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Technical Analysis and Implementation Cost Assessment of Sigma-Point Kalman Filtering and Particle Filtering in Autonomous Navigation Systems
Abstract
The chapter provides technical analysis and implementation cost assessment of Sigma-Point Kalman Filtering and Particle Filtering in autonomous navigation systems. As a case study, the sensor fusion-based navigation of an unmanned aerial vehicle (UAV) is examined. The UAV tracks a desirable flight trajectory by fusing measurements coming from its Inertial Measurement Unit (IMU) and measurements which are received from a satellite or ground-based positioning system (e.g. GPS or radar). The estimation of the UAV’s state vector is performed with the use of (i) Sigma-Point Kalman Filtering (SPKF), (ii) Particle Filtering (PF). Trajectory tracking is succeeded by a nonlinear controller which is derived according to flatness-based control theory and which uses the UAV’s state vector estimated through filtering. The performance of the autonomous navigation system which is based on the aforementioned state estimation methods is evaluated through simulation tests. Implementation cost assessment shows that PF requires more sample points than SPKF to approximate the state distribution. Therefore PF is a computationally more demanding method which needs more costly computing machines. However, the PF is a nonparametric filter which can be applied to any kind of state distribution, while the SPKF state estimators are still based on the assumption of a Gaussian process and measurement noise.
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