INS/SFGPS PPP紧组合系统动态补偿滤波算法

惯性/卫星组合导航系统结合精密单点定位技术可有效提高导航定位精度。但精密单点定位技术一般需采用双频接收机,成本较高;同时该系统中采用载波相位作为部分或全部观测量,极容易受到周跳的影响而导致精度下降和系统不稳定。针对上述问题,设计了一种惯性/卫星精密定位紧组合导航系统以及基于动态周跳补偿的鲁棒滤波算法。该系统采用低成本的单频接收机(SFGPS),以精密单点定位技术(PPP)处理过的伪距和载波相位作为观测信息,与惯性导航系统(INS)等效观测量进行紧组合,建立了相应紧组合观测模型并引入周跳作为信息融合滤波状态模型中的状态量,以滤波器信息构建周跳检测统计量并对周跳幅值进行识别和估计,实时补偿观测量以提高观测信息精度,同时以前述周跳估计的结果对状态模型中周跳状态量部分滤波参数进行实时调节。上述方法通过动态补偿周跳误差提高导航精度,通过滤波器参数自适应调节提高滤波稳定性。仿真结果验证了该系统模型及算法的有效性。

Dynamic compensation filter algorithm for INS/SFGPS PPP tightly coupled system

Combined with PPP (Precise Point Positioning), the positioning precise of INS/GPS integrated navigation system could be improved effectively. However, the PPP technique usually requires using double frequency receiver, which is too expensive for widespread use. Moreover, in this system, the carrier phase must be used as partial or whole observation quantities, which is easily affected by cycle slip and leads to decreased precision and unstable system. Aiming at this problem, a new INS/SFGPS PPP tightly coupled navigation system and a robust adaptive filtering method based on dynamic cycle slip compensation are proposed in this paper. The system adopts a low cost single frequency receiver (SFGPS), takes the pseudorange and carrier phase processed by precise point positioning (PPP) as the observation information, which is tightly coupled with the equivalent observation quantity of the inertial navigation system (INS). The corresponding tightly coupled observation model is built and the cycle slip is introduced as the state quantity in the information fusion filter state model. The filter information is used to construct the cycle slip detection statistics, and the cycle slip amplitude is identified and estimated. The observation quantity is compensated in real time to improve the observation information precision. The above mentioned cycle slip estimation result is used to adjust the partial filter parameters of the cycle slip state quantity in the state model. The proposed method improves the navigation precision through dynamically compensating the cycle slip error and improves the filtering stability through adaptively adjusting the filter parameters. Simulation results verify the effectiveness of the proposed system model and algorithm.