Abstract
Gravity aided inertial navigation system (GAINS), which uses earth gravitational anomaly field for navigation, holds strong potential as an underwater navigation system. The gravity matching algorithm is one of the key factors in GAINS. Existing matching algorithms cannot guarantee the matching accuracy in the matching algorithms based gravity aided navigation when the initial errors are large. Evolutionary algorithms, which are mostly have the ability of global optimality and fast convergence, can be used to solve the gravity matching problem under large initial errors. However, simply applying evolutionary algorithms to GAINS may lead to false matching. Therefore, in order to deal with the underwater gravity matching problem, it is necessary to improve the traditional evolutionary algorithms. In this paper, an affine transformation based artificial bee colony (ABC) algorithm, which can greatly improve the positioning precision under large initial errors condition, is developed. The proposed algorithm introduces affine transformation to both initialization process and evolutionary process of ABC algorithm. The single-point matching strategy is replaced by the strategy of matching a sequence of several consecutive position vectors. In addition, several constraints are introduced to the process of evolution by using the output characteristics of the inertial navigation system (INS). Simulations based on the actual gravity anomaly base map have been performed for the validation of the proposed algorithm.
Highlights
It is well known that inertial navigation systems (INSs) typically used on underwater vehicles tend to develop accumulated errors
Gravity aided inertial navigation system can solve the problem of INS error accumulation and ensure the concealment of INS
We focus on the problem of how to ensure the matching accuracy under the large initial errors condition
Summary
It is well known that inertial navigation systems (INSs) typically used on underwater vehicles tend to develop accumulated errors. The mismatching of ICCP algorithm is included when the initial position errors of INS are large (Han et al, 2016). Gao et al proposed an improved artificial bee colony (ABC) algorithm and obtained a good performance on gravity matching under small initial errors (Gao et al, 2014). We consider a challenging gravity matching problem of achieving high precision under large initial errors. To solve this problem, we propose an affine transformation based ABC algorithm. In order to achieve high positioning accuracy under large initial errors, this paper proposes an affine transformation based ABC algorithm. The affine transformation satisfies the constraint conditions provided by the output characteristics of the INS
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