Department of Electronic Engineering, Tsinghua University, Beijing 100084, China.
Sensors (Basel). 2010;10(11):10181-97. doi: 10.3390/s101110181. Epub 2010 Nov 11.
We address the problem of adaptive waveform design for extended target recognition in cognitive radar networks. A closed-loop active target recognition radar system is extended to the case of a centralized cognitive radar network, in which a generalized likelihood ratio (GLR) based sequential hypothesis testing (SHT) framework is employed. Using Doppler velocities measured by multiple radars, the target aspect angle for each radar is calculated. The joint probability of each target hypothesis is then updated using observations from different radar line of sights (LOS). Based on these probabilities, a minimum correlation algorithm is proposed to adaptively design the transmit waveform for each radar in an amplitude fluctuation situation. Simulation results demonstrate performance improvements due to the cognitive radar network and adaptive waveform design. Our minimum correlation algorithm outperforms the eigen-waveform solution and other non-cognitive waveform design approaches.
我们解决了认知雷达网络中扩展目标识别的自适应波形设计问题。将闭环主动目标识别雷达系统扩展到集中式认知雷达网络的情况，其中采用基于广义似然比(GLR)的序贯假设检验(SHT)框架。利用多个雷达测量的多普勒速度，计算每个雷达的目标方位角。然后，使用不同雷达视线（LOS）的观测值更新每个目标假设的联合概率。基于这些概率，提出了一种最小相关算法，以在幅度波动情况下自适应地设计每个雷达的发射波形。仿真结果表明，认知雷达网络和自适应波形设计可以提高性能。我们的最小相关算法优于特征波形解和其他非认知波形设计方法。
