CN104794345A

CN104794345A - Flat type multi-hypothesis associative processing method during trajectory tracking

Info

Publication number: CN104794345A
Application number: CN201510189952.7A
Authority: CN
Inventors: 王运锋; 吴义忠
Original assignee: Sichuan University
Current assignee: Sichuan University
Priority date: 2015-04-21
Filing date: 2015-04-21
Publication date: 2015-07-22
Anticipated expiration: 2035-04-21
Also published as: CN104794345B

Abstract

The invention discloses an effective processing scheme for associating measurement reports of a sensor and a maintenance system trajectory in a multi-hypothesis mode under a complex environment. According to the scheme, a conventional multi-hypothesis associative tree-shaped structure is converted into a flat type structure, different weightings are given to all associative hypotheses, the complex degree of multi-hypothesis associative maintenance can be effectively reduced, and the associative accuracy can be effectively improved.

Description

Platypelloid type many hypothesis association process method in Track In Track

Technical field

The present invention relates generally to the process of complex scene target tracking domain data correlation, carrying out in complex scene multiple target tracking process especially, in the insurmountable situations of correlating method process such as the arest neighbors of dependence routine, universe arest neighbors, probability are interconnected, when adopting many hypothesis track associations.Usually during many hypothesis track associations, set up flight path hypothesis relevance tree, relevance tree there will be geometry speed increment in complex situations, safeguard that implementation process brings dyscalculia to association, The present invention gives the process of a kind of effectively many hypothesis track associations to improve one's methods, under the accuracy prerequisite effectively solving track association, reduce the complexity of many hypothesis association process, avoid the situation occurring relevance tree geometric growth.

Background technology

In existing all kinds of Information Handling System, aerial by various kinds of sensors detection, the moving target on ground, be that target sets up effective flight path maintenance system by information handling system, in processing procedure, owing to being subject to the reason such as all kinds of interference and sensor detection, realizing sensor target measurement report and safeguarding to there is multiple interrelational form between flight path, the difference of degree is responsible for for observation scene, common measurement report comprises arest neighbors with the correlating method safeguarded between flight path and associates, universe arest neighbors associates, suppose association more, probabilistic data association etc., the scene that often kind of association algorithm has it to adapt to and relative merits.The process of conventional many hypothesis associations to be positioned at multiple measurement reports of the prediction association door safeguarding flight path all as the affiliated partner that this flight path is possible, set up the relevance assumption of one-to-many, and in the follow-up measurement report cycle, each relevance assumption there will be again multiple relevance assumption, under complex scene, there will be many hypothesis relevance tree that geometry expands very soon, as shown in Figure 1.

The correlation threshold that 3 measurement reports are positioned at this flight path is carved with during T2, according to supposing correlation rule more, set up 3 associations to build, in figure, dotted line mark is connected to the situation of measurement report 1,2,3, during to the T3 moment, to above-mentioned 3 hypothesis associations, there are again multiple relevance assumptions of measurement report 4-10 respectively, namely through two moment of T3, T4, just occurred 7 hypothesis associations, in routes planning and maintenance, complexity is very large.And in actual treatment; usual somebody adopts the situation of restriction hypothesis tree excessively rapid growth; i.e. restricted T 3 moment three hypothesis spread scenarios; only allow to retain original hypothesis in the T3 moment; this restriction is only reduction of the complexity of maintenance; but to the accuracy aspect of track association, cause certain loss, likely occur that association situation occurs by mistake.

The tree-like relational structure of many hypothesis that the present invention is directed to this geometric growth shown in Fig. 1 improves, many hypothesis correlating method that a kind of platypelloid type is safeguarded is proposed, and to each hypothesis according to follow-up hypothesis spread scenarios, set different weights, can carry out according to the size of weights the selection supposed in subsequent treatment, when ensureing association accuracy, effectively reduce the complexity of many hypothesis associations.

Summary of the invention

The present invention relates generally to complex scene lower sensor measurement report and safeguards that flight path carries out supposing in association process more, to suppose that relevance tree is adjusted to the improvement disposal route of platypelloid type maintenance more, and give different process weights to each hypothesis, thus realize the method determining final association results, be mainly reflected in following four steps.

