在本篇論文中，我們考慮雙基地多輸入多輸出雷達之起始方位角與到達方位角的問題，我們發展出兩個演算法，一個低維度之基於旋轉不變信號參數估測和一個高效能之基於最大似然法則的多維度參數估測演算法。基於嵌套式架構，這兩個演算法，從粗略到精細反覆的方式作參數估測，同時藉由訊號分離過程中所設計的濾波處理對方位角相近或相同的信號做適當的分群已提高參數估測精確度，此分群方法不僅能精確地估測相近的參數，同時在每一階層只需要使用一維參數演算法來進行估測，因此能大幅降低計算複雜度。為了有效執行最大似然估計，使用了Pincus理論和蒙地卡羅方法之重要取樣來求得最大似然法之全域最佳解。除此之外，這兩個演算法中的受估測參數不需要額外的運算負擔就可以自動達成配對。最後我們應用大量的電腦模擬與其他文獻所提出的方法相比，模擬結果顯示了我們所提出的演算法在估測精確度與複雜度之間取得一個較好的平衡點。

This thesis presents two nested algorithms - one based on the Estimation of Signal Parameters via Rotational Invariance Technique (ESPRIT) algorithm and the other one based on the maximum likelihood estimation (MLE) - for joint direction of departure (DOD) and direction of arrival (DOA) estimation in bistatic multiple-input multiple-output (MIMO) radar. Both nested algorithms estimate the parameters in a rough to fine way with a signal separation process in between.The signal separation process, implemented by a set of properly designed filters, can separate the signals into appropriate groups, so that only one-dimensional (1-D) parameter estimation algorithms are required in each stage and the DODs and DOAs can be precisely estimated even some of these parameters are very close. To facilitate implementation of the ML estimation,the theorem of Pincus and a Monte Carlo method known as importance sampling (IS) are employed to determine the global optimum ML solution. Moreover, the pairing of these two parameters are automatically achieved without extra computational overhead.
Simulation results show that both of the proposed nested algorithms strike a good tradeoff between estimation accuracy and complexity compared with the main state-of-the-art works.

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