Kalman Filter-Based RAIM for Reliable Aircraft Positioning with GPS and NavIC Constellations

Abstract

This paper presents a novel Kalman filter (KF)-based receiver autonomous integrity monitoring (RAIM) algorithm for reliable aircraft positioning with global navigation satellite systems (GNSS). The presented method overcomes major limitations of the authors’ previous work, and uses two GNSS, namely, Navigation with Indian Constellation (NavIC) of India and the Global Positioning System (GPS). The algorithm is developed in the range domain and compared with two existing approaches—one each for the weighted least squares navigation filter and KF. Extensive simulations were carried out for an unmanned aircraft flight path over the Indian sub-continent for validation of the new approach. Although both existing methods outperform the new one, the work is significant for the following reasons. KF is an integral part of advanced navigation systems that can address frequent loss of GNSS signals (e.g., vector tracking and multi-sensor integration). Developing KF RAIM algorithms is essential to ensuring their reliability. KF solution separation (or position domain) RAIM offers good performance at the cost of high computational load. Presented range domain KF RAIM, on the other hand, offers satisfactory performance to a certain extent, eliminating a major issue of growing position error bounds over time. It requires moderate computational resources, and hence, shows promise for real-time implementations in avionics. Simulation results also indicate that addition of NavIC alongside GPS can substantially improve RAIM performance, particularly in poor geometries.

Performance Analyses of a RAIM Algorithm for Kalman Filter with GPS and NavIC Constellations

“…Both fault detection test statistics and PLs of KF RAIM presented in [16] are briefly discussed in this section for ease of understanding of the subsequent developments.…”

“…The limitation of the above formulation is that measurement errors across time epochs are assumed to be uncorrelated, which is a gross simplification. To overcome this limitation, [16] assumes the following error models. Broadcast satellite orbit and clock error, residual tropospheric error, receiver noise, and multipath are together represented as a first order Gauss Markov (FOGM) random process with a 100 s time constant (carrier smoothing interval of code).…”

“…This paper attempts to explore range domain methods further for their relatively low architectural complexity. To this end, it extends the work in [16], which uses an SKF [24] navigation processor and carrier-smoothed pseudoranges for absolute positioning. SKF accounts for measurement errors as "consider parameters" without estimating them.…”

“…It is shown to reduce the computational load to a great extent and provide global coverage for CAT I precision approaches. Reference [16] designs a computationally efficient KF RAIM algorithm for GNSS receivers. It checks measurement inconsistencies in the range domain, models time correlated errors, and requires Schmidt KF (SKF) as the navigation processor.…”

“…Five of the aforementioned references discuss KF-based PL calculations. Among them, [10,15] perform fault detection in the position domain, [16,17] follow range domain methods, and [20] assumes that fault detection and mitigation have been executed. This paper attempts to explore range domain methods further for their relatively low architectural complexity.…”