clutter filter 中文意思是什麼
clutter filter
解釋
反干擾濾波器-
According to the statistic characteristics of ir background clutter, an improved spatial matched filter ( smf ) is presented
理論和實驗結果表明，該方法對于抑制強起伏的紅外背景是十分有效的。 -
In the subsystem of signal processing, digital sidelobe cancell block, digital pulse compress block, adaptive clutter canceller block, doppler filter and constant false alarm rate block are respectively discussed and set up. in signal processing, data in the same coherent processing interval form a matrix, and the data processing was completed based on the matrix. data are processed with the row vector or column vector respectively when needed
在信號處理分系統中，將脈沖壓縮得到的數據在各個相干處理區間進行處理，將一個相干處理區間的數據按照不同的脈沖重復周期和不同的距離單元排列成一個矩陣，當需要實現數字脈沖壓縮時對矩陣的各行（同一個脈沖重復周期的數據）進行處理，當需要實現多普勒濾波時，就對矩陣的各列（同一個距離單元的數據）進行處理。 -
In this dissertation, the research trends for the problem have been introduced ; the ‘ dim ’ and ‘ point ’ has been strictly defined in mathematics from machine vision and human vision ; the ideal clutter suppression system based on clutter predication and the realization and evaluation of evaluation index has been studied, in succession the clutter suppression technologies have been researched. firstly, the classic nonparametric algorithm has been analyzed in detail and systematically, for it ’ s weakness that it cannot remove the non - stationary clutter ideally, kalman filter algorithm for clutter suppression in 2d image signal has been built. secondly, fast adaptive kalman filter is presented based on fast wide - sense stationary areas partition algorithm : limited combination and division algorithm based on quarti - tree algorithm, new taxis filter route algorithm which can break through the limitation of the necessity of pixel neighborhood of 2d filter and laplace data model with two parameters which is perfectly suitable for the residual image of kalman clutter suppression
首先分析了經典的非參數法，對於四種具有代表性的核，從前述的三個性能評價方面做了分析和對比，指出了其速度快的優點和對非平穩圖像適應性差的弱點，針對非參數法的弱點，重點研究了對非平穩圖像適應良好的卡爾曼雜波抑制技術:建立了非平穩圖像的類自回歸模型，在此基礎上建立了二維卡爾曼濾波基礎的兩個方程:狀態方程和測量方程;建立了非平穩圖像準平穩區域快速劃分演算法:基於四叉樹法的有限分裂合併演算法;二維空間的基於k排序的濾波路線演算法，突破了空域濾波路線上區域相鄰的限制;在這些研究的基礎上實現了快速卡爾曼估計，實驗驗證了該方法相對逐點卡爾曼估計可以提高運算速度三倍左右;雜波抑制結果表明傳統的高斯性檢驗並不適合卡爾曼估計后的殘余圖像，由此建立了殘余圖像的雙參數拉普拉斯模型，實驗表明其可以完好的吻合殘余圖像的概率密度曲線。 -
Firstly, according to the characteristic that the doppler frequency shift signal can be approximated as a single sinusoid signal, the extended sinusoid signal retrieval ( pisarenko and esprit ) methods are presented and signal state and measurement formulations are developed, so the kalman filter recursive method is got. the brief introduction of low velocity moving target doppler frequency shift signal wigner - ville transformation and wavelet transformation expression are presented in this paper. secondly, because the clutter is gaussian distribution, cumement and high - order spectrum based methods are presented and the simulation results prove their good performance to suppress gaussian clutter in low velocity moving target doppler frequency shift signal processing
一個方面是根據低速目標的多普勒信號可簡化為單一正弦波形式這一特點，得到了擴展的高斯色噪聲背景下的諧波恢復演算法，即高斯色噪聲中的pisarenko諧波恢復法和旋轉因子不變法（ esprit ） ；並推導了信號的狀態方程和觀測方程，進而得到基於卡爾曼濾波的遞推演算法對信號進行提取；本文還簡單的介紹了低速運動目標的多普勒頻移信號的wigner - ville變換與小波變換；另一個方面是針對雜波服從高斯分佈這一特點，提出了對接收信號求累積量和高階譜來對高斯雜波進行抑制。 -
We apply local entropy algorithm to the clutter tracking and detect clutter edge by local entropy filter. we also develop a new method to estimate fractal dimension from clutter surface for region segmentation. regarding the different results of clutter tracking as the background information, we propose clutter feature ( cf ) - cfar detector and structuring element ( se ) - cfar detector
然後針對這兩種不同的雜波跟蹤結果，提出分別採用雜被屬性恆虛警檢測器和結構元素恆虛警檢測器，以改善傳統雷達系統中僅採用單一恆虛警演算法造成的檢測性能低下。 -
It expatiates the implementation circuit of key technology of the clutter storage and binary detection in radar signal processing. the second part discusses implementation of radar data processing in this system. it introduces the c ~ - ~ 3 filter algorithm and software design of the auto - detect - system in details
第一部分介紹了雷達信號自動檢測錄取和恆虛警處理的有關理論基礎和硬體實現，詳細闡述了雷達視頻處理中的雜波圖存儲、二進制檢測等關鍵技術的工程實現；第二部分介紹雷達數據處理在本系統的實現，詳細地介紹了-演算法原理和整個錄取系統的軟體體系設計，並就雷達錄取程序中有關難題的解決作詳細探討。
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