(1) will suppose to be converted to the tree structure associated platypelloid type enclosed structure, to analyze with the actual examples reference run into, process as shown in Figure 2, effectively reduces the complexity that tree structure is safeguarded more.

(2) for the hypothesis in each platypelloid type structure gives process weights, assignment method is as follows:

The initial weight of each hypothesis association is set to 0;

Next sense cycle, when there being the measurement report of associated (no matter single or multiple suppose association) more, weight adds a(as value a=2), and many hypothesis associated weights that its direct subsequent cycle derives from is added 2a(non-immediate derive from do not change), by that analogy; When it does not have measurement report associated, its weight is subtracted 5a, by that analogy.

(3) in platypelloid type structure, only retain 2 of maximum weight, remaining is deleted, because these hypothesis comprise two classes, the first has belonged to out-of-date hypothesis, and it two is that the interruption belonged to without subsequent association is supposed.

(4) select according to the weights of platypelloid type structure, the hypothesis that namely right to choose is great as output, when maximum weights are not exclusive, a conduct can be selected wherein to export in real time, in platypelloid type classification, only safeguard two hypothesis of maximum weight, treat that the follow-up multicycle continues checking.

Accompanying drawing explanation

Fig. 1 is many hypothesis association tree structures;

Fig. 2 is many hypothesis association platypelloid type structure weights assignment and selects schematic diagram.

Embodiment

(1) software design and development environment

The complex environment targetpath that the present invention relates to follow the tracks of in measurement report point mark and flight path many hypothesis relating design and development environment as follows:

Operating system: Windows XP and above version, Linux, VxWorks etc.;

Software translating environment: C/C++ compiler.

(2) measurement report-flight path many hypothesis associations implementing procedure

This software, in implementation process, comprises following four steps.

The first step:

Calculate the sensor measurement report being positioned at system maintenance Trajectory Prediction correlation threshold scope, when there is multiple measurement report, set up multiple hypotheis tracking and safeguard list (in figure t2 moment), and select one as real-time output, and the flight path in all hypothesis linked lists is safeguarded, distribute initial weight to each hypothesis, calculate its predicted position, be convenient to carry out interactive calculation next measuring period.

Second step:

When the next measurement report cycle arrives, according to the predicted position of all hypothesis of background maintenance, detect the measurement report being positioned at each prediction correlation threshold, according to the weight computing rule of the present invention's design, calculate the weights of each hypothesis.

3rd step:

Select the hypothesis of maximum weight, as real-time output, when there being multiple maximum weights, optionally wherein; Simultaneously only two kinds of hypothesis of maximum weight in maintained hypothesis list, delete the hypothesis that other weights are less; And subsequent time status predication is carried out to the relevance assumption safeguarded.

4th step:

Repeat second and third step above-mentioned, automatically can repair many hypothesis associations and follow the tracks of.

Claims

1. complex scene lower sensor measurement report and flight path carry out the design proposal that platypelloid type (non-tree-like) is supposed to associate more, based on C/C++ compiler language, in kinds of platform application such as Windows, Linux, Vxworks, mainly comprise:

(1) routine is supposed the tree structure associated more, be converted to platypelloid type enclosed structure, reduce the complexity that tree structure is safeguarded;

(2) for process weights are given in the hypothesis association in each platypelloid type structure, assignment method is as follows:

The initial weight of each hypothesis association is set to 0;

Next sense cycle, when have to suppose the measurement report associated with the upper cycle time (no matter single or multiple suppose to associate) more, weights add a(as value a=2), and many hypothesis associated weight value that its direct subsequent cycle derives from is added 2a(non-immediate derive from do not change), by that analogy; When it does not have measurement report associated, its weights are subtracted 5a, by that analogy;

(3) in platypelloid type structure, only retain 2 of maximum weight, remaining is deleted, because these hypothesis comprise two classes, the first has belonged to out-of-date hypothesis, and it two is that the interruption belonged to without subsequent association is supposed;

(4) select according to the weights of platypelloid type structure, namely the hypothesis selecting weight maximum is as output, when maximum weights are not exclusive, optionally can select a conduct wherein to export in real time, in platypelloid type classification, only safeguard two hypothesis of maximum weight, treat that the follow-up multicycle continues checking.