【作者】 李灯熬； 韩冲； 赵菊敏； 邓豆豆； 赵晓芳；

【Author】 Li Dengao;Han Chong;Zhao Jumin;Deng Doudou;Zhao Xiaofang;Taiyuan University of Technology;

【机构】 太原理工大学；

【摘要】 随着全球导航卫星系统广泛应用,用户对接收机性能的需求不断提升,要求GNSS接收机在恶劣环境下具有实时高精度定位的性能,为了满足实时性、高精度的要求,提出了一种基于压缩感知的多源融合导航算法,感知信息空间相关性的多传感器信息压缩融合算法,采用基于压缩感知理论中信息空间相关性、变差函数和卡尔曼滤波原理融合多传感器数据。在保证定位精度的前提下,对接收机的航向角和位置冗余信息进行压缩融合,合理降低导航信息的采样密度,能有效地减少信息采集量和处理时间,同时减小噪声干扰。通过实验验证,接收机中各传感器获得的信息和位置信息的压缩融合,有效的减少了信息采集量,提高了算法的处理速度。同时能够有效抑制噪声干扰,提高了接收机在恶劣环境下的定位精度。更多还原

【Abstract】 With the widespread use of global navigation satellite systems,the users’ demand for the performance of receiver continues to improve,which requires the GNSS receiver to have real-time and high-precision positioning performance in harsh environment.Although the multi-source fusion navigation algorithm can provide high positioning accuracy,but the multi-sensor information with redundancy and complementarity increase the complexity of the algorithm.Therefore,in order to meet the real-time and high-precision requirements,a multi-sensor information compression fusion algorithm based on spatial correlation of perceived information is proposed.The algorithm combines the multi-sensor data based on the spatial correlation of information space,the variance function and the Kalman filter principle in the compression perceptual theory.Under the premise of ensuring the positioning accuracy,the sampling density of navigation information can be reduced reasonably,which can effectively reduce the amount of information acquisition and processing time,and reduce noise interference.更多还原