Similar track recognition method for classic tracks

Abstract

The invention discloses a similar track recognition method for classic tracks and aims at providing a classic track recognition method which has a high similar track recognition rate and can process an instable track. The technical scheme comprises steps: a classic track is read from a classic track knowledge base, a real-time track is then read from a real-time track base, a Douglas-Peucker algorithm is adopted to compress the real-time track, track features are used for track similarity initial judgment, initial judgment succeeds, the distance between a point of the classic track and a linesection of the real-time track is used to calculate the longest common substring distance of multiple one pairs, the longest common substring distance of multiple one pairs is used as the longest common substring distance of multiple one pairs for a point to a line between the classic track and the real-time track, the ratio of the longest common substring distance of multiple one pairs for a point to a line to the length of the classic track is used as a track similarity, track similarity precise judgment is then carried out according to the track similarity, and if the track similarity precise judgment succeeds, a result is outputted.

Description

Technical field [0001] The invention belongs to the field of pattern recognition, and relates to classic track recognition technology in the field of intelligent intelligence and intelligence big data. Background technique [0002] The classic trajectory is the movement trajectory of a classic target in the field of technical reconnaissance and intelligence. The classic targets have regular activities each time they travel, and their trajectories are relatively stable. The typical trajectory of this classic target is the classic trajectory. Classical trajectory plays a very important role in the analysis of target recognition, target warning, and target behavior intention. The similar track recognition of the classic track is to identify the similar track of the real-time track in the classic track library. In actual situations, the real-time track obtained is very unstable, which is mainly reflected in: [0003] 1) Obtaining the track is not continuous, it is easy to miss and ...

AI Technical Summary

Problems solved by technology

[0009] 3) The calculation of the longest common substring distance is time-consuming, and the algorithm needs to be optimized to reduce the amount of calculation

[0010] At present, when the longest common substring distance is applied to asynchronous and non-isoperiodic track recognition, the main solution is to convert the comparison between points into line segment and line segment comparison, although it solves the problem of real-time track and classic track sampling The cycle is not synchronized, but it cannot solve the problem that the classic track and the real-time track are not equal, causing multiple line segments or points of the classic track to match a line segment of the real-time track, and it does not solve the problem that the longest common substring distance is used for the track. The time-consuming nature of track recognition; there is another solution to the time-consuming track. This method forms a multi-layer grid on the plane where the point is located, and replaces each grid with a character to form a multi-layer character string recognition structure. Fast, but this method cannot solve the identification of asynchronous and non-isoperiodic tracks

Images