本文選題：無線聲傳感器陣列網(wǎng)絡(luò) + 波達(dá)角估計(jì)　； 參考：《浙江大學(xué)》2016年博士論文

【摘要】：無線聲傳感器陣列網(wǎng)絡(luò)(WASAN)將無線聲傳感器網(wǎng)絡(luò)與傳感器陣列處理相結(jié)合,因此兼具他們各自擁有的高精度、自組網(wǎng)、強(qiáng)隱蔽性等優(yōu)勢,廣泛應(yīng)用于低空無人機(jī)、地面重型車輛以及水下潛艇等國防安全領(lǐng)域的被動(dòng)目標(biāo)定位和追蹤。然而受本地計(jì)算、通信帶寬和能量供給方面的限制,低成本無線陣列實(shí)時(shí)高效DOA估計(jì)是一個(gè)極具挑戰(zhàn)性的課題。近年來新興的壓縮感知技術(shù),面向信號的稀疏特性,通過少量的隨機(jī)觀測數(shù)據(jù)高概率地恢復(fù)出原始信號,在本地資源受限和通信受限的WASAN系統(tǒng)中具有非常大的潛在應(yīng)用價(jià)值。本文在綜述了國內(nèi)外關(guān)于WASAN被動(dòng)目標(biāo)定位最新研究進(jìn)展的基礎(chǔ)上,針對WASAN的資源受限問題從低功耗信號采集、高效實(shí)時(shí)數(shù)據(jù)傳輸、信息信號處理以及快速算法實(shí)現(xiàn)方面展開較為系統(tǒng)和深入的研究。本文的主要內(nèi)容如下:1.提出了 一種基于聲陣列信號疏特性的隨機(jī)壓縮采樣與重構(gòu)方法。該方法基于陣列信號的頻域稀疏特性,針對傳感器節(jié)點(diǎn)本地計(jì)算資源和通信帶寬受限的特點(diǎn)通過隨機(jī)非均勻采樣技術(shù)對原始信號進(jìn)行降維觀測,在降低WASAN數(shù)據(jù)發(fā)送量的同時(shí)無需額外的本地計(jì)算開銷。2.提出了一種基于聯(lián)合稀疏特性的DOA(DirectionofArrival)估計(jì)方法。該方法利用有限數(shù)目的目標(biāo)在角度空間的稀疏性和聲源信號在頻域的稀疏性,通過引入了聯(lián)合稀疏表示矩陣的方法對陣列隨機(jī)壓縮采樣數(shù)據(jù)在角度空間和頻率空間進(jìn)行聯(lián)合稀疏表示,并直接對目標(biāo)的DOA角度進(jìn)行稀疏重構(gòu)。該方法在降低陣列數(shù)據(jù)量的同時(shí)避免了傳統(tǒng)壓縮采樣-信號重構(gòu)-DOA處理模式中信號重構(gòu)誤差到DOA估計(jì)誤差的傳遞過程,減少了信號處理的冗余。3.提出了一種極低數(shù)據(jù)量的DOA估計(jì)方法。該方法面向DOA估計(jì)的本質(zhì)問題-相位差估計(jì),利用單比特壓縮感知技術(shù)對信號波形進(jìn)行重構(gòu)的特點(diǎn),在傳感器節(jié)點(diǎn)端僅用1比特對采樣得到的數(shù)據(jù)進(jìn)行量化。進(jìn)一步利用信號的聯(lián)合稀疏特性,從單比特量測中估計(jì)出目標(biāo)的DOA空間譜。4.提出了 一種快速三維DOA估計(jì)方法用于WASAN系統(tǒng)本地DOA估計(jì)實(shí)現(xiàn)。該方法基于陣列幾何結(jié)構(gòu)特性解決了當(dāng)陣元間距大于信號半波長時(shí)候存在的相位模糊問題并構(gòu)建了基于相位量測的線性DOA估計(jì)模型直接在相位空間對DOA值進(jìn)行計(jì)算。該方法解決了傳統(tǒng)的AML、MUISC、Beamforming等基于角度窮舉搜索算法計(jì)算復(fù)雜度大的問題。理論分析表明,該方法的DOA估計(jì)誤差的協(xié)方差和AML估計(jì)器的克拉美羅下界一致。5.提出了 一種基于相位量測的分布式快速目標(biāo)定位算法。該方法僅需要將少量的本地預(yù)處理結(jié)果發(fā)送到后端融合中心進(jìn)行直接定位,避免了傳統(tǒng)基于完整信息直接定位方法的完整數(shù)據(jù)發(fā)送要求。理論分析表明該方法的估計(jì)誤差協(xié)方差與傳統(tǒng)的基于完整信息直接定位方法的克拉美羅下界一致。
[Abstract]:WASAN (Wireless Acoustic Sensor Array Network) combines wireless acoustic sensor network with sensor array processing, so they have the advantages of high precision, self-organizing network and strong concealment, so they are widely used in low-altitude UAVs. Passive target location and tracking in defense and security areas such as ground heavy vehicles and underwater submarines. However, due to the limitations of local computing, communication bandwidth and energy supply, real-time and efficient DOA estimation for low-cost wireless arrays is a challenging task. In recent years, the new compression sensing technology, which is oriented to the sparse characteristic of signal, recovers the original signal with high probability through a small amount of random observation data. It has great potential application value in WASAN systems with limited local resources and communication constraints. On the basis of summarizing the latest research progress of WASAN passive target localization at home and abroad, this paper aims at the problem of resource limitation of WASAN from low-power signal acquisition and efficient real-time data transmission. Information signal processing and fast algorithm implementation are studied systematically and deeply. The main