Department of Electronic Engineering, Tsinghua University, Beijing 100084, China.
Sensors (Basel). 2013 Apr 22;13(4):5347-67. doi: 10.3390/s130405347.
We study the Cramer-Rao bounds of parameter estimation and coherence performance for the next generation radar (NGR). In order to enhance the performance of NGR, the signal model of NGR with master-slave architecture based on a single pulse is extended to the case of pulse trains, in which multiple pulses are emitted from all sensors and then integrated spatially and temporally in a unique master sensor. For the MIMO mode of NGR where orthogonal waveforms are emitted, we derive the closed-form Cramer-Rao bound (CRB) for the estimates of generalized coherence parameters (GCPs), including the time delay differences, total phase differences and Doppler frequencies with respect to different sensors. For the coherent mode of NGR where the coherent waveforms are emitted after pre-compensation using the estimates of GCPs, we develop a performance bound of signal-to-noise ratio (SNR) gain for NGR based on the aforementioned CRBs, taking all the estimation errors into consideration. It is shown that greatly improved estimation accuracy and coherence performance can be obtained with pulse trains employed in NGR. Numerical examples demonstrate the validity of the theoretical results.
我们研究了下一代雷达（NGR）的参数估计和相干性能的克拉美-罗界。为了提高 NGR 的性能，基于单个脉冲的主从架构的 NGR 信号模型被扩展到脉冲串的情况，其中多个脉冲从所有传感器发射，然后在一个唯一的主传感器中进行空间和时间上的集成。对于正交波形发射的 NGR 的 MIMO 模式，我们推导出了广义相干参数（GCP）估计的闭式克拉美-罗界（CRB），包括相对于不同传感器的时延差、总相差和多普勒频率。对于使用 GCP 估计值进行预补偿后发射相干波形的 NGR 的相干模式，我们基于上述 CRB 开发了一种 NGR 的信噪比（SNR）增益的性能界，考虑了所有的估计误差。结果表明，采用脉冲串可以大大提高 NGR 的估计精度和相干性能。数值示例验证了理论结果的有效性。
