A Real-Time Spatial Registration Algorithm for Airborne Networked Radar Without Prior Information

doi:10.3969/j.issn.1009-086x.2025.04.011

Abstract

Abstract:

This paper presents a novel spatial registration strategy for multiple motion radars with varying spatial attitudes， which does not require prior information such as radar position and attitude. The strategy involves key steps， including the real-time calculation of registration parameters and optimization of fusion tracks. The recursive least squares （RLS） is employed to establish spatial attitude relationships between radars by using target track data. A method for reverse adjustment of target trajectory-level spatial registration parameters based on fusion results is presented. The method models registration errors and optimizes them using gradient descent， resulting in reduced fusion trajectory errors， improved registration accuracy， and enhanced tracking quality. The proposed strategy provides a comprehensive and efficient solution for real-time registration of radar spatial attitudes. It has both theoretical and practical value.

Key words: networked radar, spatial registration, maneuver targets, data fusion, recursive least squares（RLS）

摘要：

针对多部空间姿态时刻变化的机载雷达，提出了一种全新的、无需依赖先验信息（如雷达位置和姿态）的空间配准策略，本策略涉及到实时配准参数解算以及融合点迹优化等多个关键环节。利用目标点迹数据建立雷达间的空间姿态关系，借助递归最小二乘法（recursive least squares,RLS）迭代求解旋转矩阵和平移向量，进而实现各雷达坐标系的实时配准。此外，引入了一种基于融合结果的目标轨迹级空间配准参数反向调节策略，通过构建配准误差模型并运用梯度下降法进行优化，有效降低了融合轨迹误差，提升了配准精度与跟踪质量。所提策略为雷达空间姿态的实时配准问题提供了一种全面且高效的解决方案，具有重大的理论价值与实际应用前景。

关键词: 组网雷达, 空间配准, 机动目标, 数据融合, 递归最小二乘法

CLC Number:

TN959.73

Xincheng LIU, Chenyu ZHU, Yubing HAN. A Real-Time Spatial Registration Algorithm for Airborne Networked Radar Without Prior Information[J]. Modern Defense Technology, 2025, 53(4): 105-111.

刘心成, 朱陈予, 韩玉兵. 无先验信息的机载组网雷达实时空间配准算法[J]. 现代防御技术, 2025, 53(4): 105-111.

Figures/Tables 4

Fig. 1 Diagram of the spatial structure of the registration parameters for reverse adjustment of target point track fusion

Fig. 2 Comparison of target tracking accuracy between KF algorithm and the proposed algorithm

Fig. 3 Comparison of target tracking accuracy between SVD algorithm and the proposed algorithm

Fig. 4 Comparison of the accuracy of target tracking with and without inverse adjustment of alignment parameters

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