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摘要:
针对双机协同定位误差较大问题，首先，在球坐标与直角坐标相结合的基础上，建立双机协同定位的数学模型，并通过对数学模型的分析得出双机距离较近时定位误差大的原因。其次，利用岭回归算法求解出定位精度较高的两组测量子集的目标位置估计值和定位误差协方差矩阵。最后，利用加权最小二乘算法对两组测量子集进行融合定位，推导出协同定位优化算法。仿真分析表明，该算法能显著改善了整个探测区域内的定位精度，并且在双机相距较近时也能保持高的定位精度。
Abstract:Aimed at the problem that the infrared sensor has a large error, in this paper, based on the combination of spherical coordinates and Cartesian coordinates, a mathematical model of cooperative location is established, and the reason for the large positioning error when the distance between the two machines is close is obtained by analyzing the mathematical model. Then, the ridge regression algorithm is used to solve the target position estimation value and the positioning error covariance matrix of the two sets of measurement subsets with higher positioning accuracy. Finally, the two sets of measurement subsets are fused and positioned by the weighted least squares algorithm to derive the cooperative location optimization algorithm. The simulation analysis shows that the proposed algorithm can significantly improve the positioning accuracy in the whole detection area, and can maintain high positioning accuracy when the two machines are close.
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Key words:
- infrared sensor /
- ridge regression /
- cooperative location /
- location accuracy /
- least squares
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图 2 基于岭回归的IRST协同定位优化算法框图
Figure 2. Block diagram of optimization algorithm of IRST cooperative location based on ridge regression
图 4 双机相距0.50 km定位精度图
Figure 4. Positioning accuracy map of two machines separated by 0.50 km
图 5 双机相距0.25 km定位精度图
Figure 5. Positioning accuracy map of two machines separated by 0.25 km
表 1 机载IRST协同定位仿真基本参数设置
Table 1. Basic parameter setup of airborne IRST cooperative location simulation
参数 数值 A机IRST方位测角精度/mrad 0.9 A机IRST俯仰测角精度/mrad 0.9 B机IRST方位测角精度/mrad 1.0 B机IRST俯仰测角精度/mrad 1.0 载机位置测量精度/m 25 目标出现范围(x与y方向)/km ±20 注：目标位置z坐标固定为5 km。 
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