高频地波雷达（HFSWR）波束宽度大，导致其目标检测方位精度低。多目标跟踪（MTT）算法在应用于高频地波雷达的原始目标检测数据时，不能有效地滤除目标方位角，从而导致目标航迹和航向不准确。本文提出了一种利用多普勒速度和连续观测积累的信息对舰船方位和航向进行联合估计的方法。首先将MTT算法应用于HFSWR获得的测量目标状态数据序列，以建立初始目标轨迹，由此获得测量的距离、方位和径向速度数据序列。然后，从所获得的方位数据序列中提取方位趋势作为粗略校正的方位估计，利用该粗略校正的方位估计来粗略校正目标位置。随后，基于粗略校正的位置数据序列来估计目标速度和初始航向，随后是基于所提出的控制参数规则的数据选择过程，以选择用于分别计算速度和方向方面的投影角的合格数据。最后，目标方位数据序列进一步细化使用线性方位误差模型，其参数通过使用约束优化方法最小化投影角度之间的差异来获得。实测数据的实验结果表明，该方法可以有效地提高目标方位角的估计精度。修正后的目标位置偏差大大减小，航向估计精度提高。
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.