Enhancing the Performance of Ultra-Tight Integration of GPS/PL/INS: A Federated Filter Approach

Abstract

The integration of GPS, PL and INS sensors can be implemented at three different levels. Compared with loose and tight integration, ultra-tight integration offers numerous advantages including increased robustness under high dynamics, and improved antijamming performance. In current ultra-tight integration scenarios, a centralised Kalman filter is commonly employed to fuse either In-phase (I) and Quadrature (Q) data from the tracking loop or the pseudorange measurement and Position, Velocity, Attitude (P, V, A) measurements from the Inertial Navigation System (INS). Though relatively simple, this centralised filter structure has some disadvantages. Firstly, to reduce the computational load, the filter only makes coarse estimates of Inertial Measurement Unit (IMU) random errors, which significantly degrades the system performance. Secondly, for more accurate estimates, the filter becomes much more complicated, resulting in a large increase in the computation time. All of these hinder the performance of ultra-tight integration considerably. This paper proposes a federated filter structure for the ultra-tight integration of GPS, PL and INS sensors. The new filter structure distributes the computing tasks to different Kalman filters, leading to reduced filter complexities and improved system performance. IMU random errors are estimated separately by the pre-filter at a high data rate, whilst the main filter has a simplified structure, i.e. no estimation of the IMU random errors, and operates at a relatively slow rate. This paper will discuss the dynamic modelling method based on the Walsh function transform for implementing the pre-filter and the simplification of the main filter. Simulation tests were performed to compare the performance of the federated filter with that of the usual centralised Kalman filter in the estimation of the IMU random errors. The results show that with the simplification of the Kalman filter structure, the federated filter design can achieve the almost equally precise estimates as the centralised Kalman filter does but with less computational burden. Hence the federated design is more suitable for implementing the ultra-tight integration for real-time applications. Finally, the simulated high dynamic flight test results of ultra-tight integration based on the federated Kalman filter are presented.