The design of periodic binary sequence sets for multiple-input multiple-output (MIMO) phase-modulated continuous wave (PMCW) automotive radar is crucial to its performance. While the corresponding optimization problem is NP-hard and high-dimensional, and many existing computational algorithms are not suitable for designing binary sequence sets with long lengths due to their high computational complexities. In this paper, we develop a new algorithm for designing periodic binary sequence sets with good auto- and cross-correlation properties for MIMO PMCW radar. Fast Fourier transform (FFT) can be used to implement the proposed algorithm, making the algorithm computationally efficient and hence can be used to design binary sequence sets with quite long lengths. Several numerical examples are provided to demonstrate the performance of the proposed algorithm.
Automatic modulation classification (AMC) plays an important role in the development of cognitive radio and cognitive radar systems. Due to the distinct aims of radar and communication systems, their commonly used modulation techniques may differ significantly. However, AMC in the radar and communication domains can be closely aligned. Considering the widespread applications of deep learning architectures, particularly, convolutional neural networks (CNN) in classification problems, we propose a CNN-based framework for the joint classification of communication and radar signals. In the proposed framework, a CNN is first trained by signals in the in- phase and quadrature domain and then another CNN is trained by using constellation diagrams to differentiate between close modulations. A publicly available benchmark dataset, which consists of different modulation schemes in the communication domain, has been augmented with our simulated linear frequency modulated radar signals under the same noise condition to validate the accuracy of the proposed framework.
Radars are commonly used in automotive applications to provide information on a target's location and velocity simultaneously. The multiple-input and multiple-output technology has been applied to improve spatial awareness, but impacts the Doppler sampling rate and causes velocity ambiguity. An incorrect velocity indication can pose significant safety risks in the automotive industry. To address this, we propose a complex-valued neural network to retrieve the actual velocity from aliased Doppler response by parsing signal magnitudes and phases. On the artificial data, it achieves an accuracy of 99.3%, outperforming the conventional methods on the same data.
This paper presents a method to model monolithically integrated photonic radar transceiver (TRX) with optical local oscillator (LO) distribution in silicon germanium (SiGe) electronic photonic integrated circuits (EPICs). The model proposed approximates the behavior of the nonlinear scattering (S)-parameters and noise Figure of each building block of the TRX chipset by Laplace polynomials and hyperbolic tangent functions. The modular approach of the model allows to optimize hardware components with respect to the entire TRX system, and fault identification with reduced computational effort. The proposed method is validated using the first monolithically integrated photonic radar transceiver chipset and shows excellent agreement with the post layout simulation results and, including the photodiode (PD) bandwidth (BW) degradation, also with the measurements.
This paper describes the design of a high-gain ultra-wideband (UWB) linearly tapered slot PCB antenna for mm-wave radar applications. The antenna design was focused on matching differential microstrip to antenna input feed for the best VSWR. For better antenna parameters PTFE lens was added.The antenna is designed as a planar structure on a four-layer PCB on substrate Isola Astra® MT77 with dielectric constant D k = 3.0. The dimensions of the antenna without a lens are 18.9x9 mm. The basic simulation of the antenna was performed. The results show the proposed antenna directivity, gain, and low VSWR over the operating frequency. The VSWR of the antenna is under 2, and the designed operation frequencies of an antenna are from 25 GHz to 85 GHz. The maximum achieved gain is 17.6 dBi at 65 GHz.
