This paper investigates the design of waveforms for multiple-input multiple-output (MIMO) radar systems that can exploit multipath returns to enhance target detection performance. By making reasonable use of multipath information in the waveform design, MIMO radar can effectively improve the signal-to-interference and noise ratio (SINR) of the receiver under a constant modulus (CM) constraint. However, optimizing the waveform design under these constraints is a challenging non-linear and non-convex problem that cannot be easily solved using traditional methods. To overcome this challenge, we proposed a novel waveform design method for MIMO radar in multipath scenarios based on deep learning. Specifically, we leveraged the powerful nonlinear fitting ability of neural networks to solve the non-convex optimization problem. First, we constructed a deep residual network and transform the CM constraint into a phase sequence optimization problem. Next, we used the constructed waveform optimization design problem as the loss function of the network. Finally, we used the adaptive moment estimation (Adam) optimizer to train the network. Simulation results demonstrated that our proposed method outperformed existing methods by achieving better SINR values for the receiver.

Video generation methods have important implications for the fields of visual control and decision-making. Current research often use the Fréchet Video Distance (FVD) as an evaluation metric for machine-generated video. However, FVD has not been thoroughly verified on non-visible light sources, especially the widely used infrared light. Therefore, there is an urgent need to use real infrared video data to test the reliability and generalization ability of FVD. Toward that goal, we first collected mainstream infrared video datasets and added various types of noise to synthesize infrared videos of different quality levels. Experiments based on synthetic dataset demonstrate the feasibility of using FVD to assess the quality of infrared video. Next, we trained the Pix2PixGAN network using a dataset containing aligned visible and infrared image pairs. The trained model can generate videos of different quality levels in the infrared light domain. With the generated infrared videos, our experiments show that FVD is able to distinguish the quality differences of different infrared videos. In particular, we found that the lack of labeled infrared dataset and relatively small dataset size of infrared videos has a negative impact on calculating credible FVD values. This is because extracting effective infrared video features remains a difficult problem. Our experimental results suggest that infrared video features can be extracted using large-scale visible light video pre-trained I3D models, and their calculated FVD values are even better than those directly using infrared video pre-trained I3D models. Our study provides a basis for using FVD to evaluate the quality of machine-generated videos under multispectral conditions.