认知偏差是认知电子战中的客观存在。基于动态博弈方法，针对认知雷达对抗过程中因不完整信息和测量误差导致的认知偏差，研究雷达对抗建模方法：以雷达抗干扰改善因子和干扰机干扰效益因子计算博弈双方的效益，采用精炼贝叶斯均衡建立动态雷达对抗模型，分析认知偏差造成的影响。仿真实验结果证明了所提方法的有效性。

针对低信噪比条件下，现有的雷达辐射源信号识别方法存在识别正确率低、时效性差的问题，提出了一种基于压缩残差网络的雷达辐射源信号识别方法。首先，利用Choi-Williams分布的时频分析方法将时域信号转换为二维时频图像；然后，根据应用场景特点，选择卷积神经网络（Convolutional Neural Networks, CNN）“压缩”范围；最后，构建压缩残差网络来自动提取图像特征并完成分类。仿真实验结果表明，在同等体量的设计下，与当前较为常用的标准CNN以及ResNet模型相比，所提模型能够降低信号识别运行时间约88%，在信噪比为−14 dB条件下对14种雷达辐射源信号的平均识别率高约5%。提供了一种高效的雷达辐射源信号智能识别方法，具有潜在的工程应用前景。

This paper experimentally demonstrates a distributed photonics-based W-band integrated sensing and communication (ISAC) system, in which radar sensing can aid the communication links in alignment and data rate estimation. As a proof-of-concept, the ISAC system locates the users, guides the alignment, and sets a communication link with the estimated highest data rate. A peak net data rate of 68.6 Gbit/s and a target sensing with a less-than-1-cm error and a sub-2-cm resolution have been tested over a 10-km fiber and a 1.15-m free space transmission in the photonics-based W-band ISAC system. The achievable net data rates of the users at different locations estimated by sensing are experimentally verified.

Recently, there has been increased attention toward 3D imaging using single-pixel single-photon detection (also known as temporal data) due to its potential advantages in terms of cost and power efficiency. However, to eliminate the symmetry blur in the reconstructed images, a fixed background is required. This paper proposes a fusion-data-based 3D imaging method that utilizes a single-pixel single-photon detector and millimeter-wave radar to capture temporal histograms of a scene from multiple perspectives. Subsequently, the 3D information can be reconstructed from the one-dimensional fusion temporal data by using an artificial neural network. Both the simulation and experimental results demonstrate that our fusion method effectively eliminates symmetry blur and improves the quality of the reconstructed images.

针对共振荧光测风雷达的探测需求，设计和研制了种子注入的Nd∶YAG电光调Q单频1 116 nm脉冲激光器，采用带偏置电压反馈的扫描-触发谐振探测技术，建立了1 116 nm单纵模振荡，并对中心频率稳定性进行分析。最终实现输出1 116 nm激光单脉冲能量6.48 mJ，重复频率60 Hz，输出线宽为33.2 MHZ，脉冲宽度为75.7 ns。通过波长计测量输出光波长，波长稳定在1 116.297 9 nm附近。利用外差拍频，测定120 min中心频率稳定性（均方根）为818.3 kHz。该激光器输出光通过三倍频可变换至372 nm，作为铁共振荧光多普勒测风激光雷达的发射光源，频率抖动量对应的风速测量误差小于1 m/s。

为避免双天线InSAR系统基线测量动态监测过程中引入误差，影响基线测量精度，对基线长度与角度测量过程中的可能误差进行定性与定量分析。采用坐标变换法建立系统误差数学模型，明确测量系统的误差来源。提出误差灵敏度概念，对误差项进行定量计算，并对每一自由度的误差源进行灵敏度分析，进一步形成综合误差定量合成结果。根据误差灵敏度系数给出一组精度反演误差分配案例。最后，依据蒙特卡洛法在MATLAB平台闭环验证精度量化分配方法的可行性与稳定性。仿真分析结果表明，激光视觉三轴位置的测量精度要求为300 μm（3σ），三轴角度的测量精度要求为50''（3σ），即可满足基线长度精度1 mm（1.6σ），基线角度精度2''（1.6σ）。通过本方法可由测量环境条件输入直接获得基线测量的精度，根据灵敏度系数对误差分配进行反演可以得到系统最优布局，其结果可为测量系统的方案设计与精度分解提供有效指导。