【摘要】：聲矢量傳感器(Acoustic Vector Sensor,AVS)技術(shù)可應(yīng)用在通信定位、聲吶、故障源定位、雷達(dá)以及生物醫(yī)學(xué)等眾多的國(guó)民經(jīng)濟(jì)以及軍事國(guó)防領(lǐng)域，同時(shí)聲矢量傳感器能夠同步共點(diǎn)地測(cè)量某點(diǎn)處的聲壓和質(zhì)點(diǎn)振速的矢量信息，獲取多維聲信號(hào)信息，進(jìn)而能夠用于分析處理的信息更多，因此，聲矢量傳感器技術(shù)以及聲矢量傳感器信號(hào)處理技術(shù)一直是備受關(guān)注的研究方向。 近些年來(lái)，基于聲矢量傳感器的陣列信號(hào)處理技術(shù)的研究主要集中在空間譜估計(jì)上，而空間譜估計(jì)算法都是建立在假設(shè)目標(biāo)在觀測(cè)時(shí)間內(nèi)是靜止的這一前提之下，在實(shí)際環(huán)境中，信號(hào)源通常是運(yùn)動(dòng)的，致使目標(biāo)的波達(dá)方向(Direction of Arrival，DOA)在觀測(cè)時(shí)間內(nèi)靜止的假設(shè)不再成立，針對(duì)動(dòng)態(tài)目標(biāo)的情況，目標(biāo)入射方位估計(jì)算法會(huì)造成較大的估計(jì)誤差。隨著人們對(duì)目標(biāo)測(cè)向的精確度和實(shí)時(shí)性要求越來(lái)越高，靜態(tài)波達(dá)方向估計(jì)算法不能滿足日益增高的要求，因此研究用于動(dòng)態(tài)目標(biāo)的DOA估計(jì)跟蹤算法具有重要意義。 本文主要研究了基于粒子濾波算法以及其改進(jìn)算法的聲矢量傳感器DOA跟蹤估計(jì)算法。首先對(duì)聲矢量傳感器的陣列流型和測(cè)量模型的建立進(jìn)行推導(dǎo)，對(duì)粒子濾波算法和貝葉斯估計(jì)理論進(jìn)行詳細(xì)的介紹。其次，研究了基于粒子濾波算法的DOA估計(jì)跟蹤算法，并且在該算法的基礎(chǔ)上，針對(duì)俯仰角和方位角的相對(duì)獨(dú)立性，提出了分別評(píng)價(jià)樣本權(quán)重的改進(jìn)粒子濾波算法，并將其應(yīng)用于方位跟蹤方面。再次，利用空間譜估計(jì)理論中的經(jīng)典高分辨MUSIC(Multiple Signal Classfication, MUSIC)算法的空間譜與似然函數(shù)的相似性，提出了改進(jìn)似然函數(shù)的粒子濾波跟蹤算法。然后，基于四元數(shù)理論結(jié)合導(dǎo)向矢量的長(zhǎng)矢量模型的推導(dǎo)，建立聲矢量傳感器導(dǎo)向矢量的四元數(shù)模型，給出了四元數(shù)MUSIC算法的譜估計(jì)式，利用其作為粒子權(quán)重的評(píng)價(jià)函數(shù)，提出了一種改進(jìn)的DOA跟蹤算法以及改進(jìn)粒子濾波算法的跟蹤算法。最后，，通過(guò)MATLAB仿真實(shí)驗(yàn)驗(yàn)證了本文提出的跟蹤算法可實(shí)現(xiàn)目標(biāo)方位的跟蹤估計(jì)，并且具有較好的跟蹤性能。 本文主要針對(duì)聲矢量傳感器研究了DOA跟蹤問(wèn)題，期待本文的研究有助于聲矢量陣列信號(hào)理論的發(fā)展和實(shí)際工程的應(yīng)用。在今后的工作中，在聲矢量傳感器DOA跟蹤領(lǐng)域，相關(guān)研究也需繼續(xù)進(jìn)行。
[Abstract]:Acoustic vector sensor (Acoustic Vector Sensor,AVS) technology can be used in many fields of national economy and military defense, such as communication location, sonar, fault source location, radar, biomedicine and so on. At the same time, the acoustic vector sensor can synchronously measure the vector information of sound pressure and particle vibration velocity at a certain point, and obtain multi-dimensional acoustic signal information, which can be used to analyze and process more information. Acoustic vector sensor technology and acoustic vector sensor signal processing technology have been paid close attention to. In recent years, the research of array signal processing technology based on acoustic vector sensor is mainly focused on spatial spectrum estimation, and spatial spectrum estimation algorithms are based on the assumption that the target is stationary in observation time. In the actual environment, the signal source is usually moving, so that the assumption that the target's direction-of-arrival (Direction of Arrival,DOA) is stationary during the observation time is no longer true, for the case of the dynamic target, The estimation algorithm of the incident azimuth of the target will cause a large estimation error. With the increasing demand for accuracy and real-time of target direction finding, the static DOA estimation algorithm can not meet the increasing requirements. Therefore, it is of great significance to study the DOA estimation and tracking algorithm for dynamic targets. This paper mainly studies the DOA tracking and estimation algorithm of acoustic vector sensor based on particle filter and its improved algorithm. Firstly, the array flow pattern and measurement model of acoustic vector sensor are deduced, and the particle filter algorithm and Bayesian estimation theory are introduced in detail. Secondly, the DOA estimation and tracking algorithm based on particle filter algorithm is studied, and based on this algorithm, an improved particle filter algorithm is proposed to evaluate the weight of samples, aiming at the relative independence of pitch angle and azimuth angle. It is applied to azimuth tracking. Thirdly, using the similarity between spatial spectrum and likelihood function of classical high-resolution MUSIC (Multiple Signal Classfication, MUSIC) algorithm in spatial spectrum estimation theory, a particle filter tracking algorithm with improved likelihood function is proposed. Then, based on the quaternion theory and the derivation of the long vector model of the guidance vector, the quaternion model of the guidance vector of the acoustic vector sensor is established, and the spectrum estimation formula of the quaternion MUSIC algorithm is given, which is used as the evaluation function of the particle weight. An improved DOA tracking algorithm and an improved particle filter algorithm are proposed. Finally, the MATLAB simulation results show that the proposed tracking algorithm can achieve the target azimuth estimation and has good tracking performance. In this paper, the DOA tracking problem for acoustic vector sensors is studied, and it is expected that the research in this paper will be helpful to the development of acoustic vector array signal theory and the application of practical engineering. In the future work, in the field of acoustic vector sensor DOA tracking, the related research also needs to be continued.
【學(xué)位授予單位】：吉林大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類號(hào)】：TN911.7;TN713

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