The growing demand for radio frequency (RF) spectrum access poses new challenges for next-generation radar systems. To operate in a crowded electromagnetic environment, radars must coexist with other RF emitters while maintaining system performance. This work evaluates the performance of a spectrum sharing cognitive radar system, which predicts and avoids RF interference (RFI) in real time. The system applies a cognitive perception-action cycle that senses RFI, learns RFI behavior over time, and adapts the radar's frequency band of operation. Through cognition, the system learns a stochastic model describing RF activity. This model allows the cognitive radar to predict RF activity in real time and share the spectrum with emitters, such as communication systems. A set of synthetic and measured interference signals are used to evaluate the performance of this predictive spectrum sharing scheme. This work assesses the impact of RFI on the cognitive radar's range profile with respect to variation in RF environment. The system demonstrates accurate avoidance of deterministic RFI with a degradation in spectrum sharing efficiency as variability over time increases.

中文翻译：

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认知雷达实时预测频谱共享的评估

对射频（RF）频谱访问的需求不断增长，对下一代雷达系统提出了新的挑战。为了在拥挤的电磁环境中运行，雷达必须与其他RF发射器共存，同时保持系统性能。这项工作评估了频谱共享认知雷达系统的性能，该系统实时预测并避免了RF干扰（RFI）。该系统采用认知感知-动作周期，该周期感知RFI，了解RFI随时间的变化，并适应雷达的工作频段。通过认知，系统学习描述RF活动的随机模型。该模型允许认知雷达实时预测RF活动并与发射器（例如通信系统）共享频谱。一组合成的和测量的干扰信号用于评估此预测频谱共享方案的性能。这项工作评估了射频干扰对RF环境变化的影响，RFI对认知雷达的测距曲线的影响。该系统演示了随着时间变化性的增加，准确避免确定性RFI以及频谱共享效率下降的问题。