在本論文中，我們發展了兩個階層式的演算法—Hierarchical DFT-ESPRIT與Hierarchical ESPRIT。這兩個演算法在估量距離方面可分為離散傅立業轉換以及子空間演算法。兩個演算法皆須藉由到達時間(距離)的資訊而設計投影矩陣，利用投影矩陣可將信號做適當的分群，以提高估量的準確度。在角度估量上則是將分群後的信號進行一維ESPRIT來估量參數，以此克服接收天線個數的問題，透過少於目標物之接收天線個數估量出多目標物之距離以及角度，且在每一階段皆使用一維參數估測演算法來進行估量到達時間(距離)與到達方位角，藉此能大幅降低演算法之複雜度。除此之外這兩個演算法所估量的參數無須額外運算即可達到自動配對，最後我們將利用電腦模擬與其他文獻所提供的方法進行比較。模擬結果顯示，我們的演算法在估量準確度以及複雜度之間取得較好的平衡點，也可以從距離與到達方位角點圖上觀察到，我們提出的兩個演算法在不同模擬場景下都保有很好的性能以達到實際上的應用。

This thesis presents two accurate, yet low-cost algorithms for joint distance and angle estimation of the targets in the Frequency- modulated continuous-wave (FMCW) radar systems. Both algorithms begin with estimation of time delays and distance - one uses the discrete Fourier transform while the other uses the renowned subspace algorithm, estimation of signal parameters via rotational invariance techniques (ESPRIT). Afterward, based on the estimated time delays, we establish a set of projection matrices to separate the received signals into appropriate groups. Finally, ESPRIT is invoked again to estimate the angles of the targets. With such a signal separation process, there is in general only one target in each group, so the subspace algorithms are applicable even if the number of receive antennas is less than that of the targets. Moreover, these two parameters can be precisely estimated with lower computational overhead, as only one parameter needed to be estimated in each stage. The pairing of the estimated distance and angles is also automatically achieved without extra overhead. Simulations show that both algorithms can provide satisfactory estimation accuracy in various scenarios and strike a better trade-off between accuracy and complexity as compared with the state-of-the art works.

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