Oh Raegeun, Shi Yifang, Choi Jee Woong
Department of Marine Science & Convergence Engineering, Hanyang University ERICA, Ansan 15588, Korea.
School of Automation, Hangzhou Dianzi University, Xiasha Higher Education Zone, 2nd Street, Hangzhou 310018, China.
Sensors (Basel). 2021 Mar 13;21(6):2033. doi: 10.3390/s21062033.
Bearing-only target motion analysis (BO-TMA) by batch processing remains a challenge due to the lack of information on underwater target maneuvering and the nonlinearity of sensor measurements. Traditional batch estimation for BO-TMA is mainly performed based on deterministic algorithms, and studies performed with heuristic algorithms have recently been reported. However, since the two algorithms have their own advantages and disadvantages, interest in a hybrid method that complements the disadvantages and combines the advantages of the two algorithms is increasing. In this study, we proposed Newton-Raphson particle swarm optimization (NRPSO): a hybrid method that combines the Newton-Raphson method and the particle swarm optimization method, which are representative methods that utilize deterministic and heuristic algorithms, respectively. The BO-TMA performance obtained using the proposed NRPSO was tested by varying the measurement noise and number of measurements for three targets with different maneuvers. The results showed that the advantages of both methods were well combined, which improved the performance.
由于缺乏水下目标机动信息以及传感器测量的非线性，通过批处理进行的纯方位目标运动分析（BO-TMA）仍然是一个挑战。传统的BO-TMA批估计主要基于确定性算法进行，最近也有关于启发式算法的研究报道。然而，由于这两种算法各有优缺点，对一种互补缺点并结合两种算法优点的混合方法的兴趣与日俱增。在本研究中，我们提出了牛顿-拉夫逊粒子群优化算法（NRPSO）：一种结合牛顿-拉夫逊方法和粒子群优化方法的混合方法，这两种方法分别是利用确定性算法和启发式算法的代表性方法。通过改变测量噪声和测量次数，对三个具有不同机动的目标进行测试，以检验使用所提出的NRPSO获得的BO-TMA性能。结果表明，两种方法的优点得到了很好的结合，从而提高了性能。
