基于主被动联合工作声呐探测信息的UKF目标运动要素解算方法

张洪瑞; 苏骏; 李谦; 李斌; 寇小明

doi:10.11993/j.issn.2096-3920.2023-0045

基于主被动联合工作声呐探测信息的UKF目标运动要素解算方法

doi: 10.11993/j.issn.2096-3920.2023-0045

1.
中国船舶集团有限公司第七〇五研究所, 陕西西安, 710077
2.
西北工业大学, 陕西西安, 710077

详细信息

作者简介:
张洪瑞(1999-), 男, 在读硕士, 主要研究方向为无人水下航行器声自导总体技术

中图分类号: TJ634; U674
计量
- 文章访问数: 44
- HTML全文浏览量: 12
- PDF下载量: 21
- 被引次数: 0
出版历程
- 收稿日期: 2023-04-26
- 修回日期: 2023-05-28
- 录用日期: 2023-06-12

Calculation Method for UKF Target Motion Elements Based on Detection Information of Active and Passive Sonars

1.
The 705 Research Institute, China State Shipbuilding Corporation Limited, Xi’an 710077, China
2.
Northwest Polytechnic University, Xi′an 710077, China

摘要

摘要: 目标运动要素作为水下作战的重要信息, 其解算结果会对目标命中概率产生较大影响, 进而影响打击决策。目前传统的水面舰反潜目标运动要素解算方法信息来源为主动声呐。由于主动声呐采用固定送数周期, 在连续跟踪过程部分时间段目标信息存在空隙, 导致目标运动要素解算结果误差大、收敛速度慢。为了更加快速、准确地获得目标运动要素, 文中将被动声呐观测信息加入到滤波过程中, 使用无迹卡尔曼滤波方法对只采用主动声呐探测信息方式及主被动声呐联合探测信息方式进行仿真研究, 并对结果进行对比分析。仿真结果表明, 在其他条件相同的情况下, 与传统方法相比, 文中方法可明显提高收敛精度和速度, 使速度解算精度平均提升33.55%, 方位角解算精度平均提升38.99%, 航向解算精度平均提升35.29%, 验证了该方法的有效性。
- 水下作战 /
- 目标运动要素 /
- 无迹卡尔曼滤波 /
- 主动声呐 /
- 被动声呐
Abstract: The target motion element is important information in anti-submarine warfare, and its calculation results have a great influence on the hitting probability of the target, thus affecting combat decision-making. At present, active sonar is the main source of information in the calculation method for target motion elements in anti-submarine warfare of surface ships. However, active sonar uses a fixed number of sending periods, and there are gaps in the target information during the continuous tracking process. As a result, there are large errors and slow convergence in the calculation results of the target motion elements. In order to obtain the target motion elements more quickly and accurately, the detection information of passive sonar was added to the filtering process. The unscented Kalman filter(UKF) method was used to simulate the information detection methods using only active sonar and both active and passive sonars, and the results were compared. The simulation results show that under the same conditions, the proposed method can significantly improve the convergence accuracy and speed compared with the traditional method. It can improve the calculation accuracy of speed, azimuth, and heading angle by 33.55%, 38.99%, and 35.29% on average, verifying its effectiveness.
- underwater warfare /
- target motion element /
- unscented Kalman filter /
- active sonar /
- passive sonar

HTML全文

图 1 目标跟踪原理图

Figure 1. Schematic diagram of target tracking

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图 2 观测站-目标几何态势

Figure 2. Observation station-target geometry situation

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图 3 情况1跟踪仿真结果

Figure 3. Tracking simulation results under condition 1

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图 4 情况1航向角、方位角、速度和距离估计RMSE

Figure 4. The estimation RMSE of heading angle, azimuth angle, velocity and range under condition 1

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图 5 情况2跟踪仿真结果

Figure 5. Tracking simulation results under condition 2

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图 6 情况2航向角、方位角、速度和距离估计RMSE

Figure 6. The estimation RMSE of heading angle, azimuth angle, velocity and range under condition 2

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表 1 情况1估计RMSE与标准值对比

Table 1. Comparison of the estimated RMSE with the standard value under condition1

记录周期	目标距离/km	目标方位/(°)	目标航向/(°)	目标速度/kn	距离/m		速度/kn		方位角/(°)		航向角/(°)
记录周期	目标距离/km	目标方位/(°)	目标航向/(°)	目标速度/kn	RMES	标准值	RMES	标准值	RMES	标准值	RMES	标准值
6	7	30	0	24	118.5	179.6	4.53	2	2.31	2.5	14.52	9.28
			90		108.7	175.2	5.37	2	1.69	2.5	13.51	9.28
			180		84.5	151.8	4.35	2	1.85	2.5	10.32	9.28
			−90		84.1	157.2	5.33	2	1.90	2.5	11.92	9.28
12	7	30	0	24	96.9	184.0	1.61	2	2.14	2.5	5.19	9.28
			90		86.7	178.8	1.77	2	1.43	2.5	4.60	9.28
			180		59.0	127.4	1.39	2	1.71	2.5	3.70	9.28
			−90		58.3	135.8	1.63	2	1.67	2.5	3.16	9.28
15	7	30	0	24	91.2	186.2	1.09	2	2.11	2.5	3.86	9.28
			90		79.6	182.4	1.22	2	1.38	2.5	3.57	9.28
			180		51.1	118.7	0.94	2	1.64	2.5	3.01	9.28
			−90		49.7	125.9	1.08	2	1.56	2.5	2.29	9.28

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表 2 情况2给定态势下估计RMSE与标准值对比

Table 2. Comparison of the estimated RMSE with the standard under condition2

记录周期	目标距离/km	目标方位/(°)	目标航向/(°)	目标速度/kn	距离/m		速度/kn		方位角/(°)		航向角/(°)
记录周期	目标距离/km	目标方位/(°)	目标航向/(°)	目标速度/kn	RMES	标准值	RMES	标准值	RMES	标准值	RMES	标准值
6	7	30	0	24	110.0	179.6	3.57	2	1.56	2.5	8.05	9.28
			90		121.0	175.2	2.98	2	1.80	2.5	8.64	9.28
			180		101.0	151.8	3.31	2	0.50	2.5	7.74	9.28
			−90		87.9	157.2	2.61	2	0.43	2.5	7.97	9.28
12	7	30	0	24	90.4	184.0	1.29	2	1.53	2.5	2.61	9.28
			90		106.9	178.8	1.08	2	1.72	2.5	2.94	9.28
			180		77.2	127.4	0.96	2	0.46	2.5	2.62	9.28
			−90		65.3	135.8	0.75	2	0.32	2.5	2.74	9.28
15	7	30	0	24	87.4	186.2	0.96	2	1.51	2.5	1.80	9.28
			90		101.2	182.4	0.81	2	1.71	2.5	2.43	9.28
			180		67.4	118.7	0.62	2	0.46	2.5	1.92	9.28
			−90		49.7	125.9	1.08	2	1.56	2.5	2.29	9.28

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