臺灣博碩士論文加值系統

對於遠距離的目標偵測來說，可能會受到天氣、光線、溫度等因素影響，雷達是現今最普遍的遠距離探索方式，它可以發射出較遠的探測電波，透過與物體的反射電波來計算出該物體的位置高度和方向，並且具有一定的穿透能力，不會受到雲、霧、雨的阻擋。
對於雜波、干擾、高速與高機動目標的環境下，搜索雷達如何提高目標追蹤品質是很重要的議題，尤其在低更新率的雷達條件限制下更容易造成雷達偵追效能不佳，因此如何能在低更新率的雷達條件下達到更高的雷達偵追效能是本論文的主要目的。
本研究將探討兩種廣泛應用於具有偵測和追蹤功能雷達的多目標追蹤法則， Multiple Hypothesis Tracking (MHT)和Joint Probability Data Association (JPDA)，我們在Matlab中模擬5秒旋轉一次的陸基雷達和多個飛行物體交錯飛行的場景，檢驗追蹤法則追蹤多重目標的穩定度。之後則使用真實資料進行兩種追蹤法則的比較。為了改善track-oriented multiple hypothesis tracking (TOMHT)計算全域假設的時間，我們導入了Greedy Randomized Adaptive Search Procedure (GRASP)和Max Product Belief Propagation (MPBP)兩種算法來加速與優化Maximum Weight Independent Set Problem (MWISP)，並呈現其追蹤效果。

For long-distance target detection, it may be affected by factors such as weather, light, temperature, etc. Radar is the most common long-distance exploration method today. It can emit long-distance detection radio waves, calculate the position, height and direction of the object through the reflected radio wave transmission of the object. And has a certain penetration ability, will not be blocked by fog, clouds, rain.
For the environment with clutter, interference, high speed and high maneuvering targets, how to improve the quality of target tracking by search radar is a very important issue, especially under the limitation of low-update-rate radar conditions, it is more likely to cause poor radar tracking performance. The main purpose of our research is to achieve higher radar reconnaissance and tracking performance under the condition of low-update-rate.
We will investigate two multi-target tracking algorithms that are widely used in radars with detection and tracking capabilities. One is Multiple Hypothesis Tracking (MHT), and the other is Joint Probability Data Association (JPDA). In Matlab environment we simulated a ground-based radar that rotates once every 5 seconds while multiple objects are flying interlaced. Such simulation can be used to test the stability of the tracking performance. In the follow-up study, the real data is used to compare the two multi-target tracking algorithms. To improve the time required for track-oriented multiple hypothesis tracking (TOMHT) to calculate the global hypothesis, we introduced two algorithms, Greedy Randomized Adaptive Search Procedure (GRASP) and Max Product Belief Propagation (MPBP), to accelerate solving the Maximum Weight Independent Set Problem (MWISP) and present their tracking performance.

目錄 vii
圖目錄 ix
表目錄 xiii
第一章 緒論 1
1.1 研究背景 1
1.2 研究動機與目的 1
1.3 本文貢獻 2
1.4 論文大綱 3
第二章 相關文獻探討 5
2.1 聯合機率資料關聯 5
2.2 多目標追蹤假設 5
2.3 交互多模型 7
2.3.1 擴展卡爾曼濾波器 8
2.3.2 無損卡爾曼濾波器 8
第三章 實驗與分析方法 9
3.1 實驗流程 9
3.2 資料前處理 10
3.3 trackerTOMHT 11
3.4 trackerJPDA 11
3.5 使用貪婪隨機自適應搜索計算出全域假設 12
3.6 使用最大乘積信心傳播計算出全域假設 16
3.7 Measurement Noise 18
3.8 Detection Probability和False Alarm Rate 19
3.9 IMM Filter 20
第四章 實驗結果 21
4.1 比較TOMHT和JPDA追蹤效能：模擬資料 23
4.2 比較TOMHT和JPDA追蹤效能：真實資料 24
4.3 加入GRASP和MPBP的追蹤效能差異 26
4.4 不同的Measurement Noise設定 27
4.5 不同的Detection Probability和False Alarm Rate設定 28
4.6 調整IMM Filter做比較 36
4.7 其他真實資料之追蹤情況 41
第五章 結論 46
參考文獻 47

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