The empirical selection criterion of gravity matching area was given by using local gravity standard deviation and local gravity correlation coefficient as quantity indexes for selecting gravity-matching area.

In order to improve the locating precision and matching rate of the gravity aided inertial navigation system(INS) in regions with significant gravity anomaly characteristic,the pattern recognition neural network was used to investigate the problem of gravity matching.

Based on the principle and performance of integrated INS/Gravity-map matching navigation system, a gravity-map matching method based on RANSAC was presented to restrain the error increase of INS, which is very robust even if there are outliers in the gravity data.

Compared with the heavy-edge matching and sorted heavy-edge matching,light-vertex matching is certificated to have smaller edge cutting and better balance during the experiments of network graph partitioning.

Based on the idea of repeated matching and clustering arithmetic, the heuristic rules and heuristic algorithm for packing and scheduling problem are put forward.