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Spectral Prediction and Notching of RF Emitters for Cognitive Radar Coexistence

机译：认知雷达共存的射频发射器的光谱预测和陷波

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The increasing demand for radio frequency (RF) spectrum requires radars to coexist with interfering RF emitters. Innovations in waveform design paired with dynamic spectrum access techniques allow for efficient spectrum sharing for radar. This work evaluates a recently developed real-time implementation of spectrum sharing cognitive radar using commercial-of-the-shelf (COTS) hardware. Prediction of coexisting RF emitter frequencies informs the design of spectrally notched FM noise waveforms on transmit. Waveform notches are optimized on a pulse-to-pulse basis while accounting for the zero-order hold model inherent to lower fidelity digital-to-analog converters to ensure desired reconstruction. The radar system employs cognition to learn and predict RF emitter activity via a stochastic model-based approach. Initially, passive spectrum observations are used to estimate a stochastic model which is then exploited to predict the likelihood of future RF activity. The benefits and limitations of this sense-predict-and-notch (SPAN) approach are evaluated using a set of synthetic interference scenarios in real-time.
机译：对射频（RF）频谱的需求不断增长，要求雷达与干扰性RF发射器共存。波形设计的创新与动态频谱访问技术相结合，可实现雷达的有效频谱共享。这项工作评估了使用现成的商用（COTS）硬件最近开发的频谱共享认知雷达的实时实现。共存的RF发射器频率的预测有助于设计发射时频谱上陷波的FM噪声波形。波形陷波在脉冲到脉冲的基础上进行了优化，同时考虑了低保真度数模转换器固有的零阶保持模型，以确保所需的重构。雷达系统采用认知，通过基于随机模型的方法来学习和预测RF发射器的活动。最初，无源频谱观测用于估计随机模型，然后利用该模型预测未来RF活动的可能性。使用一组综合干扰场景，实时评估了这种“感觉预测和陷波”（SPAN）方法的优点和局限性。

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