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.

Although interferometric radar altimeter (IRA) provides accurate three‐dimensional terrain information, it is difficult to eliminate some IRA uncertainties by signal processing. This calls for a filter that possesses strong robustness against outliers. An auxiliary particle filter (APF), which can be used to perform terrain referenced navigation using IRA technology as an alternative to an integrated navigation system using a global navigation satellite system, can mitigate the effect of outliers by introducing auxiliary variables. To improve APF robustness, the authors propose an immune APF, which introduces an artificial immune system to enhance outlier performance by replacing inferior particles with superior particles. The mutation process is applied to obtain particle diversity using a regularised kernel. Monte Carlo simulation results verify the robustness of the proposed filter.