比較預先量測方法與模糊算法用於非線性追蹤

Comparison Study on Measurement Preprocessing & Fuzzy-based methods for Nonlinear Tracking

湯竣富(Schmakartney Tang)

指導教授 ： 何天讚

中原大學/工學院/電機工程研究所/碩士(2011年)

https://doi.org/10.6840/cycu201100647

摘要

本論文主要目的是研究比較預先處理量測算法與模糊算法用於非線性目標物追蹤，使用非線性測量方程式結合於卡爾曼濾波器。預先處理量測是以最大概似理論為基礎，推導估測的位置、速度與加速度，減少量測值的不確定性，以改善追蹤性能。我們考慮目標是在極座標上測量，再以轉換成直角座系統，之後經過線性化的量測運用在預先處理方法。此外，我們還結合了多維濾波器於預先處理算法與模糊算法。最後進行模擬與評估性能來驗證提出的方法結果比較(考慮較小的量測方位誤差情況)。

關鍵字

多維濾波器。 ； 預先處理量測 ； 卡爾曼濾波器 ； 非線性目標物追蹤

並列摘要

In this paper, an algorithm for measurement preprocessing in nonlinear target tracking is proposed by using the nonlinear measurement equation for the Kalman filter. Measurement preprocessing approach is based on the maximum-likelihood algorithm to derive estimates of the position, velocity and acceleration, and reduce the uncertainty of measurements to improve tracking performance. We consider that the original measurements from the polar co-ordinates are transformed to the Cartesian co-ordinates, and the linearization of the transformed measurements are employed in the preprocessing scheme. We incorporate the preprocessing algorithm into the variable dimension filter (VDF). We also develop a fuzzy rule-based VDF. Simulations illustrate that the measurement preprocessing-based VDF performs better than the fuzzy rule-based VDF and the VDF. (we taking into account the smaller bearing error of measurement.)

並列關鍵字

measurement preprocessing ； Kalman filter ； nonlinear target tracking ； variable dimension filter

參考文獻

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