Disclosed is a spatio-temporal DP method based on ship trajectory characteristic point extraction, which belongs to the technical field of ship trajectory compression and includes: Step 1: performing clustering analysis on AIS raw data using a clustering algorithm to identify outliers in the AIS data and then eliminate noise points; Step 2: identifying and retaining the characteristic trajectory points of the ship course change, ship speed change, and the ship entering and exiting from a certain area and the like; Step 3: compressing the AIS data by taking the start and end points of the ship trajectory and the characteristic trajectory points retained in step 2 as the initial points, and considering the spatio-temporal characteristics of the AIS data at the same time. The present disclosure can effectively compress redundant AIS data.


    Access

    Download


    Export, share and cite



    Title :

    SPATIO-TEMPORAL DP METHOD BASED ON SHIP TRAJECTORY CHARACTERISTIC POINT EXTRACTION


    Contributors:
    MA YONG (author) / JIANG HAIYANG (author) / YAN XINPING (author)

    Publication date :

    2023-04-20


    Type of media :

    Patent


    Type of material :

    Electronic Resource


    Language :

    English


    Classification :

    IPC:    B63B Schiffe oder sonstige Wasserfahrzeuge , SHIPS OR OTHER WATERBORNE VESSELS / G05D SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES , Systeme zum Steuern oder Regeln nichtelektrischer veränderlicher Größen



    Spatio-temporal DP method based on ship trajectory characteristic point extraction

    MA YONG / JIANG HAIYANG / YAN XINPING | European Patent Office | 2023

    Free access

    SHIP TRAJECTORY FEATURE POINT EXTRACTION-BASED SPATIO-TEMPORAL DP METHOD

    MA YONG / JIANG HAIYANG / YAN XINPING | European Patent Office | 2022

    Free access

    TrAISformer: Spatio-Temporal Ship Trajectory Prediction Based on Transformer

    Li, Yunbo / Wang, Jiayu / Li, Tao et al. | IEEE | 2024


    SaveDat: Spatio-Temporal Trajectory Compression by LSTM

    Horovitz, Shay / Cohen, Guy Yosef / Shmirer, Dan et al. | IEEE | 2022


    Detecting Taxi Trajectory Anomaly Based on Spatio-Temporal Relations

    Qian, Shiyou / Cheng, Bin / Cao, Jian et al. | IEEE | 2022