Abstract
Gravity matching navigation algorithm is one of the key technologies for gravity aided inertial navigation systems. With the development of intelligent algorithms, the powerful search ability of the Artificial Bee Colony (ABC) algorithm makes it possible to be applied to the gravity matching navigation field. However, existing search mechanisms of basic ABC algorithms cannot meet the need for high accuracy in gravity aided navigation. Firstly, proper modifications are proposed to improve the performance of the basic ABC algorithm. Secondly, a new search mechanism is presented in this paper which is based on an improved ABC algorithm using external speed information. At last, modified Hausdorff distance is introduced to screen the possible matching results. Both simulations and ocean experiments verify the feasibility of the method, and results show that the matching rate of the method is high enough to obtain a precise matching position.
Highlights
Gravity matching navigation is a kind of matching navigation technology aimed at obtaining the best matching position, in which the gravity data from the gravimeter and gravity reference database are imported into the computer for gravity matching solver
This paper proposes a new search mechanism, using external velocity information to restrain the matching points
Δg obs (k ) − Δg ( P k ) < σ g where P k -1 -P k is the distance between P k -1 and P k, ν is the external velocity information, T is the time interval for adjacent positions indicated by inertial navigation system (INS), Δgobs (k ) is the gravity anomaly outputted by gravimeter at k moment, Δg ( P ) is the gravity anomaly of search point P from the gravity reference k -1 k database (EGM2008) at k moment, σd and σg are the thresholds of distance and difference of gravity anomaly, respectively
Summary
Gravity matching navigation is a kind of matching navigation technology aimed at obtaining the best matching position, in which the gravity data from the gravimeter and gravity reference database are imported into the computer for gravity matching solver. The inertia factor and acceleration factor are introduced to the ABC algorithm to increase the convergence rate [16] Both the number of search parameters and the range of search steps are adjusted to improve search performance [17], in order to avoid the constraints of fixed-step search and increase the efficiency, especially for search objects with high-dimension parameters. These improvements are aimed at the search process for the entire bee colony, but for different search stages, the pressures of search and selection are different.
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