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摘要翻译：
本文提出了一种度量两个有限航迹集（航迹是真实目标的路径或估计目标的路径）之间距离的新度量。该度量与Schuhmacher等人设计的最优子模式分配(OSPA)度量基于相同的原理。然而，重要的是，新的度量度量两个有限的轨道集之间的距离，而OSPA度量度量两个有限的目标状态集之间的距离。新的OSPAMT度量还考虑了由漏检和误报警引起的假航迹、漏检航迹和多航迹分配给一个航迹情况的性质，使两个有限航迹集上航迹之间的所有距离最小化，从而使多目标跟踪(MTT)算法的性能评估比现有的度量（如OSPA度量和Ristic等人提出的度量两个有限标记目标状态集之间距离的增强OSPAT度量）更加全面和准确。
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英文标题：
《Optimal Subpattern Assignment Metric for Multiple Tracks (OSPAMT Metric)》
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作者：
Tuyet Vu and Rob Evans
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最新提交年份：
2019
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分类信息：
一级分类：Electrical Engineering and Systems Science 电气工程与系统科学
二级分类：Signal Processing 信号处理
分类描述：Theory, algorithms, performance analysis and applications of signal and data analysis, including physical modeling, processing, detection and parameter estimation, learning, mining, retrieval, and information extraction. The term "signal" includes speech, audio, sonar, radar, geophysical, physiological, (bio-) medical, image, video, and multimodal natural and man-made signals, including communication signals and data. Topics of interest include: statistical signal processing, spectral estimation and system identification; filter design, adaptive filtering / stochastic learning; (compressive) sampling, sensing, and transform-domain methods including fast algorithms; signal processing for machine learning and machine learning for signal processing applications; in-network and graph signal processing; convex and nonconvex optimization methods for signal processing applications; radar, sonar, and sensor array beamforming and direction finding; communications signal processing; low power, multi-core and system-on-chip signal processing; sensing, communication, analysis and optimization for cyber-physical systems such as power grids and the Internet of Things.
信号和数据分析的理论、算法、性能分析和应用，包括物理建模、处理、检测和参数估计、学习、挖掘、检索和信息提取。“信号”一词包括语音、音频、声纳、雷达、地球物理、生理、（生物）医学、图像、视频和多模态自然和人为信号，包括通信信号和数据。感兴趣的主题包括：统计信号处理、谱估计和系统辨识；滤波器设计；自适应滤波/随机学习；（压缩）采样、传感和变换域方法，包括快速算法；用于机器学习的信号处理和用于信号处理应用的机器学习；网络与图形信号处理；信号处理中的凸和非凸优化方法；雷达、声纳和传感器阵列波束形成和测向；通信信号处理；低功耗、多核、片上系统信号处理；信息物理系统的传感、通信、分析和优化，如电网和物联网。
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英文摘要：
In this paper, we propose a new metric which measures the distance between two finite sets of tracks (a track is a path of either a real or estimated target). This metric is based on the same principle as the Optimal Subpattern Assignment (OSPA) metric devised by Schuhmacher et al. Importantly however, the new metric measures the distance between two finite sets of tracks whereas the OSPA metric measures the distance between two finite sets of target states. By also considering the properties of false tracks, missed tracks and many tracks assigned to one track situations caused by missed detections and false alarms, the minimization of all distances between tracks across two finite sets of tracks employed by the new OSPAMT metric enables performance evaluation of multi-target tracking (MTT) algorithms in a more comprehensive and accurate manner than existing metrics such as the OSPA metric and the enhanced OSPAT metric introduced by Ristic et al which measures the distance between two finite sets of labeled target states.
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PDF链接：
https://arxiv.org/pdf/1808.02242
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