基于天线阵列的近场WiFi多参数联合估计算法研究

发布时间：2018-07-16 16:03

【摘要】：WiFi室内定位技术是近些年来大家关注的热点,GPS在室内定位上有局限性,每次定位至少需要捕获4颗卫星的信号,而且在室内和高楼密布的环境中,信号遮挡严重,常用的TOA(Time of Arrival)、TDOA(Time Difference of Arrival)、RSSI(Received Signal Strength Indication)等算法达不到定位精度和实时性的要求。因此,需要对室内定位算法进行新的尝试,研究一种能够满足人们需求的高精度,低复杂度的室内定位算法。DOA(Direction of Arrival)多参数联合估计算法通过对到达角、俯仰角、距离等参数进行联合估计以实现定位目的,大量研究成果在雷达、声纳、电子侦察等领域已投入使用,本文从以下几个方面对DOA多参数联合估计算法展开研究,验证算法在近场WiFi场景中的可行性。首先,研究了基于二阶累积量的多参数联合估计算法,梁军利等提出一种新型的基于二阶统计量的近场源定位算法,算法将累积量的性质与ESPRIT算法相结合,创新性的构建两个二阶累积量矩阵,算法不需要构造一个高维的累积量矩阵,就可以实现多参数的联合估计并且使得参数自动配对,提高了参数估计的性能。其次,研究了基于二阶统计量的ML算法,将基于子空间拟合的ML算法与MUSIC算法、新型的基于二阶统计量的近场源定位算法进行多参数估计的精度分析,并对比了几种基于二阶统计量的算法的复杂度。然后,针对二阶累积量算法性能较差的问题,利用高阶累积量的优势,对基于四阶累积量的两种多参数估计算法：对称阵列的MUSIC算法和非对称阵列的ESPRIT算法进行研究,比较两种算法的优缺点,详细分析了阵元数、快拍数、信噪比的变化对DOA多参数估计的精度产生的影响。接着,提出一种基于非对称阵列的TLS-ESPRIT改进算法,以较少的四阶累积量矩阵构造新的矩阵,将TLS-ESPRIT算法与MUSIC-like算法相结合,避免了参数配对问题,实现了多信源场景下的角度参数和距离参数的联合估计。改进后的算法提高了阵列孔径的利用率,与原算法相比,两种算法计算复杂度相近,但是改进算法有效的提升了参数估计的精度,通过仿真实验,对比分析了二阶累积量算法和四阶累积量算法的参数估计精度、计算复杂度。最后,对全文工作进行了总结,并对后续研究工作进行了展望。
[Abstract]:WiFi indoor positioning technology is a hot topic in recent years. GPS has its limitations in indoor positioning. It needs to capture at least four satellite signals each time. Moreover, in the indoor and high-rise environment, the signals are blocked seriously. The time difference of Arrival) (TDOA) (received signal standing indication) algorithm can not meet the requirements of location accuracy and real-time performance. Therefore, it is necessary to make a new attempt on indoor localization algorithm, and to study a high precision and low complexity indoor localization algorithm. DOA (Direction of Arrival) multiparameter joint estimation algorithm) is based on angle of arrival and pitch angle. A large number of research results have been used in radar, sonar, electronic reconnaissance and other fields for joint estimation of range and other parameters to achieve the purpose of location. In this paper, the DOA multi-parameter joint estimation algorithm is studied from the following aspects. Verify the feasibility of the algorithm in near field WiFi scenarios. Firstly, the multi-parameter joint estimation algorithm based on second-order cumulant is studied. Liang Junli et al proposes a new near-field source location algorithm based on second-order statistics, which combines the properties of cumulant with Esprit algorithm. Two second-order cumulant matrices are constructed innovatively. The algorithm does not need to construct a high-dimensional cumulant matrix, it can realize the joint estimation of multi-parameter and make the parameters match automatically, which improves the performance of parameter estimation. Secondly, the ML algorithm based on second-order statistics is studied. The accuracy of multi-parameter estimation is analyzed by combining ML algorithm based on subspace fitting with music algorithm and a new near-field source location algorithm based on second-order statistics. The complexity of several algorithms based on second order statistics is compared. Then, aiming at the poor performance of the second-order cumulant algorithm, two multi-parameter estimation algorithms based on the fourth-order cumulant, the music algorithm of symmetric array and the Esprit algorithm of asymmetric array, are studied based on the advantage of high-order cumulant. The advantages and disadvantages of the two algorithms are compared, and the effects of the number of array elements, the number of beats and the signal-to-noise ratio on the accuracy of DOA multi-parameter estimation are analyzed in detail. Then, an improved TLS-ESPRIT algorithm based on asymmetric array is proposed. The new matrix is constructed with less fourth order cumulant matrix, and the TLS-ESPRIT algorithm is combined with the MUSIC-like algorithm to avoid the parameter matching problem. The joint estimation of angle parameter and distance parameter in multi-source scenario is realized. The improved algorithm improves the utilization ratio of array aperture, compared with the original algorithm, the computational complexity of the two algorithms is similar, but the improved algorithm can effectively improve the precision of parameter estimation. The parameter estimation accuracy and computational complexity of the second order cumulant algorithm and the fourth order cumulant algorithm are compared and analyzed. Finally, the paper summarizes the work of the paper, and looks forward to the future research work.
【学位授予单位】：西南交通大学
【学位级别】：硕士
【学位授予年份】：2016
【分类号】：TN820.15;TN92

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