Airborne Target Tracking Algorithm Using Multi-Platform Heterogeneous Information Fusion
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摘要: 该文以高空无人机(UAV)飞艇载双光电传感器，无人机载两坐标雷达对航空目标的精确定位跟踪为研究背景，针对参与融合的传感器均无法独立获得目标位置信息导致传统点迹关联、定位方法失效等问题，提出一种基于多平台异构信息融合的航空目标跟踪算法。首先，在坐标系转换的基础上提出基于角度-距离两级点迹关联算法，从而实现多传感器量测关联。其次，提出基于线面交汇融合定位算法，通过最小二乘法、交汇点投影、距离最近点解算及同源数据压缩确定目标的航迹起始位置。在此基础上，利用空基多平台侦察的异构信息，结合传统无迹卡尔曼滤波器(UKF)设计扩维UKF对航空目标进行跟踪。仿真结果表明，该算法实现了对航空高速目标的高精度跟踪。
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关键词:
- 信息融合 /
- 目标跟踪 /
- 量测关联 /
- 扩维无迹卡尔曼滤波器 /
- 空基多平台
Abstract: An innovative aviation target tracking algorithm is presented in this paper, utilizing high-altitude unmanned airship dual photoelectric sensors in conjunction with Unmanned Aerial Vehicle (UAV)-borne two-coordinate radar. The algorithm addresses the challenge of integrating sensor data to accurately track targets when individual sensors lack complete target position information, thus overcoming limitations of traditional point-trace association methods. Initially, a two-level point-trace correlation algorithm based on angle and distance is introduced for multi-sensor measurement association following coordinate system transformation. Subsequently, a line-plane intersection fusion localization algorithm is proposed to determine the initial target track position through techniques such as least squares method, intersection projection, distance nearest point solution, and homologous data compression. Leveraging heterogeneous information from space-based multi-platform reconnaissance, an extended Unscented Kalman Filter (UKF) is designed to track aviation targets by enhancing the traditional UKF. Simulation results demonstrate that this algorithm achieves superior precision in tracking high-speed aerial targets. -
表 1 仿真参数
参数 名称 数值 参数 名称 数值 ${{\mathrm{S}}} {1_{{\mathrm{location}}}}$ 无人飞艇1坐标 (12.0°N, 130.0°E, 30 km) ${R_{{\mathrm{location}}}}$ 机载雷达地理坐标 (15.1°N, 138.0°E, 20 km) ${{\mathrm{S}}} {2_{{\mathrm{location}}}}$ 无人飞艇2坐标 (12.0°N, 140.0°E, 30 km) ${v_{\mathrm{R}}}$ 机载雷达速度(m/s) (200, 100, 5) ${{T}}_{{{\mathrm{location}}}}^1$ 目标1坐标 (12.3°N, 138.5°E, 6 km) ${v_{{\mathrm{T}}1}}$ 目标1速度(m/s) (300, 100, 10) ${{T}}_{{{\mathrm{location}}}}^2$ 目标2坐标 (12.4°N, 138.5°E, 6 km) ${v_{{\mathrm{T}}2}}$ 目标2速度(m/s) (–300, 100, 10) ${{T}}_{{{{\mathrm{location}}} }}^3$ 目标3坐标 (12.5°N, 138.5°E, 6 km) ${v_{{\mathrm{T}}3}}$ 目标3速度(m/s) (300, –100, 10) ${a_{\mathrm{T}}}$ 目标加速度(m/s2) (0.5, –0.2, 0.05) $\sigma $ 量测误差 100 m, 0.5°, 0.01°, 0.01°, 0.03°, 0.03° 
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表 3 仿真具体结果
融合类型 位置估计误差(m) 速度估计误差(m/s) 雷达-传感器1-传感器2 32.6 0.006 传感器1-传感器2 53.7 0.008 雷达-传感器1 95.6 0.013 雷达-传感器2 150.7 0.020
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