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.
Highlights
Unmanned aerial vehicles (UAV) have played a vital role in military applications such as reconnaissance and surveillance missions since they allow access to places where conditions are harsh or inhospitable to humans
This study introduces a new particle filter to improve the robustness and navigation performance of Terrain referenced navigation (TRN) systems that utilize low-grade interferometric radar altimeter (IRA)
A one-step ant colony optimization (ACO) algorithm moves ants into the most desirable position for mutation. This new form of mutation achieves a smaller variance of importance weight than the auxiliary particle filter (APF) and immune auxiliary particle filter (IAPF)
Summary
Unmanned aerial vehicles (UAV) have played a vital role in military applications such as reconnaissance and surveillance missions since they allow access to places where conditions are harsh or inhospitable to humans. As indicated by the name, UAVs must possess the autonomous navigation ability to successfully carry out missions. The most popular navigation system for UAVs is a combination of the inertial navigation system (INS) and global navigation satellite system (GNSS) [1,2]. The INS can provide navigation information without external information, but the error accumulates over time. The GNSS provides precise navigation information, but the low-powered ranging signals make the system vulnerable to interferences like jamming and spoofing. This is a crucial disadvantage that may lead to mission failure. Alternative navigation technology is required to overcome this weakness. Terrain referenced navigation (TRN) has been suggested as a promising alternative since TRN does not require outsourced sensor information
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