Collaborative Target Azimuth Perception Algorithm of Unmanned Aerial Vehicles Based on Spatial Spectrum Estimation
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摘要:
无人机协同目标感知技术是有人机无人机混合运行的重要安全保障. 针对复杂空域环境下的感知可靠性问题，分析大中型无人机的复杂融合空域运行场景，并确定无人机协同目标感知的精准性、高实时性、抗干扰性和低载荷性等需求，提出一种四单元阵列天线和数字化射频体制的无人机协同目标感知系统架构；同时，结合空管雷达信号特性和天线体制，设计方位感知算法，通过修正协方差矩阵、信号子空间加权和噪声子空间加权等方法，设计基于多信号分类（multiple signal classification，MUSIC）的空间谱估计算法，并提出基于子空间分解的幅相误差在线估计算法；最后，开展算法仿真试验和实际空域环境飞行试验. 研究结果表明：相比传统MUSIC算法，优化算法的方位感知高分辨性能提升23.3%，并改善了无人机协同目标方位感知的高实时性、抗干扰性和低载荷性.
Abstract:Collaborative target perception technology of unmanned aerial vehicles (UAVs) is an important security guarantee for the mixed operation of manned aerial vehicles and UAVs. In view of the perception reliability problem in complex airspace environments, the operation scenarios of large and medium-sized UAVs in complex mixed airspace were analyzed, and the needs of collaborative target perception of UAVs, such as precision, high real-time performance, anti-interference, and low load were determined. A collaborative target perception system architecture of UAVs combining a four-unit array antenna and digital radio frequency was proposed. At the same time, the signal characteristics and antenna system of air traffic control (ATC) radar were utilized to design an azimuth perception algorithm. By modifying the covariance matrix and weighting signal subspace and noise subspace, a spatial spectrum estimation algorithm based on multiple signal classification (MUSIC) was designed. In addition, an online amplitude-phase error estimation algorithm based on subspace decomposition was designed. Finally, the algorithm simulation test and flight test in a real airspace environment were carried out. The research results show that compared with the traditional MUSIC algorithm, the improved algorithm improves the high resolution performance of azimuth perception by 23.3% and enhances the high real-time performance, anti-interference, and low load of the collaborative target azimuth perception of UAVs.
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图 2 无人机协同目标感知系统架构
Figure 2. Collaborative target perception system architecture of UAVs
图 10 不同信噪比条件下的算法估计偏差
Figure 10. Estimation deviation under different signal-to-noise ratios
表 1 幅相误差
Table 1. Amplitude-phase error
通道 设计值 估计值 1 1.0000 1.0000 2 0.2249 + 1.178 7i 0.1727 + 1.103 9i 3 −0.8229 − 1.132 6i −0.7198 − 1.012 8i 4 −1.5874 + 0.200 5i −1.3692 + 0.158 8i 
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