An Adaptive △M-ICCP Geomagnetic Matching Algorithm

Abstract

In this paper, a novel method is proposed to generate the matching sequence of an ICCP algorithm for aircraft geomagnetic-aided navigation based on the M coding principle. The length of the matching sequence and the selection of the matching points directly affects the performance of the Iterated Closest Contour Point (ICCP) algorithm. This study proposes an adaptive geomagnetic matching method, ΔM-ICCP, to solve the problem of selecting suitable matching lengths, and matching points, when a vehicle is moving in a highly dynamic environment. First, the △M coding principle is adopted to select the matching points based on the information of the magnetic field, the resolution of the magnetic map, and the accuracy of the magnetic sensor. Then, the problem of selecting parameters for the △M-ICCP algorithm is turned into an optimisation problem, which can be solved by a Binary Particle Swarm Optimisation (BPSO) algorithm. Finally, the algorithm is verified through simulation experiments. The proposed algorithm can provide a basis to determine the matching length of the △M-ICCP algorithm and adaptively adjust the algorithm's parameters according to different trajectories. The algorithm is applicable even in the areas where the fluctuations of Earth's magnetic field are not significant.

An Improved ICCP Matching Algorithm for use in an Interference Environment during Geomagnetic Navigation

“…It can be regarded as an optimisation problem to find the appropriate parameters for the algorithm. The optimisation process is discussed in detail in our previous work (Xiao et al, 2017).…”

“…First, the parameter of the M-ICCP algorithm was determined according to Xiao et al's (2017) method. The results show that the optimal solution of the planned path was: L = 6, δ = 2 nT, where L refers to matching length and δ is the quantisation step of the ICCP algorithm.…”

“…However, the computational time multiplies as the length of the matching sequence increases. Xiao et al (2017) introduced the delta modulation principle to the ICCP algorithm to find an optimal matching length without decreasing the accuracy. The anti-interference performance of the algorithm was improved.…”

Geomagnetic signal de-noising method based on improved empirical mode decomposition and morphological filtering

“…If the interference cannot be effectively eliminated, the accuracy of geomagnetic navigation will be seriously affected. 3–5 The hard magnetic and soft magnetic errors can be compensated and weakened by establishing a certain error compensation model. 6–8 The random interference magnetic field mainly includes random noise error caused by the surrounding environment, the measurement noise of the magnetic sensor itself, the high-frequency electronic interference caused by the operation of the electronic equipment on the carrier, the instantaneous pulse interference caused by the passing of the ferromagnetic object near the carrier.…”

An INS/Geomagnetic Integrated Navigation Algorithm Based on Matching Strategy and Hierarchical Filtering

“…It does not need to consider the distribution of measurement errors and has a certain anti-interference ability [18]. Xiao combined the probability data association (PDA) algorithm with the ICCP algorithm [19,20] and proposed a PDA-ICCP algorithm. It regarded all measurements of the magnetometer within a certain confidence range in interference environment as the effective measurements of a position, and finally evaluated the carrier's location with the PDA fusion algorithm.…”