專屬經濟海域(EEZ)的管理和安全維護皆為世界各國海洋事務執行機構的主要職責，因此妥善應用船舶監控科技，即時掌握船舶動態，不僅是海事管理的重要議題，也是近年來海洋科技的研究方向。面對來自海上威脅日益增高的挑戰，高頻雷達的超視距特性可克服地球曲率限制，得以有效掌握廣大海域的船舶動態；再透過雷達回波解算出船舶資訊，將可主動、即時、且快速地過濾或識別高危害風險之船舶。此一技術的開發可與現今海域監控系統結合，以達互補之效。臺灣現已建立環臺岸基高頻雷達測流系統網絡，現階段是以海洋表面流速量測為主要運作模式，且可進而推估其他海洋參數，例如海表面之風和浪的量測。在不影響現有流場量測前提下，開發船舶偵測與追踪技術可以增加測流高頻雷達的附加應用價值。
本研究期望達到三個主要目的：第一，對區域環境雜訊進行分析，進而了解其雜訊分佈及系統限制；第二為發展調適性偵測技術(ADT)，以建立一個可以隨環境雜訊分佈自動調整的門檻，從而提取高頻雷達都卜勒能譜中的船舶資訊；最後一個目的是利用上述資訊進行威脅評估程序，進而有助於相關指揮管制系統決策的下達。
本研究首先探討了高頻雷達環境場的複雜且不規則的雜訊分佈，以了解區域特點和系統限制。透過對環境雜訊時空變化的分析，將有助於偵測技術的開發。結果顯示，夜間的雜訊水平(noise level)顯著地比白天更高。透過雜訊水平時間序列的頻譜分析，可發現類似電離層的日變化規律性。此外，分析後發現，較佳的偵測時間區間是02:00至07:00 (UTC)，而較佳的偵測距離是從距離單元4至12。若船舶目標落入以上時空區間，將較容易被高頻雷達及偵測技術所識別。
調適性偵測技術(ADT)是利用二維移動平均濾波器來建立一個調適性的篩選門檻曲面，以過濾出可能的船舶訊號。萃取出的船舶回波訊號再利用船舶自動辨識系統(AIS) 提供的資料進行比對後，證實調適性偵測技術應用在高頻雷達偵測船舶的實用性。此外，濾波器的視窗選擇不再是一個權衡過程，而是可隨環境雜訊的時空特性進行最佳視窗的選定。
高頻雷達偵測出的船舶回波訊號如無法以AIS辨識的船舶可視為非合作目標，透過本研究所提出的威脅評估程序將可進行目標快速分類，進而計算可疑目標的威脅等級，以協助指揮管制系統採取適當的行動。

There is a growing need to deal with threats to national security coming from the ocean. Compared to conventional radar, high-frequency (HF) ground wave radar transmits low-power radar waves that can extend the detection distance up to several hundred kilometers with little attenuation. Moreover, HF radar has various advantages, such as active, continuous monitoring and near real-time performance. It is thus worth developing a vessel detection algorithm based on the HF radar system for large area surveillance.
The present study aims to achieve three objectives. The first is to analyze the local environmental noise to understand its distribution and effects. The second is to extract ship echoes from the HF radar system’s sea-returns by applying a simple procedure to the cross-spectra series of the signals of the SeaSonde HF radar system. An adaptive detection technique (ADT) is used to build a threshold surface which can be adaptive to the local environmental noise. In this work the ship information retrieved from the HF radar data was validated against the automatic identification system (AIS) data. The final aim is to explore the potential use of this system for maritime management, with a focus on threat evaluation of non-cooperative targets.
Since environmental noise may contain the complex irregular variations, this study first explores this noise to understand the regional characteristics and system limits. Appropriate observations in range and time intervals are derived from the spatial and temporal variations of the environmental noise, which can help develop a better detection scheme. The results show that the background noise levels during the nighttime are significantly higher than those during the daytime. Similar to the regularities of the ionosphere, the diurnal variations of time series can be derived by spectral analysis. Furthermore, the results of the environmental noise analysis show that the better time period and scope for vessel detection is from 02:00 to 07:00 (UTC) and the range cells from 4 to 12, within which the targets can be easily identified.
Based on ADT, a two-dimensional moving average filter is applied to build an adaptive threshold surface in the region of interest to extract ship motion data. Actual information for the vessels collected from the AIS database closely matched that derived from the HF radar sea echoes, which confirms the vessel signal detection capabilities of this HF radar. In addition, the main advantage of ADT is the decision process applied to find the optimal window, which may change based on the properties pof the environmental noise. The process of optimal window determination in ADT is not a tradeoff check, and this can be decided according to the spatial and temporal characteristics of the environmental noise.
Furthermore, the unidentified targets, which may be the non-cooperative ones, are determined by ADT and checked against the AIS. The proposed threat evaluation procedure is a preliminary idea which not only provides an initial and rapid classification of possible targets, but also helps the command system to take the appropriate actions using the related information. The threat levels can thus be computed to obtain the related information, which can be regarded as part of the decision support system to better understand the reaction time of the command system with regard to maritime security.

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