近年來隨著無線技術的發展，對於定位服務的需求也急遽上升。全球衛星定位系統(GPS) 是常見的無線定位技術之一，但是當接收機放在室內時，定位效果會不如預期，主要原因是環境從通視變成非通視的環境以及較大的訊號能量衰減造成可以協助定位的衛星數量減少。本論文研究結合時間到達定位法和訊號到達角度定位法，並且在未知通視或非通視的情況下探討定位效果。根據測量到的距離資訊與角度資訊，可以推導出最大似然估計器，但這是一個非凸優化的問題，不易求解，我們根據半正定規劃將非凸優化的問題轉成凸優化問題，使求解可以更有效率。為了對抗非通視的影響，提出的半正定規劃估計器中，針對每一個路徑資訊設計出不同的權重，以提升定位效果。最後，將提出的半正定規劃估計器探討在不同的網路配置下的效能，並且應用在蜂巢式網路，模擬結果顯示，所提出的方法可有效提升非通視環境的定位精確度。

With the rapid development of wireless technologies, the demand for positioning services has grown dramatically over the past years. Global Positioning System (GPS) is widely used in wireless devices for positioning. However, GPS receiver can not operate in the indoor because of the existence of Non-Line of Sight (NLOS) and the absence of available satellites. In this work, we investigate the hybrid localization technique based on Time of Arrival (TOA) and Angle of Arrival (AOA) information. We assume that the estimator does not have any prior knowledge of path propagation type, i.e., either LOS or NLOS. Based on the range and angle measurements, we derive the Maximum Likelihood (ML) estimator in this work, which is a non convex optimization problem. We show that the ML estimator can be transformed into a convex problem by applying the Semide nite Programming (SDP) relaxation technique, which can be solved effciently. To be robust in the NLOS environment, the proposed SDP estimator treats each measured data with different importance by assigning a weight to each path. Extensive simulations are performed to examine the performance of the proposed SDP estimator in numerous network con gurations. A case study is also carried out to demonstrate the usefulness of the proposed SDP estimator in cellular networks.

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