深入研究了UWB(ultra wideband)无线传感器网络中基于匹配滤波门限检测的TOA(time of arrival)估计算法.针对现有算法的不足,提出了一种三步TOA估计算法:先确定DP(direct path)搜索区域,然后使用门限检测确定DP的粗略位置,最后精确搜索到DP的中心.其中,用于计算检测门限的门限因子依据匹配滤波输出的峭度动态设置,设置模型独立于信道模式,其正确性通过与使用固定门限因子所获得的性能对比进行了验证.与其他算法的性能对比仿真结果表明,所提出的三步TOA估计算法在运算效率和TOA估计精度上取得了较好折衷,适合于当前实际应用.还通过对TOA估计误差的统计分析讨论了测距结果的可信度:依据峭度将测距结果划分为可信和不可信两个级别,并为各级别的TOA估计误差分别了建立概率密度模型.在定位模块中有效利用这些可信度信息,可进一步提高定位精度.%The TOA (time of arrival) estimation algorithms based on match-filtering detection for UWB (ultra wideband) wireless sensor networks are extensively studied in this paper. Based on the analysis of the drawbacks of the algorithms in the literature, a three-step algorithm is proposed: first, determine the search region for DP (direct path) detection; then, a rough detection of DP is made by threshold comparison; and last, the precise location of DP, i.e., the center of the arriving pulse, is obtained by a refined search process. The threshold factor used to calculate the threshold in the second step is set dynamically by using a model in terms of the kurtosis of the match-filtering output. The model is well independent of the channel model, and its effectiveness is proved through the comparison of the resulted performance with that of using fixed threshold factor. By comparing the performance of this algorithm with that of other algorithms, it can be observed that the proposed three-step algorithm has achieved a good trade-off between computational efficiency and estimation accuracy, thus more appropriate for current applications. In addition, the reliability of TOA estimation result is discussed through statistical analysis. Two levels of reliability are defined with regard to the corresponding kurtosis of the TOA estimation, and the probability density function for TOA estimation errors of each level is modeled. Properly incorporating the reliability information into the positioning algorithm will definitely improve the final location estimation accuracy.
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