粒子群最佳化演算法於天線元件擾動之最大相似性DOA 估測

Maximum Likelihood DOA Estimation Using Particle Swarm Optimization Under Sensor Perturbations

陳嘉易(Chia-Yi Chen)

指導教授 ： 張志忠

嶺東科技大學/資訊學院/資訊科技應用研究所/碩士(2013年)

https://doi.org/10.6823/LTU.2013.00047

摘要

對於存在天線元件擾動下的陣列校正，訊號來源方向和未知的陣列參數的估計是必要的。我們考慮分碼多重存取 (CDMA) 系統中存在天線元件陣列增益或位置擾動情形下之訊號到達方向 (DOA) 的估計問題，其估測的成本函數是一種參數估測 (最大相似(ML)、權重子空間擬合(WSF)) 函數的延伸。經證實 ML 與 WSF 函數是一種具高維度問題空間上的一個複雜非線性的多峰函數，此一函數原本是使用於一個完全校正的陣列作 DOA 估測。但對於陣列校正上，我們以改良式粒子群最佳化 (PSO) 來計算 ML (或WSF) 函數並找出函數的最佳解。本論文所提出的方法並不需要先校正訊號源，而可同時對天線元件擾動和訊號入射的 DOA 作估測。模擬結果顯示所提出之估測器能提供比其它的估測方法更好之估測性能。

關鍵字

粒子群最佳化 ； 到達方向估測 ； 分碼多重存取 ； 最大相似 ； 權重子空間擬合

並列摘要

In this study, we consider the problem of direction-of-arrival (DOA) estimation for code-division multiple access (CDMA) signals in the presence of array model perturbations. Due to changes in the surrounding environment, the response of the array and antenna position may be different from when they were calibrated before. For array calibration with sensor gain or sensor position errors, the estimation of the source directions and unknown array parameters are essential. The cost function is an extension of the maximum likelihood (ML) and weighted subspace fitting (WSF) criteria that were originally developed for DOA estimation with a perfectly calibrated array. It is shown that the ML function and WSF functions are a complicated nonlinear multimodal function over a high-dimensional problem space. An adaptive particle swarm optimization (APSO) is presented to compute the ML functions and find the global minimum cost function for array calibration. This presented method has no requirement for calibration sources while the array model errors, sensor gain or position errors, as well as the DOA of the incident signals can be estimated at the same time. Simulation results show that the proposed estimator has better performance over other popular methods.

並列關鍵字

direction-of-arrival (DOA) estimation ； code-division multiple access (CDMA) ； array model perturbations ； maximum likelihood (ML) ； weighted subspace fitting (WSF) ； particle swarm optimization (PSO)

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