Contours-Seeking Proposal Density Particle Filter and Resilient Terrain-Referenced Navigation

Abstract

Auxiliary navigation systems are essential for the robust operation of aerial vehicles, particularly in self-contained frameworks like terrain-referenced navigation. However, challenges such as multimodal likelihoods, highly nonlinear terrain elevations, and unknown prediction biases result in highly multimodal and less predictable posterior distributions, leading to particle filter degeneration. This study addresses the numerical instability and degeneration of the particle filter approach by proposing a sampling strategy tailored to this problem. The approach introduces a Gaussian mixture random forcing mechanism, which nudges particles along terrain slopes and against biases towards the most probable terrain contours. Each mixture is associated with a mode of likelihood, enhancing adaptability to unmodeled terrain features. To further improve effectiveness, auxiliary sampling selectively applies this mixture sampling to probable particles, yielding a less degenerate and evenly weighted particle set. Numerical experiments demonstrate the effectiveness of the proposed method in reducing weight variance, improving effective sample size. In addition, the approach exhibits strong resilience under deteriorating scenarios, such as severe unknown prediction bias and multimodal measurement noise, ensuring long-term reliable particle filtering.