In this paper, we propose a cognitive radar paradigm based on frequency diverse array (FDA), which allows flexible spectrum control via element-wise transmit power allocation. As an emerging array technique, FDA differs from conventional phased array (PA) in that it imposes an additional frequency increment across the array elements. The use of frequency increment provides the FDA radar with the ability of flexible spectrum adjustment. We propose the cognitive FDA radar for target tracking in spectrally dense scenarios. Two optimization criteria, i.e., signal-to-interference-plus-noise ratio (SINR) maximization and Cramér-Rao bound (CRB) minimization, are employed to adaptively update the array weight vector for power allocation at each transmission. Numerical results show that the proposed cognitive FDA radar can adjust the signal spectrum to avoid the interfered frequencies for better output SINR. The resulting tracking errors of FDA radar with adaptive power allocation are lower than that for fixed power allocation. Moreover, the CRB criterion further improves the tracking performance.

在本文中，我们提出了一种基于频率变化阵列（FDA）的认知雷达范式，该范式允许通过逐元素发送功率分配进行灵活的频谱控制。作为一种新兴的阵列技术，FDA与常规相控阵（PA）的不同之处在于，它在整个阵列元件上施加了额外的频率增量。频率增量的使用为FDA雷达提供了灵活的频谱调整能力。我们建议使用认知FDA雷达在光谱密集的情况下进行目标跟踪。两种优化标准，即信号干扰加噪声比（SINR）最大化和Cramér-Rao界限（CRB）最小化，可用于自适应地更新阵列权重矢量，以便在每次传输时进行功率分配。数值结果表明，所提出的认知FDA雷达可以调整信号频谱，避免干扰频率，从而获得更好的输出SINR。自适应功率分配的FDA雷达所产生的跟踪误差要低于固定功率分配的跟踪误差。此外，CRB标准进一步提高了跟踪性能。