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.

The employment of Time-Interleaved Analog-to-Digital Converter (TIADC) technology in wideband digital radar (WDR) receivers enables the adaptation to the ever-increasing bandwidth demands. Nevertheless, the performance of TIADC is significantly affected by channel mismatch errors. This paper presents a real-time calibration method for TIADCs utilized in WDR. The calibration process involves two steps: broadband mismatches estimation and compensation, respectively. Firstly, a novel approach is proposed to estimate broadband mismatches by utilizing the transmitted signal - Linear Frequency Modulated (LFM) signal - as a reference signal. Compared with conventional techniques, this new approach offers lower computational complexity and more streamlined hardware circuitry. Secondly, in mismatches compensation step, an optimized perfect reconstruction (OPR) structure is implemented to decrease computational resource usage and increase processing speed. Finally, the compensation of mismatches are implemented in the field programmable gate array (FPGA). The speed of error compensation is equal to the data generation rate, enabling the TIADC system to transmit the corrected radar echo data to the signal processing unit in real-time. The experiment results show that the spurious-free dynamic range (SFDR) is improved by more than 15dB and the signal to noise and distortion ratio (SNDR) is improved by more than 10dB after real-time calibration.