【摘要】：海上交通監(jiān)控對于在航船舶的航行安全具有重要意義。船舶AIS的強制安裝及沿海VTS的建立給海事監(jiān)管部門帶來極大的便捷,但海事部門對海上交通監(jiān)控的主要方式仍為人工監(jiān)控,該方法費時費力,且缺乏針對性,尤其在一些繁忙港口僅僅依靠人工監(jiān)控很難滿足港口安全需求。為了對船舶行為進(jìn)行實時監(jiān)控并自主發(fā)現(xiàn)異常行為船舶,提出了一種基于軌跡聚類的船舶異常行為識別模型。針對傳統(tǒng)Hausdorff距離在度量軌跡不等長及軌跡點丟失情況下存在度量距離加大的問題,優(yōu)化Hausdorff距離度量方式,將傳統(tǒng)度量最鄰近點距離改進(jìn)為度量軌跡點到最鄰近兩點所在直線的垂直距離,使船舶軌跡間距離度量更加準(zhǔn)確;將新提出的密度峰聚類算法應(yīng)用到航海領(lǐng)域并對船舶軌跡進(jìn)行聚類,該聚類方法不需要人為設(shè)置參數(shù),可以避免人為因素的干擾;設(shè)置掃描線對聚類后的每一簇船舶軌跡進(jìn)行掃描,獲得船舶典型軌跡模型;統(tǒng)計每條船舶軌跡與船舶典型軌跡模型的距離、航向和航速偏差,根據(jù)準(zhǔn)確率和誤報警率來確定最優(yōu)偏差閾值,若待測船舶偏差超過設(shè)置的閾值則識別為異常行為,從而達(dá)到智能識別出船舶異常行為的效果。通過對廈門港VTS監(jiān)控中心發(fā)現(xiàn)的船舶異常行為案例進(jìn)行驗證,實驗結(jié)果表明提出的算法模型能自主、有效識別船舶異常行為,并與值班人員相比能更早發(fā)現(xiàn)船舶行為發(fā)生異常,可為值班人員及早發(fā)現(xiàn)船舶異常行為提供參考依據(jù)。
[Abstract]:Maritime traffic monitoring plays an important role in the safety of navigation. The mandatory installation of ship AIS and the establishment of coastal VTS bring great convenience to maritime supervision department, but the main way of maritime traffic monitoring is manual monitoring, which is time-consuming and laborious, and lacks pertinence. Especially in some busy ports only rely on manual monitoring is difficult to meet port security needs. In order to monitor the ship's behavior in real time and find out the abnormal behavior of the ship independently, a ship abnormal behavior identification model based on trajectory clustering is proposed. Aiming at the problem that the traditional Hausdorff distance has the problem of increasing the measurement distance under the condition of the unequal length of the measuring path and the loss of the locus point, the Hausdorff distance measurement method is optimized. The distance between the most adjacent points is improved to measure the vertical distance between the trajectory points and the nearest two points, which makes the distance measurement of ship trajectory more accurate. The new density peak clustering algorithm is applied to the navigation field and the ship trajectory is clustered. This clustering method does not need to set the parameters artificially, so it can avoid the interference of human factors. Scanning lines are set to scan each cluster of ship trajectories after clustering, and the typical ship trajectory model is obtained, and the distance, course and speed deviation between each ship trajectory and the typical ship trajectory model are calculated. According to the accuracy and false alarm rate, the optimal deviation threshold is determined. If the deviation of the ship under test exceeds the set threshold, it will be recognized as abnormal behavior, so as to achieve the effect of intelligently recognizing the abnormal behavior of the ship. The experimental results show that the proposed algorithm model can identify the abnormal behaviors of ships independently and effectively, and can detect the abnormal behaviors of ships earlier than those of the watchmen, through the verification of the cases of abnormal behaviors of ships discovered by the VTS Monitoring and Control Center of Xiamen Port, and the experimental results show that the proposed algorithm can identify the abnormal behaviors of ships. It can provide reference basis for early detection of abnormal behavior of ship by duty personnel.
【學(xué)位授予單位】：集美大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：U698

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