於分碼多重存取通訊系統使用適應性粒子群最佳化實現低計算負荷之到達方向估測

本論文於一個分碼多重存取(CDMA)系統中考慮到達方向(DOA)估測的問題，傳統的多重訊號分類(MUSIC)估測的搜尋複雜度與估測的精準度已經被證明與搜尋時的隔柵大小有關。這是相當耗時的，而且我們無法預先知道所需要隔柵大小為多少才是適當的。粒子群最佳化(PSO)演算法的計算效率高，並且不容易落入區域最佳解。此外，一個最重要的特徵是PSO演算法本身易於被實現。儘管有前述所提到的這些優勢，計算粒子的適應值仍會消耗大量的計算時間，並且有些PSO參數可能會大幅的影響結果。因此，如何減少PSO的計算複雜度是ㄧ個非常重要的問題。本論文所提的改良技術係針對幾個參數進行調整以使得PSO更有效率。首先，基於ULA的波束場型為固有的幅射對稱，亦即所有角度相對於水平軸都是對稱的特性，本論文提出一種粒子的搜尋位置映射技術，稱之為模數校正邏輯限制，來取代粒子位置剪裁的方式。第二，提出具有合理平衡的全域探勘能力與區域探勘能力的PSO適應性多重慣性權重，每個粒子的各個維度在每次運行的各次迭代中可以根據搜尋空間中自身的情況選擇合適的慣性權重。透過上述兩種所提方法，PSO的性能可以被改善。接著，我們應用所提的PSO演算法於有區域散射與天線元件位置擾動的環境，且由於所提的PSO方法可以同時估測天線元件的位置擾動誤差與訊號源入射角度，而不需要校正的訊號源。最後，一些模擬結果被提供用以證明所提技術的有效性。

關鍵字

粒子群最佳化；到達方向

並列摘要

This thesis deals with consider the problem of estimating the direction-of-arrival (DOA) for code-division multiple access (CDMA) system. It has been shown that the searching complexity and estimating accuracy of the conventional spectral searching multiple signal classification (MUSIC) estimator strictly depends on the number of search grids used during the search. It is time consuming and the required number of search grid is not clear to determine. The particle swarm optimization (PSO) method is computationally efficient and has great capability of escaping local optima. In addition, a key characteristic of PSO is that the algorithm itself is highly robust yet remarkably simple to implement. Although it has those advantages mentioned, it consumes a lot of computation time to compute the fitnesses of particles and some parameters in PSO may affect the solution significantly. Therefore, how to reduce the computational complexity of PSO algorithm is a very important issue. In this thesis, the PSO with the proposed techniques offers a much efficient option with few parameters to be adjusted. First, based on the inherent radiation symmetry of the ULA, all radiation patterns are symmetric about the axis of the linear array, this paper presents a particle search position mapping technique, which termed as correction logic constraint, to replace the particle position clipping. Second, an adaptive multiple inertia weight is proposed to rationally balance the global exploration and local exploitation abilities for PSO, at each iteration, during the run, every particle can choose appropriate inertia weight along every dimension of search space according to its own situation. By these two proposed methods, the performance of PSO could be improved. Next, we used the proposed PSO algorithm in the local scattering channel and sensor elements position perturbations environment. Because of this proposed PSO method has no requirement for calibration sources while the sensor position errors as well as the DOAs of the incident signals can be estimated simultaneously. Finally, several computer simulations are provided to demonstrate the effectiveness of the proposed techniques.

並列關鍵字

particle swarm optimization ； direction-of-arrival

參考文獻

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