Complex sea condition sea target positioning and tracking method with limited dependent variable constraint

By introducing a restricted dependent variable model and Bernoulli random variables, the problem of data loss or truncation in the maritime target positioning and tracking system under complex sea conditions is solved, and continuous high-precision target tracking is achieved, which is suitable for shipborne radar monitoring and maritime supervision platforms.

CN121500301APending Publication Date: 2026-02-10CHINA WATERBORNE TRANSPORT RES INST
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Patent Information

Application Number
CN202511927882.0
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-12-19
Publication Date
2026-02-10

AI Technical Summary

Technical Problem

Existing maritime target positioning and tracking systems suffer from trajectory breakage and missed detection due to loss or truncation of observation data in complex sea conditions, and existing methods lack effective handling mechanisms.

Method used

By introducing a restricted dependent variable model and constructing a nonlinear observation censoring model, and through state prediction and correction, combined with Bernoulli random variables to calculate the observation state adaptation weights, the correlation between observation restrictions and the true state of the target is quantified, thereby achieving effective processing of incomplete observation data.

Benefits of technology

It improves the accuracy and reliability of target positioning and tracking under complex sea conditions, avoids trajectory breakage, and enhances tracking stability and accuracy in environments with sea waves and low visibility.

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Abstract

A complex sea condition sea target positioning and tracking method constrained by dependent variables belongs to the technical field of intelligent maritime affair monitoring and ship navigation, and comprises the following steps: S1, constructing a sea target positioning and tracking state model including a state equation based on uniform linear motion and two observation equations adapted to AIS / GNSS and radar; s2, establishing a limited dependent variable observation model, and quantitatively observing nonlinear correlation between loss / truncation and the real state of the target; s3, performing prior prediction of the current target state and the covariance based on the historical state; s4, fusing the prediction state and actual observation (including censored data) to complete state and error covariance updating; and S5, introducing a Bernoulli random variable to calculate an observation state adaptive weight. Through combination of the limited dependent variable model and the filtering method, the problems of trajectory breakage and target missing detection of a traditional tracking method caused by incomplete observation data under complex sea conditions are solved, and the method is directly integrated to a shipborne radar monitoring system and a maritime affair supervision platform.
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