实时频谱共享雷达的变点检测
Changepoint Detection for Real-Time Spectrum Sharing Radar
摘要：
雷达必须适应不断变化的环境，我们提出了变点检测作为这样做的方法。在无线电频率日益拥挤的世界中，雷达必须适应以避免干扰。许多雷达系统在频谱共享时采用预测动作周期来主动确定传输模式。该方法构建并实现了一个环境模型来预测未使用的频率，然后在此预测的可用性中进行传输。对于这些选择策略，性能直接依赖于基本环境模型的质量。为了跟上不断变化的环境，这些模型可以采用变点检测。变点检测是识别从中提取数据的分布中的突然变化或变点。这些信息允许模型丢弃来自先前分发的“垃圾”数据，这些数据与环境的当前状态无关。本文将贝叶斯在线变点检测（BOCD）应用于感知和预测算法中，以提高模型的准确性，改善其性能。在频谱共享的背景下，这些变化点表示离开和进入频谱环境的干扰源。增加的变化点检测允许动态和鲁棒的频谱共享，即使干扰模式急剧变化。BOCD是特别有利的，因为它能够在线检测变点，允许模型不断更新，因为数据收集。这种策略也可以应用于在不断变化的环境中创建模型的许多其他预测算法。
英文摘要：
Radar must adapt to changing environments, and we propose changepoint detection as a method to do so. In the world of increasingly congested radio frequencies, radars must adapt to avoid interference. Many radar systems employ the prediction action cycle to proactively determine transmission mode while spectrum sharing. This method constructs and implements a model of the environment to predict unused frequencies, and then transmits in this predicted availability. For these selection strategies, performance is directly reliant on the quality of the underlying environmental models. In order to keep up with a changing environment, these models can employ changepoint detection. Changepoint detection is the identification of sudden changes, or changepoints, in the distribution from which data is drawn. This information allows the models to discard "garbage" data from a previous distribution, which has no relation to the current state of the environment. In this work, bayesian online changepoint detection (BOCD) is applied to the sense and predict algorithm to increase the accuracy of its models and improve its performance. In the context of spectrum sharing, these changepoints represent interferers leaving and entering the spectral environment. The addition of changepoint detection allows for dynamic and robust spectrum sharing even as interference patterns change dramatically. BOCD is especially advantageous because it enables online changepoint detection, allowing models to be updated continuously as data are collected. This strategy can also be applied to many other predictive algorithms that create models in a changing environment.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
