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1887

access icon free RBPPFF for robust TAN

© The Institution of Engineering and Technology
Received 03/06/2019, Accepted 28/08/2019, Revised 18/08/2019, Published 17/09/2019

Terrain-aided navigation (TAN) is a technique for position estimation based on altimeter and digital elevation model measurements. To perform TAN, non-linear filters such as a particle filter (PF) or a point mass filter (PMF) are mainly used owing to the non-linearity of measurements and systems. In particular, owing to the degradation of the global positioning system or sensors and the non-linearity of maps and measurements, an initial state error or sudden state change may occur. The authors propose a Rao-Blackwellised particle-point mass fusion filter (RBPPFF) that performs robustly in the presence of such initial state errors or sudden state changes with low computational complexity in real time. The RBPPFF combines the advantages of a PF and PMF, and it employs Rao-Blackwellisation to efficiently increase the number of states. To develop the RBPPFF, the formulae of the RBPF and Rao-Blackwellised point mass fusion filter (RBPMF) are derived using the same conventions, and a method is presented for an effective fusion of the PF and the PMF. Finally, the performance of the proposed algorithm is demonstrated through Monte Carlo simulation across various environments with filters such as PF, PMF, RBPF, and RBPMF.

Inspec keywords: particle filtering (numerical methods); digital elevation models; aerospace navigation; navigation; altimeters; position measurement; Monte Carlo methods

Other keywords: global positioning system; Rao-Blackwellisation; robust TAN; RBPMF; low computational complexity; initial state error; position estimation; digital elevation model measurements; Rao-Blackwellised particle-point mass fusion filter; point mass filter; RBPPFF; particle filter; terrain-aided navigation; PMF; Rao-Blackwellised point mass fusion filter

Subjects: Monte Carlo methods; Aerospace instrumentation and equipment; Spatial variables measurement; Air traffic control and navigation; Filtering methods in signal processing; Other topics in aerospace

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