基于GMTI测量的意图推断和句法跟踪
Intent Inference and Syntactic Tracking with GMTI Measurements
摘要：
在传统的目标跟踪系统中，人类操作员使用估计的目标轨迹来对目标行为/意图进行更高级别的推断。本文开发了语法过滤算法，协助人类操作员提取空间模式的目标轨迹，以确定可疑/异常的空间轨迹。目标的空间轨迹由随机上下文无关文法（SCFG）和切换模式状态空间模型建模。贝叶斯过滤算法的随机上下文无关文法提取的句法结构，并说明了一个地面运动目标指示器（GMTI）雷达的例子。使用DRDC渥太华的X波段宽带实验机载雷达（XWEAR）收集的实验数据的算法的性能进行了测试。
英文摘要：
In conventional target tracking systems, human operators use the estimated target tracks to make higher level inference of the target behaviour/intent. This paper develops syntactic filtering algorithms that assist human operators by extracting spatial patterns from target tracks to identify suspicious/anomalous spatial trajectories. The targets' spatial trajectories are modeled by a stochastic context free grammar (SCFG) and a switched mode state space model. Bayesian filtering algorithms for stochastic context free grammars are presented for extracting the syntactic structure and illustrated for a ground moving target indicator (GMTI) radar example. The performance of the algorithms is tested with the experimental data collected using DRDC Ottawa's X-band Wideband Experimental Airborne Radar (XWEAR).
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
