本文中，我們提出了一兼具簡單及精確的演算法， cluster ESPRIT， 以同時估測行車的距離和角度。此演算法首先採用一維旋轉不變技術信號參數估測 (1D-ESPRIT)演算法估量時間延遲和距離，並根據第一階段所估得的不同時間延遲(距離)參數設計一組相對應的濾波器，此濾波器組除了可將不同距離的目標物之信號予以分離至不同的群外，並降低了雜訊。最後再度引入一維ESPRIT演算法於分群後的信號中以估量出各個目標物的角度。

在所提出的 cluster ESPRIT演算法中，我們利用了濾波分群的技巧克服了傳統上當多個目標物其具有相同的距離(或角度)以及接收天線根數少於目標物個數情況下無法解析的問題。另外， 因所提出的演算法每一目標物之參數估測階段均利用一維ESPRIT演算法，因此可大幅降低計算的複雜度。此外，所提出的演算法在無需增加運算量下即可達到同目標物其距離及其相應角度的自動配對。

我們藉由電腦數值模擬及實際場測以驗證所發展的高效率演算法，與其他文獻所提供的方法相較，結果顯示所發展的演算法在估量準確度及複雜度間取得較好的平衡點，且在不同的模擬/實測場景下均能獲致較佳的性能。

In this dissertation, we consider a simple, yet accurate approach for joint angle and range estimation of multiple targets in the frequency-modulated continuous-wave (FMCW) radar systems. Our new algorithm starts with the estimation of time delays and ranges by establishing a Hankel matrix based on the received signals. The estimation of signal parameter via rotational invariance techniques (ESPRIT)is then utilized for delay and range estimation. Afterward, according to the estimated time delays, we develop a group of filters to divide the received signals into different clusters. Finally, ESPRIT is invoked again to estimate the angles of the targets in each cluster. With such a signal clustering process, the parameter estimation accuracy can be boosted %with the mitigation of noise
and the complexity can be reduced as now the eigenvalue decomposition
(EVD) is with respect to smaller matrices. Additionally, the new algorithm possesses some advantageous features. First, the number of the receive antennas could be less than that of the targets, as the number of targets is fewer in each cluster after the clustering process. Second, it is still applicable even when some targets are located at the same ranges or angles. Third,
the estimated ranges and angles are automatically paired
together without extra efforts. Both of the numerical simulations and practical experiments are conducted to demonstrate the superiority of our algorithm over the state-of-the-art works in terms of accuracy in various scenarios.

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