量子隨機(jī)濾波器及其參數(shù)計(jì)算研究
發(fā)布時間：2019-01-27 22:47
【摘要】：在隨機(jī)濾波領(lǐng)域中,觀測信號的狀態(tài)轉(zhuǎn)換模型通常是未知的,設(shè)計(jì)一個不依賴于觀測信號及干擾噪聲先驗(yàn)特性的智能濾波器,對抗隨機(jī)干擾具有重要作用。量子濾波器利用薛定諤方程作為萬能狀態(tài)轉(zhuǎn)換方程,從而實(shí)現(xiàn)了無模型濾波。因此,改進(jìn)量子濾波算法和提高其適用性具有重要的應(yīng)用價值。本文為了提高量子濾波算法的準(zhǔn)確性、穩(wěn)定性以及適用性主要進(jìn)行了如下的研究:(1)為了提高量子濾波器的準(zhǔn)確性與穩(wěn)定性,提出了采用可變方差的高斯核函數(shù)對觀測信號進(jìn)行預(yù)處理,對該方法提高濾波器穩(wěn)定性和準(zhǔn)確性的原理進(jìn)行了詳細(xì)分析。最后將改進(jìn)的量子濾波算法與遞歸最小二乘濾波算法進(jìn)行仿真對比,說明了改進(jìn)量子濾波算法的準(zhǔn)確性、自適應(yīng)性以及靈活性。(2)為了能快速獲得量子濾波器的濾波參數(shù),通過分析量子濾波器的認(rèn)知過程,將濾波參數(shù)分為與觀測信號無關(guān)的系統(tǒng)參數(shù)和僅與觀測信號相關(guān)非系統(tǒng)參數(shù),并建立了非系統(tǒng)參數(shù)和正弦輸入信號的頻率及輸入信噪比之間的函數(shù)關(guān)系。最后將利用本文所述方法求得的濾波參數(shù)的濾波器和利用遺傳算法得到的濾波參數(shù)的濾波器進(jìn)行仿真對比,說明了本文所述方法的有效性和高效性。(3)在過濾非正弦輸入時,為了保證濾波的準(zhǔn)確性,本文采用短時傅里葉變換近似在線估計(jì)輸入信號的頻率及輸入信噪比,從而實(shí)現(xiàn)非系統(tǒng)參數(shù)的在線更新。最后通過固定非系統(tǒng)參數(shù)的濾波器和在線更新非系統(tǒng)參數(shù)的濾波器的仿真對比,說明本文所述非系統(tǒng)參數(shù)在線更新算法的優(yōu)越性及其不足。(4)當(dāng)觀測信號為矢量序列時,為了充分利用其空間相關(guān)性,本文首先設(shè)計(jì)了時間復(fù)雜度較低的無反饋量子濾波算法,然后將其拓展為二維量子濾波算法。最后將二維量子濾波器和獨(dú)立一維量子濾波器組進(jìn)行仿真對比,反映二維量子濾波算法的優(yōu)缺點(diǎn)。
[Abstract]:In the field of random filtering, the state transition model of observation signal is usually unknown. It is very important to design an intelligent filter which does not depend on the prior characteristics of observation signal and interference noise. The quantum filter uses Schrodinger equation as the universal state transformation equation to realize modelless filtering. Therefore, improving the quantum filtering algorithm and improving its applicability have important application value. In order to improve the accuracy, stability and applicability of the quantum filter algorithm, this paper mainly studies the following: (1) in order to improve the accuracy and stability of the quantum filter, A variable variance Gao Si kernel function is proposed to preprocess the observed signal, and the principle of improving the stability and accuracy of the filter is analyzed in detail. Finally, the improved quantum filter algorithm is compared with the recursive least square filter algorithm, and the accuracy, adaptability and flexibility of the improved quantum filter algorithm are illustrated. (2) in order to obtain the filter parameters of the quantum filter quickly, By analyzing the cognitive process of the quantum filter, the filter parameters are divided into the system parameters independent of the observed signal and the non-system parameters related only to the observed signal. The functional relationship between the non-system parameters, the frequency of sinusoidal input signal and the input signal-to-noise ratio is established. Finally, the filter with the filter parameters obtained by the method described in this paper is simulated and compared with the filter parameters obtained by genetic algorithm. The effectiveness and efficiency of the proposed method are illustrated. (3) in order to ensure the accuracy of the filtering, the short time Fourier transform (STFT) is used to estimate the frequency and the input signal to noise ratio (SNR) of the input signal. In order to realize the online update of non-system parameters. Finally, the advantages and disadvantages of the on-line updating algorithm for the non-system parameters are illustrated by the comparison between the filter with fixed non-system parameters and the filter with on-line updating parameters. (4) when the observed signal is a vector sequence, In order to make full use of its spatial correlation, this paper first designs a non-feedback quantum filtering algorithm with low time complexity, and then extends it to two-dimensional quantum filtering algorithm. Finally, the two-dimension quantum filter and the independent one-dimensional quantum filter bank are simulated and compared to reflect the advantages and disadvantages of the two-dimensional quantum filter algorithm.
【學(xué)位授予單位】：華僑大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號】：TN713
本文編號：2416778
[Abstract]:In the field of random filtering, the state transition model of observation signal is usually unknown. It is very important to design an intelligent filter which does not depend on the prior characteristics of observation signal and interference noise. The quantum filter uses Schrodinger equation as the universal state transformation equation to realize modelless filtering. Therefore, improving the quantum filtering algorithm and improving its applicability have important application value. In order to improve the accuracy, stability and applicability of the quantum filter algorithm, this paper mainly studies the following: (1) in order to improve the accuracy and stability of the quantum filter, A variable variance Gao Si kernel function is proposed to preprocess the observed signal, and the principle of improving the stability and accuracy of the filter is analyzed in detail. Finally, the improved quantum filter algorithm is compared with the recursive least square filter algorithm, and the accuracy, adaptability and flexibility of the improved quantum filter algorithm are illustrated. (2) in order to obtain the filter parameters of the quantum filter quickly, By analyzing the cognitive process of the quantum filter, the filter parameters are divided into the system parameters independent of the observed signal and the non-system parameters related only to the observed signal. The functional relationship between the non-system parameters, the frequency of sinusoidal input signal and the input signal-to-noise ratio is established. Finally, the filter with the filter parameters obtained by the method described in this paper is simulated and compared with the filter parameters obtained by genetic algorithm. The effectiveness and efficiency of the proposed method are illustrated. (3) in order to ensure the accuracy of the filtering, the short time Fourier transform (STFT) is used to estimate the frequency and the input signal to noise ratio (SNR) of the input signal. In order to realize the online update of non-system parameters. Finally, the advantages and disadvantages of the on-line updating algorithm for the non-system parameters are illustrated by the comparison between the filter with fixed non-system parameters and the filter with on-line updating parameters. (4) when the observed signal is a vector sequence, In order to make full use of its spatial correlation, this paper first designs a non-feedback quantum filtering algorithm with low time complexity, and then extends it to two-dimensional quantum filtering algorithm. Finally, the two-dimension quantum filter and the independent one-dimensional quantum filter bank are simulated and compared to reflect the advantages and disadvantages of the two-dimensional quantum filter algorithm.
【學(xué)位授予單位】：華僑大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號】：TN713
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