contents of this paper are as follows: 1. A random compression sampling and reconstruction method based on the sparse characteristics of acoustic array signals is proposed. This method is based on the sparse characteristic of array signal in frequency domain, aiming at the local computing resource and the limited communication bandwidth of sensor node, the dimension reduction observation of the original signal is carried out by random non-uniform sampling technique. Reduce the amount of WASAN data sent without additional local computing overhead. 2. 2. A method for estimating load Direction of Arrivalbased on joint sparsity is proposed. This method utilizes the sparsity of a finite number of targets in angle space and the sparsity of sound source signals in frequency domain. The joint sparse representation matrix is introduced to represent the array randomly compressed sampling data in angle space and frequency space, and the DOA angle of the target is reconstructed directly. This method not only reduces the amount of array data, but also avoids the transfer process from signal reconstruction error to DOA estimation error in the traditional compression sample-signal refactor-DOA processing mode, and reduces the redundancy of signal processing. A very low data DOA estimation method is proposed. This method aims at the essential problem of DOA estimation-phase difference estimation. It uses single bit compression sensing technology to reconstruct the signal waveform and quantifies the sampled data only with 1 bit at the sensor node. Furthermore, the DOA spatial spectrum. 4. 4 of the target is estimated from the single bit measurement by using the joint sparsity property of the signal. A fast 3D DOA estimation method for local DOA estimation in WASAN systems is proposed. The method solves the phase ambiguity problem when the array spacing is larger than the half-wavelength of the signal based on the geometric structure of the array and constructs a linear DOA estimation model based on phase measurement to calculate the DOA value directly in the phase space. This method solves the problem of high computational complexity of traditional AML-MUISC-Beamforming algorithm based on angle exhaustive search. Theoretical analysis shows that the covariance of the DOA estimation error of this method is consistent with that of the lower bound of the AML estimator. A distributed fast target location algorithm based on phase measurement is proposed. This method only needs to send a small amount of local preprocessing results to the back end fusion center for direct location, thus avoiding the requirement of complete data transmission based on traditional direct localization method based on complete information. Theoretical analysis shows that the estimation error covariance of this method is consistent with that of Clemero lower bound which is based on the traditional direct location method based on complete information.
【學(xué)位授予單位】：浙江大學(xué)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2016
【分類號】：TP212.9;TN911.7

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