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Automatic target recognition organized via jump-diffusion algorithms

机译:通过跳跃扩散算法组织的自动目标识别

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Proposes a framework for simultaneous detection, tracking, and recognition of objects via data fused from multiple sensors. Complex dynamic scenes are represented via the concatenation of simple rigid templates. The variability of the infinity of pose is accommodated via the actions of matrix Lie groups extending the templates to individual instances. The variability of target number and target identity is accommodated via the representation of scenes as unions of templates of varying types, with the associated group transformations of varying dimension. We focus on recognition in the air-to-ground and ground-to-air scenarios. The remote sensing data is organized around both the coarse scale associated with detection as provided by tracking and range radars, along with the fine scale associated with pose and identity supported by high-resolution optical, forward looking infrared and delay-Doppler radar imagers. A Bayesian approach is adopted in which prior distributions on target scenarios are constructed via dynamical models of the targets of interest. These are combined with physics-based sensor models which define conditional likelihoods for the coarse/fine scale sensor data given the underlying scene. Inference via the Bayes posterior is organized around a random sampling algorithm based on jump-diffusion processes. New objects are detected and object identities are recognized through discrete jump moves through parameter space, the algorithm exploring scenes of varying complexity as it proceeds. Between jumps, the scale and rotation group transformations are generated via continuous diffusions in order to smoothly deform templates into individual instances of objects.
机译:提出了一个框架,用于通过多个传感器融合的数据同时检测,跟踪和识别对象。复杂的动态场景通过简单的刚性模板的连接来表示。姿势无限性的可变性通过矩阵李群将模板扩展到各个实例的作用来适应。目标数量和目标身份的可变性是通过将场景表示为各种类型的模板的并集来实现的,并且具有关联的维度变化的组转换。我们专注于空对地和地对空场景中的识别。遥感数据围绕由跟踪雷达和测距雷达提供的与检测相关的粗略尺度,以及与高分辨率光学,前视红外和延迟多普勒雷达成像仪支持的与姿态和身份相关的精细尺度进行组织。采用贝叶斯方法,其中通过感兴趣的目标的动力学模型构造目标情景的先验分布。这些与基于物理的传感器模型相结合,该模型为给定基础场景的粗略/精细比例传感器数据定义了条件似然。通过贝叶斯后验的推理是围绕基于跳跃扩散过程的随机采样算法进行的。通过在参数空间中的离散跳跃运动来检测新物体并识别物体身份,该算法在进行过程中探索复杂程度各不相同的场景。在跳跃之间,通过连续扩散生成比例和旋转组转换,以使模板平滑变形为对象的各个实例。

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