recursive filtering 中文意思是什麼
-
The research analysed the influence of observational noises about temporal correlation, and gives recursive formula of kalman filtering
分析了測量噪聲時間相關對卡爾曼濾波結果的影響，給出了觀測噪聲時間相關時的卡爾曼濾波遞推公式。 -
For large errors introduced by nonlinear state - space model in passive locating and tracking problems, various suboptimal recursive filtering algorithms are aralyzed and summarized, such as the extended kalman filtering ( ekf ), the modified gain extended kalman filtering ( mgekf ), the second order filtering and the adaptive extended kalman filtering ( aekf )
摘要針對被動定位跟蹤中狀態空間模型非線性程度較高所引發的濾波精度偏低的問題，分析和總結了已有的包括推廣卡爾曼濾波（ ekf ） 、修正增益的推廣卡曼濾波（ mgekf ） 、二階濾波、自適應推廣卡爾受濾波（ aekf ）等各種次優遞推濾波演算法的特點。 -
The output sinr limitations of mmse and constrained mmoe optimal filter are derived in the cirsumstance aforementioned. these limitations constitute the base of the code - aided optimal filtering and nbi estimate - subtract filtering. this dissertation summarize the recursive least squares ( rls ) algorithm, blind recursive least squares algorithm and the qr decomposing for these two algorithms
為自適應實現碼輔助最優濾波，本文系統總結了遞歸最小二乘（ rls ）和盲遞歸最小二乘（ brls ）演算法，以及兩者基於正交三角分解（ qrd ）的并行計算結構。 -
First, it delves into the key technique of eis ? image registration, and presents an effective registration algorithm which is from coarse toprecise and form local to global. second, it applies recursive kalman filtering technique inmotion filtering of registration parameters and makes motion compensation for practical image. finally, it takes advantage of mosaicking to reconstruct undefined regions to avoid theubiquitous information missing and image degradation. this algorithm has high precision as wellas high speed
首先，對電子穩像中關鍵環節? ?圖像配準技術進行了深入研究，給出一種由粗到精、由局部到全局的高效配準演算法;接著，採用了遞歸kalman濾波技術對配準參數進行運動濾波並對實際圖像進行運動補償;最後，利用了圖像拼接技術進行「無定義區」重建，避免了普遍存在的信息丟失和圖像降質問題。 -
This paper studies 3 kinds of algorithms : the viterbi algorithm, multiresolutional algorithm based on wavelet transformation and bayesian bootstrap algorithm. the viterbi algorithm is based on the hidden markov model theory and it is a kind of map estimation, this paper studies this algorithm and puts up an algorithm that suits for filtering in the presence of interference. multiresolutional algorithm takes full advantage of multiresolutional data, we can see it has a better filtering ability than the traditional filtering methods ; bootstrap algorithm is a recursive bayesian estimation, it describes the probability density function by the samples, so it can be used to nonlinear non - gaussion filtering, the simulation result of the two groundings is presented
Viterbi演算法以隱馬爾可夫理論為基礎，是一種最大后驗概率估計方法，本文對該演算法進行了研究，給出了一種適合於非高斯干擾條件下的濾波方法；多分辨分析方法充分利用到了多解析度測量數據所包含的信息，從模擬結果中可以看出，該方法的濾波精度要高於傳統的濾波演算法；自主濾波方法是一種遞推貝葉斯估計演算法，它利用采樣點來描述目標狀態的概率密度函數，因而適用於非線性、非高斯條件下的濾波，本文分別對這兩種情況下的濾波進行了模擬。 -
Linear minimum variance estimate and optimally weighted ls estimate are often used in many fields such as signal processing, control and communications. kalman filtering is the recursive version of ihe first estimate
在信號處理、控制和通訊等技術領域，常常使用線性最小方差估計和最優加權最小二乘估計對參數作出估計。 -
Based on above performances the applications of multi - sensor data fusion in state estimation for maneuvering target is studied systemically. the main work includes : based on the analysis that the extreme value of acceleration presupposed causes influence in the “ current ” statistical model, a modified model is given, which utilizes the functional relationship between maneuvering status and estimation of the neighboring intersample position vector to carry out the self - adaptive of the process noise variance. then combining with the recursive characteristic of kalman filter, an improved self - adaptive filtering algorithm is presented
基於此，本文針對多傳感器數據融合技術在機動目標狀態估計中的應用進行了系統的研究，其主要工作如下: 1 、基於「當前」統計模型中加速度極限值的預先設定對于濾波效果影響的分析，利用目標機動狀況與相鄰采樣時刻間位置估計量變化之間的函數關系實現噪聲方差自適應，進而提出了一種修正的模型，並結合卡爾曼濾波遞推演算法，提出了一種改進的自適應濾波演算法。
分享友人