本文選題：跳頻信號(hào)　切入點(diǎn)：參數(shù)估計(jì)　出處：《解放軍信息工程大學(xué)》2014年碩士論文　論文類(lèi)型：學(xué)位論文

【摘要】：跳頻通信由于具有良好的抗干擾、抗截獲及較強(qiáng)的組網(wǎng)能力,已經(jīng)在軍事和民用通信領(lǐng)域中得到了廣泛的應(yīng)用,同時(shí)也對(duì)第三方的通信監(jiān)測(cè)提出了嚴(yán)峻的挑戰(zhàn)。跳頻信號(hào)參數(shù)估計(jì)作為跳頻信號(hào)分析處理的主要任務(wù)之一,具有十分重要的現(xiàn)實(shí)意義。本文就跳頻信號(hào)參數(shù)估計(jì)的相關(guān)技術(shù)展開(kāi)深入研究,具體工作及創(chuàng)新點(diǎn)如下:1、研究了基于時(shí)頻脊線的跳頻信號(hào)參數(shù)估計(jì)方法。首先,針對(duì)傳統(tǒng)時(shí)頻分析復(fù)雜度較高的問(wèn)題,給出了一種基于盒維數(shù)的跳周期快速估計(jì)方法,該方法利用盒維數(shù)與跳頻頻率正相關(guān)的規(guī)律,采用盒維數(shù)曲線代替時(shí)頻脊線,將跳周期估計(jì)轉(zhuǎn)化為盒維數(shù)變化周期的估計(jì)。仿真表明相對(duì)于短時(shí)傅里葉變換(STFT),該方法以較小的性能損失換取了計(jì)算量上的較大優(yōu)勢(shì);然后,針對(duì)STFT頻率估計(jì)精度低的缺點(diǎn),給出了一種基于滑動(dòng)旋轉(zhuǎn)不變技術(shù)(ESPRIT)的跳頻頻率估計(jì)方法,該方法利用ESPRIT超分辨的優(yōu)勢(shì)提取時(shí)頻脊線,提高了跳頻頻率的估計(jì)精度。2、研究了基于自混頻的跳頻信號(hào)跳周期和跳時(shí)估計(jì)方法。針對(duì)跳周期估計(jì),由延時(shí)共軛相乘構(gòu)造自混頻信號(hào),理論分析了低頻分量、延遲時(shí)間及跳周期的關(guān)系。然后根據(jù)低頻分量曲線拐點(diǎn)的位置估計(jì)跳周期,給出了一種基于延時(shí)自混頻的跳周期估計(jì)方法;針對(duì)跳時(shí)估計(jì),給出了一種基于分段自混頻的跳時(shí)估計(jì)方法,按照不同偏移時(shí)間對(duì)信號(hào)進(jìn)行分段自混頻,根據(jù)低頻分量、偏移時(shí)間及跳時(shí)的關(guān)系,得出了低頻分量最大值對(duì)應(yīng)的偏移時(shí)間含有跳時(shí)信息的結(jié)論。仿真結(jié)果表明上述兩種方法均能有效估計(jì)出跳周期或跳時(shí),且都適用于多跳頻信號(hào)的情況。3、研究了跳頻信號(hào)的實(shí)時(shí)跟蹤問(wèn)題。首先分析了以跳頻頻率為系統(tǒng)狀態(tài)的狀態(tài)空間模型,并引入粒子濾波算法對(duì)頻率進(jìn)行跟蹤。針對(duì)跟蹤性能欠佳的問(wèn)題,給出了一種基于ESPRIT輔助的改進(jìn)方法,通過(guò)ESPRIT算法為粒子更新提供參考信息,提高了頻率跟蹤性能。對(duì)于粒子濾波不適用于多跳頻信號(hào)的情況,研究了一種基于稀疏重構(gòu)的多跳頻信號(hào)頻率跟蹤及DOA估計(jì)方法。該方法利用跳頻信號(hào)在頻域和空域的稀疏性,建立基于陣列接收的信號(hào)稀疏表示模型,采用稀疏貝葉斯學(xué)習(xí)(SBL)算法進(jìn)行模型求解,通過(guò)頻率估計(jì)和跳變時(shí)刻檢測(cè)完成多跳頻信號(hào)頻率的實(shí)時(shí)跟蹤和DOA估計(jì)。仿真結(jié)果驗(yàn)證了該方法的有效性。
[Abstract]:Frequency hopping communication has been widely used in military and civil communication fields because of its good anti-jamming, anti-interception and strong networking capability. At the same time, it also poses a severe challenge to the third party's communication monitoring. Estimation of frequency hopping signal parameters is the main factor in frequency hopping signal analysis and processing. It has very important practical significance. In this paper, the related techniques of frequency-hopping signal parameter estimation are deeply studied. The specific work and innovation are as follows: 1. The method of frequency hopping signal parameter estimation based on time-frequency ridge is studied. In order to solve the problem of high complexity in traditional time-frequency analysis, a fast estimation method of hopping period based on box dimension is presented. The method uses the law of positive correlation between box dimension and frequency hopping frequency, and uses box dimension curve instead of time-frequency ridge. The hopping period estimation is transformed into the estimation of the variation period of the box dimension. The simulation results show that this method gains a large advantage in computation with small performance loss compared with the STFT method, and then, in view of the disadvantage of low accuracy of STFT frequency estimation, A frequency hopping frequency estimation method based on sliding rotation invariant technique (Esprit) is presented. This method uses the advantage of ESPRIT super-resolution to extract time-frequency ridges. The precision of frequency hopping frequency estimation is improved. The frequency hopping period and time hopping estimation method based on self-mixing is studied. For the frequency hopping estimation, the low frequency component is theoretically analyzed by using the time-delay conjugate multiplication to construct the frequency hopping signal. The relationship between the delay time and the hopping period. Then, according to the position of the inflexion point of the low-frequency component curve, a method for estimating the hopping period based on the time-hopping self-mixing is presented. A method of time hopping estimation based on piecewise self-mixing is presented. According to the relationship of low-frequency component, offset time and hopping time, the signal is segmented self-mixing according to different offset time. It is concluded that the offset time corresponding to the maximum value of the low-frequency component contains time hopping information. The simulation results show that the two methods mentioned above can effectively estimate the hopping period or the hopping time. In this paper, the real-time tracking problem of frequency-hopping signals is studied. Firstly, the state space model with frequency-hopping frequency as the system state is analyzed. Aiming at the problem of poor tracking performance, an improved method based on ESPRIT is presented, which can provide reference information for particle updating through ESPRIT algorithm. The performance of frequency tracking is improved. For the case that particle filter is not suitable for multi-frequency hopping signals, a method of frequency tracking and DOA estimation for multi-frequency hopping signals based on sparse reconstruction is studied. The method makes use of the sparsity of frequency-hopping signals in frequency domain and spatial domain. The sparse representation model based on array reception is established, and the sparse Bayesian learning algorithm is used to solve the model. The real-time frequency tracking and DOA estimation of multi-frequency hopping signals are accomplished by frequency estimation and jump time detection. The simulation results show that the proposed method is effective.
【學(xué)位授予單位】：解放軍信息工程大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類(lèi)號(hào)】：TN914.41

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