Distributed multi-platform underwater multi-target association and passive positioning method

A passive positioning and target association technology, applied in advanced technology, climate sustainability, complex mathematical operations, etc., can solve the problems of low positioning accuracy and low accuracy rate of multi-target association, and achieve the effect of excellent positioning accuracy

Active Publication Date: 2022-06-03
HARBIN ENG UNIV +1
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] The purpose of the present invention is to solve the problems of low positioning accuracy and low accuracy of multi-target association in the existing maneuvering target positioning and tracking method, and propose a distributed multi-platform underwater multi-target association and passive positioning method

Method used

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  • Distributed multi-platform underwater multi-target association and passive positioning method
  • Distributed multi-platform underwater multi-target association and passive positioning method
  • Distributed multi-platform underwater multi-target association and passive positioning method

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specific Embodiment approach 1

[0094] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS 1. A distributed multi-platform underwater multi-target association and passive positioning method described in this embodiment specifically includes the following steps:

[0095] Step 1. Carry out target association according to the information vector estimation result of each target under the multi-platform, and obtain the target association result;

[0096] Step 2: Construct a TMA model composed of state equation and measurement equation;

[0097] Step 3: Based on the target association result in Step 1 and the TMA model in Step 2, use the forgetting factor-based fading and strong tracking filter to passively locate the azimuth-only maneuvering target under multiple platforms, and finally output each target. The positioning result of the target (including information such as position, speed, etc.).

[0098] The multi-platform in the present invention refers to two or more platforms, and the multi-target refers to two...

specific Embodiment approach 2

[0099] Embodiment 2: The difference between this embodiment and Embodiment 1 is that in Step 1, a target association algorithm based on frequency feature multi-information vector fusion is used, and the specific process of Step 1 is:

[0100] Step 11. Input the estimation result of the target information vector under the multi-platform:

[0101] I mp (k)={θ mp (k),A mp (k),F mp(k),k=1,2,...,T},p=1,2,...,P,m=1,2,...,M

[0102] The target frequency feature information vector estimation result is extracted from the information vector estimation result of each target under multiple platforms:

[0103] J mp (k)={A mp (k),F mp (k),k=1,2,…,T} (1)

[0104] Among them, p represents the p-th target, p∈{1,2,…,P}, P is the total number of targets, m is the m-th platform, m∈{1,2,…,M}, M is the total number of platforms, k represents the sampling time k, T represents the total sampling time, A mp (k) represents the energy information of the p-th target observed by the m-th platfor...

specific Embodiment approach 3

[0121] Embodiment 3: The difference between this embodiment and Embodiment 1 or 2 is that the λ F (t k′ ) corresponding to the associated confidence value η F (t k′ )for:

[0122]

[0123] Among them, a 1 and a 2 is a constant;

[0124] the λ A (t k′ ) corresponding to the associated confidence value η A (t k′ )for:

[0125]

[0126] where b 1 and b 2 is a constant.

[0127] In this embodiment, the established test statistic rating standards are shown in Table 1 and Table 2:

[0128] Table 1 Rating standard of frequency test statistic

[0129]

[0130] Table 2 Rating criteria for energy test statistics

[0131]

[0132]

[0133] Other steps and parameters are the same as in the first or second embodiment.

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Abstract

The invention discloses a distributed multi-platform underwater multi-target association and passive positioning method, and belongs to the technical field of distributed multi-platform underwater multi-target positioning and tracking. The problems that an existing maneuvering target positioning and tracking method is low in positioning precision and low in accuracy of multi-target association are solved. According to the method, the target line spectrum feature set is introduced into the target association method, the target association algorithm based on frequency feature multi-information vector fusion is provided, the problems that a pure azimuth system cannot obtain the track and track association is difficult are solved, and the method can give a correct association result. Compared with a traditional nonlinear Bayesian filtering algorithm, the passive target positioning algorithm provided by the invention introduces a forgetting factor to construct a fading volume Kalman filtering algorithm, and forms a strong tracking filter in combination with a least square positioning algorithm. The filter does not need to give a target iteration initial value, and can passively position the maneuvering target only by using a CV tracking model. The method can be applied to multi-platform underwater multi-target association and passive positioning.

Description

technical field [0001] The invention belongs to the technical field of distributed multi-platform underwater multi-target positioning and tracking, in particular to a distributed multi-platform underwater multi-target association and passive positioning method. Background technique [0002] For the same target, due to the measurement error of the platform itself and the existence of environmental noise interference, the measurement information observed by multiple platforms is not exactly the same, but there must be some similar characteristics, such as track and frequency characteristics. How to use these similar but not identical features to determine whether they come from the same target is the target association. At present, underwater multi-target correlation mainly faces the following difficulties: due to the objective existence of marine environmental noise, the limitation of the platform's own observation ability and the lack of understanding of the target's prior i...

Claims

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Application Information

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IPC IPC(8): G01C21/20H03H17/02G06F17/16G06F17/18
CPCG01C21/20H03H17/0257G06F17/16G06F17/18Y02D30/70
Inventor 孙大军张艺翱滕婷婷兰华林肖龙腾
Owner HARBIN ENG UNIV
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