A state-constrained tracking approach for Kalman filter-based ultra-tightly coupled GPS/INS integration

被引:9
|
作者
Qin, Honglei [1 ]
Yue, Song [1 ]
Cong, Li [1 ]
Jin, Tian [1 ]
机构
[1] Beihang Univ, Sch Elect & Informat Engn, Beijing 100191, Peoples R China
关键词
KF-based tracking; INS aiding; State-constrained KF; Doppler-constraint; Moving horizon estimation; FUZZY-LOGIC; LOOP;
D O I
10.1007/s10291-019-0844-0
中图分类号
TP7 [遥感技术];
学科分类号
081102 ; 0816 ; 081602 ; 083002 ; 1404 ;
摘要
The traditional design of tracking loop in global positioning system (GPS), known as the combination of phase-locked loop and delay-locked loop, is fragile under complex environments. With the increasing requirements for tracking performance under more harsh applications, several implementations have emerged in recent years, among which Kalman filter (KF)-based tracking loop is widely used due to its adaptive nature and robust feature, and it could achieve a higher dynamics performance with the aid of inertial navigation system (INS). However, even more critical conditions, such as severe fading, abrupt phase changes, and signal interference coexisting with high user dynamics, are now challenging the traditional carrier tracking architectures, thus calling for the enhancement of robust carrier tracking techniques. A state-constrained Kalman filter-based (SC-KF) approach is proposed to restrict the errors of the tracking loop and to enhance the robustness of the tracking process in high dynamics and signal attenuation environments. In the SC-KF, the system model of INS-aided KF-based tracking loop is built from a perspective of control theory. Based on the ultra-tight GPS/INS integrated scheme, a Doppler-constrained method and moving horizon estimation architecture are introduced to correct the Doppler state and the code, carrier phase states in KF-based tracking loop, respectively. Software and hardware simulations indicate that the proposed architecture has a better performance in tracking and navigation domains comparing with the conventional INS-aided KF-based tracking loop under severe environments.
引用
收藏
页数:13
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