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首页> 外文会议>Conference on Signal and Data Processing of Small Targets 2002, Apr 2-4, 2002, Orlando, USA >On Expectation Maximization applied to GMTI Convoy Tracking
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On Expectation Maximization applied to GMTI Convoy Tracking

机译：期望最大化应用于GMTI车队跟踪

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摘要

Collectively moving ground targets are typical of a military ground situation and have to be treated as separate aggregated entities. For a long-range ground surveillance application with airborne GMTI radar we in particular address the task of track maintenance for ground moving convoys consisting of a small number of individual vehicles. In the proposed approach the identity of the individual vehicles within the convoy is no longer stressed. Their kinematical state vectors are rather treated as internal degrees of freedom characterizing the convoy, which is considered as a collective unit. In this context, the Expectation Maximization technique (EM), originally developed for incomplete data problems in statistical inference and first applied to tracking applications by STREIT et al. [1, 2], seems to be a promising approach. We suggest to embed the EM algorithm into a more traditional Bayesian tracking framework for dealing with false or unwanted sensor returns. The proposed distinction between 'external' and 'internal' data association conflicts (i.e. those among the convoy vehicles) should also enable the application of sequential track extraction techniques introduced by VAN KEUK [3] for aircraft formations, providing estimates of the number of the individual convoy vehicles involved. Even with sophisticated signal processing methods (STAP: Space-Time Adaptive Processing), ground moving vehicles can well be masked by the sensor specific clutter notch (Doppler blinding). This physical phenomenon results in interfering fading effects, which can well last over a longer series of sensor updates and therefore will seriously affect the track quality unless properly handled. Moreover, for ground moving convoys the phenomenon of Doppler blindness often superposes the effects induced by the finite resolution capability of the sensor. In many practical cases a separate modeling of resolution phenomena for convoy targets can therefore be omitted, provided the GMTI detection model is used. As an illustration we consider the contribution of the proposed GMTI sensor model to the problem of early recognition of a stopping convoy.
机译：集体移动的地面目标是军事地面情况的典型特征，必须将其视为单独的汇总实体。对于带有机载GMTI雷达的远程地面监视应用，我们特别解决了由少量单个车辆组成的地面移动车队的轨道维护任务。在所提出的方法中，不再强调车队内的各个车辆的身份。他们的运动状态向量被视为车队的内部自由度，车队被认为是一个集体单位。在这种情况下，期望最大化技术（EM）最初是为统计推断中的不完整数据问题而开发的，最早由STREIT等人应用于跟踪应用程序。 [1，2]，似乎是一种很有前途的方法。我们建议将EM算法嵌入到更传统的贝叶斯跟踪框架中，以处理错误或不需要的传感器返回。拟议中的“外部”和“内部”数据关联冲突之间的区别（即车队之间的冲突）还应使VAN KEUK [3]引入的用于飞机编队的顺序航迹提取技术的应用，可提供对飞机编队数量的估计。涉及的个别护卫车。即使采用复杂的信号处理方法（STAP：时空自适应处理），地面行驶的车辆也可以被传感器特有的杂波陷波（多普勒致盲）掩盖。这种物理现象会导致干扰衰落效应，这种衰落效应会持续较长的传感器更新周期，因此，除非正确处理，否则会严重影响轨道质量。此外，对于地面运输车队，多普勒失明现象通常会叠加传感器有限分辨率能力引起的影响。因此，在许多实际情况下，如果使用了GMTI检测模型，则可以省略针对车队目标的分离现象的单独建模。作为说明，我们考虑了提议的GMTI传感器模型对停车车队的早期识别问题的贡献。

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