Ant-Mutated Immune Particle Filter Design for Terrain Referenced Navigation with Interferometric Radar Altimeter

Abstract

This study aims to design a robust particle filter using artificial intelligence algorithms to enhance estimation performance using a low-grade interferometric radar altimeter (IRA). Based on the synthetic aperture radar (SAR) interferometry technology, the IRA can extract three-dimensional ground coordinates with at least two antennas. However, some IRA uncertainties caused by geometric factors and IRA-inherent measurement errors have proven to be difficult to eliminate by signal processing. These uncertainties contaminate IRA outputs, crucially impacting the navigation performance of low-grade IRA sensors in particular. To deal with such uncertainties, an ant-mutated immune particle filter (AMIPF) is proposed. The proposed filter combines the ant colony optimization (ACO) algorithm with the immune auxiliary particle filter (IAPF) to bring individual mutation intensity. The immune system indicates the stochastic parameters of the ACO, which conducts the mutation process in one step for the purpose of computational efficiency. The ant mutation then moves particles into the most desirable position using parameters from the immune system to obtain optimal particle diversity. To verify the performance of the proposed filter, a terrain referenced navigation (TRN) simulation was conducted on an unmanned aerial vehicle (UAV). The Monte Carlo simulation results show that the proposed filter is not only more computationally efficient than the IAPF but also outperforms both the IAPF and the auxiliary particle filter (APF) in navigation performance and robustness.

Design of robust cubature fission particle filter algorithm in multi-source cooperative navigation

“…Although particle filtering has made great progress in recent years, particle degradation and the resulting sample impoverishment problem have always restricted the further development and application of the above-mentioned various improved PFs 12 – 18 . Particle degradation means that after multiple iterations, only a small number of particles in the particle swarm have large weights, while most of the particles have negligible weights, so that a lot of calculation time and resources are wasted on particles with small weights, which affects the performance of the algorithm 19 – 21 . Kong has proved that particle degradation is an inherent problem of PF 22 .…”