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A What-and-Where Fusion Neural Network for Recognition and Tracking of Multiple Radar Emitters

机译:全方位融合神经网络,用于识别和跟踪多个雷达辐射源

摘要

A neural network recognition and tracking system is proposed for classification of radar pulses in autonomous Electronic Support Measure systems. Radar type information is combined with position-specific information from active emitters in a scene. Type-specific parameters of the input pulse stream are fed to a neural network classifier trained on samples of data collected in the field. Meanwhile, a clustering algorithm is used to separate pulses from different emitters according to position-specific parameters of the input pulse stream. Classifier responses corresponding to different emitters are separated into tracks, or trajectories, one per active emitter, allowing for more accurate identification of radar types based on multiple views of emitter data along each emitter trajectory. Such a What-and-Where fusion strategy is motivated by a similar subdivision of labor in the brain. The fuzzy ARTMAP neural network is used to classify streams of pulses according to radar type using their functional parameters. Simulation results obtained with a radar pulse data set indicate that fuzzy AIUMAP compares favorably to several other approaches when performance is measured in terms of accuracy and computational complexity. Incorporation into fuzzy ARTMAP of negative match tracking (from ARTMAP-IC) facilitated convergence during training with this data set. Other modifications improved classification of data that include missing input pattern components and missing training classes. Fuzzy ARTMAP was combined with a bank of Kalman filters to group pulses transmitted from different emitters based on their position-specific parameters, and with a module to accumulate evidence from fuzzy ARTMAP responses corresponding to the track defined for each emitter. Simulation results demonstrate that the system provides a high level of performance on complex, incomplete and overlapping radar data.
机译:提出了一种神经网络识别和跟踪系统,用于自主电子支持测量系统中的雷达脉冲分类。雷达类型信息与场景中活动发射器的位置特定信息结合在一起。输入脉冲流的特定于类型的参数被馈送到在现场收集的数据样本上训练的神经网络分类器。同时,使用聚类算法根据输入脉冲流的位置特定参数将来自不同发射器的脉冲分开。对应于不同发射器的分类器响应被划分为每个活动发射器一个的轨迹或轨迹,从而基于沿着每个发射器轨迹的多个发射器数据视图,可以更准确地识别雷达类型。这种“在哪里”融合策略是由大脑中类似的劳动分部驱动的。模糊ARTMAP神经网络用于根据雷达类型使用其功能参数对脉冲流进行分类。利用雷达脉冲数据集获得的仿真结果表明,当根据准确性和计算复杂性来衡量性能时,模糊AIUMAP与其他几种方法相比具有优势。将否定匹配跟踪(来自ARTMAP-IC)并入模糊ARTMAP中有助于在训练期间使用该数据集进行收敛。其他修改改进了数据分类,其中包括缺少输入模式成分和缺少培训课程。模糊ARTMAP与一组Kalman滤波器组合以根据不同发射器的位置特定参数对从其发射的脉冲进行分组,并与一个模块结合,以收集来自模糊ARTMAP响应的证据,该模糊ARTMAP响应对应于为每个发射器定义的轨迹。仿真结果表明,该系统在复杂,不完整和重叠的雷达数据上具有很高的性能。

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