城市复杂环境下GNSS/INS组合导航算法研究

针对城市环境下GNSS车辆导航存在卫星信号易受影响的问题，利用GNSS/INS组合算法提高复杂环境下城市车辆定位性能。基于城市环境下实测GNSS数据评估分析定位结果，使用GNSS/INS组合的常规卡尔曼滤波算法实现卫星失锁区域导航。同时，提出一种基于新息的自适应卡尔曼滤波算法，可有效增强卫星数较少及信号干扰严重区域的车辆导航定位能力。该方法利用量测与预测的关系构造自适应因子，改善定位精度。结果表明，常规卡尔曼滤波可在20 s卫星信号失锁情况下保证亚m级导航精度，自适应卡尔曼滤波算法在卫星信号受到严重干扰时，其定位精度相比于常规卡尔曼滤波算法提高30%，可满足在城市复杂环境下的高精度、高可靠性车辆导航定位服务需求。

Research on GNSS/INS Integrated Navigation Algorithm in Complex Urban Environments

Aiming at the problem of susceptibility to satellite signals in GNSS vehicle navigation in urban environments, we use the GNSS/INS combined algorithm to improve positioning performance of urban vehicles in complex environments. Based on actual measurement GNSS data from the urban environment, we evaluate and analyze the positioning results, and use the conventional Kalman filtering algorithm of the GNSS/INS combination to realize the navigation of the satellite lock-out area. At the same time, we propose an adaptive Kalman filtering algorithm based on innovation, which can effectively enhance the navigation and positioning capabilities of vehicles in areas with fewer satellites and severe signal interference. This method uses the relationship between measurement and prediction to construct an adaptive factor to improve positioning accuracy. The results show that the conventional Kalman filter can guarantee sub-meter navigation accuracy when the satellite signal is out of lock in 20 s. For satellite signals with severely interference, the positioning accuracy of the adaptive Kalman filtering algorithm is increased by 30% when compared with the conventional Kalman filter. The adaptive Kalman filtering algorithm can meet the needs of high-precision and high-reliability vehicle navigation and positioning services in the complex urban environment.