面向航空搜救的无线电数据融合定位方法

A Data Fusion Localization Method for Radio Aviation Search and Rescue

随着我国海洋经济事业的蓬勃发展,以及现代化战争中航空救援保障体系的急切需求,航空搜救定位技术越来越受到关注。基于无线电的航空搜救定位技术主要包含了测距、测向以及相应的多源融合技术。然而,搜救飞机上的起落架、轮胎等机载设备会对接收天线造成严重的遮挡、多径,给无线电测向带来较大误差。对此,基于贝叶斯滤波和群体智能理论,提出了一种基于稳健人工鱼群粒子滤波的无线电航空搜救数据融合定位方法。将粒子滤波中的粒子看作鱼群个体,通过模仿鱼群算法的觅食、群聚、追尾、随机游走行为来实现重采样步骤的优化,并且对行为中的搜索范围、寻优速度等方面做了改进,形成测距-测向融合定位算法。实际数据建模和仿真结果表明,所提的融合定位方法能够在较短的时间内达到较为准确的位置精度和角度精度,收敛性较强,具有良好的实时性和鲁棒性。

With the vigorous development of China’s marine economy and the urgent need for a modern aviation rescue support system in wars,aviation search and rescue positioning technology is receiving increasing attention.In the field of radio,aviation search and rescue positioning technology mainly includes ranging module,direction finding module,and corresponding multi-source fusion technology.However,airborne equipment such as landing gear and tires on the search and rescue aircraft can cause serious obstruction and multipath to the receiving antenna,causing significant deviation of radio data,especially direction finding data,resulting in significant positioning errors in final positioning results.Therefore,this work proposes a robust artificial fish swarm particle filter localization scheme based on Bayesian filtering ideas and swarm intelligence algorithms.Firstly,individuals in particle filtering are considered as fish swarm individuals,and the resampling steps are optimized by imitating the feeding,clustering,tailgating,and random walking behaviors of the fish swarm algorithm.Furthermore,improvements are made to the search range and optimization speed in the behavior,resulting in the formation of a ranging direction finding fusion localization algorithm.Actual data modeling and simulation results show that the fusion positioning method proposed by this algorithm can achieve relatively accurate position and angle accuracy in a short period of time,with strong convergence,good real-time performance,and robustness.