Vehicle reidentification is an elegant solution for gathering several pieces of valuable traffic information, e.g., space mean speed, travel time, vehicle tracking, and origin/destination data. Recently, a number of v...Vehicle reidentification is an elegant solution for gathering several pieces of valuable traffic information, e.g., space mean speed, travel time, vehicle tracking, and origin/destination data. Recently, a number of vehiclereidentification algorithms utilizing inductive loop signals have been proposed to take advantage of the widespread availability of loop detectors. These algorithms, however, all directly utilize the raw inductance signals for pattern matching and feature extraction without deconvolution. The raw loop signals are essentially a convolved output between the true vehicle inductance signature and the loop system function, and thus a deconvolution is needed in order to expose the detailed features of individual vehicles. The purpose of this paper is to present a recent investigation on restoration of true inductance signatures by applying a blind deconvolution process. The main advantage of blind deconvolution over the conventional deconvolution is that the computation does not require modeling of a precise loop-detector system function. Experimental results show that the proposed blind deconvolution reveals much more detailed features of inductance signals and, as a result, increases the vehicle reidentification accuracy.展开更多
GNSS民用信号因其公开性和脆弱性易受外界欺骗干扰.作为欺骗干扰检测的有效方法,信号质量监测(signal quality monitoring,SQM)技术通过检测接收机跟踪环路早码、即时码、晚码(early late phase,ELP)的相关结果,与无欺骗时的相关特性对...GNSS民用信号因其公开性和脆弱性易受外界欺骗干扰.作为欺骗干扰检测的有效方法,信号质量监测(signal quality monitoring,SQM)技术通过检测接收机跟踪环路早码、即时码、晚码(early late phase,ELP)的相关结果,与无欺骗时的相关特性对比,判断是否存在欺骗干扰.常规SQM算法仅利用ELP三个信息,检测性能受限,为此提出多相关器联合功率(SQM detection of power combined Multi-correlator groups,SPCM)算法.以ELP之间多个等间隔相关器输出功率的加权为检测量,且取相关时刻与即时码时间差的反比为加权系数;进一步分析检测量的概率分布特性,并基于Neyman-Pearson理论确定最佳检测阈值,通过比较检测量与检测阈值的大小,判断是否存在欺骗干扰.基于美国德克萨斯大学奥斯汀分校公开的场景四数据集进行试验,结果表明:与Ratio和ELP等典型SQM算法相比,在不同虚警率条件下,所提出SPCM算法兼具高检测概率和快速预警响应时间性能.展开更多
文摘Vehicle reidentification is an elegant solution for gathering several pieces of valuable traffic information, e.g., space mean speed, travel time, vehicle tracking, and origin/destination data. Recently, a number of vehiclereidentification algorithms utilizing inductive loop signals have been proposed to take advantage of the widespread availability of loop detectors. These algorithms, however, all directly utilize the raw inductance signals for pattern matching and feature extraction without deconvolution. The raw loop signals are essentially a convolved output between the true vehicle inductance signature and the loop system function, and thus a deconvolution is needed in order to expose the detailed features of individual vehicles. The purpose of this paper is to present a recent investigation on restoration of true inductance signatures by applying a blind deconvolution process. The main advantage of blind deconvolution over the conventional deconvolution is that the computation does not require modeling of a precise loop-detector system function. Experimental results show that the proposed blind deconvolution reveals much more detailed features of inductance signals and, as a result, increases the vehicle reidentification accuracy.
文摘GNSS民用信号因其公开性和脆弱性易受外界欺骗干扰.作为欺骗干扰检测的有效方法,信号质量监测(signal quality monitoring,SQM)技术通过检测接收机跟踪环路早码、即时码、晚码(early late phase,ELP)的相关结果,与无欺骗时的相关特性对比,判断是否存在欺骗干扰.常规SQM算法仅利用ELP三个信息,检测性能受限,为此提出多相关器联合功率(SQM detection of power combined Multi-correlator groups,SPCM)算法.以ELP之间多个等间隔相关器输出功率的加权为检测量,且取相关时刻与即时码时间差的反比为加权系数;进一步分析检测量的概率分布特性,并基于Neyman-Pearson理论确定最佳检测阈值,通过比较检测量与检测阈值的大小,判断是否存在欺骗干扰.基于美国德克萨斯大学奥斯汀分校公开的场景四数据集进行试验,结果表明:与Ratio和ELP等典型SQM算法相比,在不同虚警率条件下,所提出SPCM算法兼具高检测概率和快速预警响应时间性能.
