MIMO Radar Parallel Simulation System Based on CPU/GPU Architecture

The data volume and computation task of MIMO radar is huge; a very high-speed computation is necessary for its real-time processing. In this paper, we mainly study the time division MIMO radar signal processing flow, propose an improved MIMO radar signal processing algorithm, raising the MIMO radar algorithm processing speed combined with the previous algorithms, and, on this basis, a parallel simulation system for the MIMO radar based on the CPU/GPU architecture is proposed. The outer layer of the framework is coarse-grained with OpenMP for acceleration on the CPU, and the inner layer of fine-grained data processing is accelerated on the GPU. Its performance is significantly faster than the serial computing equipment, and satisfactory acceleration effects have been achieved in the CPU/GPU architecture simulation. The experimental results show that the MIMO radar parallel simulation system with CPU/GPU architecture greatly improves the computing power of the CPU-based method. Compared with the serial sequential CPU method, GPU simulation achieves a speedup of 130 times. In addition, the MIMO radar signal processing parallel simulation system based on the CPU/GPU architecture has a performance improvement of 13%, compared to the GPU-only method.

Artificial Neural Networks and Deep Learning Techniques Applied to Radar Target Detection: A Review

Radar target detection (RTD) is a fundamental but important process of the radar system, which is designed to differentiate and measure targets from a complex background. Deep learning methods have gained great attention currently and have turned out to be feasible solutions in radar signal processing. Compared with the conventional RTD methods, deep learning-based methods can extract features automatically and yield more accurate results. Applying deep learning to RTD is considered as a novel concept. In this paper, we review the applications of deep learning in the field of RTD and summarize the possible limitations. This work is timely due to the increasing number of research works published in recent years. We hope that this survey will provide guidelines for future studies and applications of deep learning in RTD and related areas of radar signal processing.

Electromagnetic Occlusion Algorithm Based on FPGA and Panel Grouping and Its Optimization

With the development of digital signal processing and advanced algorithms, real-time signal processing based on FPGA and DSP is suitable for high-speed radar signal processing. With the rapid development of science and technology, war has entered the information age guided by high technology, and advanced science and technology has played a vital role in the trend of war. In recent years in the modern war, many countries invest a lot of research effort on the stealth technology, and advanced stealth technology can use a variety of technical means to alter or weaken the feature information of the target, confuse the enemy radar detection system effectively, reduce the chance of being detected to the largest extent, and prolong the lifecycle of aircraft and weapons. This research mainly discusses the electromagnetic occlusion algorithm and its optimization based on FPGA and panel grouping. The FPGA model selected for this study is XC6VLX240T-1FF1156I. Because the amount of data processed here is not very large, the cache part directly uses the on-chip storage resources of the FPGA, and the AD device is used to perform analog-to-digital/digital-to-analog conversion on the signal and perform digital up-down conversion. For a facet, it is necessary to first verify whether it is a bright facet and set the flag to mark it, then the facet needs to be occluded with the triangular facet marked as a bright facet, and all bright facets that have been marked need to be traversed. Open MP parallelization of the occlusion algorithm is as follows: The physical optics method is used to calculate the target RCS, and the focus of parallelism is placed on the part with a large amount of calculation. When using Open MP to design a program on a multicore computer, each group is assigned a thread to give full play to the core computing power. The total field is scattered and superimposed by each surface element. This part uses the parallel processing mode of Open MP, which allows the panel judgment in the group to be carried out at the same time. This part requires schedule to allocate resources and use different parallel mechanisms for different calculations to optimize debugging. In the angular range where there is multiple scattering at 0 ° ≤ φ ≤ 90 ° , the calculation results and the measurement results are in good agreement, and when the two planes are simulated with 1820 triangular faces, the fast multiple scattering in this paper only needs 4 minutes. This research has realized the general radar signal processing method based on FPGA structure, and the design has important engineering realization significance.

Reconstruction of signal phases for signals closer than the DFT frequency resolution

Radar signal processing is a promising tool for vital sign monitoring. For contactless observation of breathing and heart rate a precise measurement of the distance between radar antenna and the patient's skin is required. This results in the need to detect small movements in the range of 0.5 mm and below. Such small changes in distance are hard to be measured with a limited radar bandwidth when relying on the frequency based range detection alone. In order to enhance the relative distance resolution a precise measurement of the observed signal's phase is required. Due to radar reflections from surfaces in close proximity to the main area of interest the desired signal of the radar reflection can get superposed. For superposing signals with little separation in frequency domain the main lobes of their discrete Fourier transform (DFT) merge into a single lobe, so that their peaks cannot be differentiated. This paper evaluates a method for reconstructing the phase and amplitude of such superimposed signals.

A MIMO Radar Signal Processing Algorithm for Identifying Chipless RFID Tags

In this paper, the multiple-input, multiple-output (MIMO) radar signal processing algorithm is efficiently employed as an anticollision methodology for the identification of multiple chipless radio-frequency identification (RFID) tags. Tag-identifying methods for conventional chipped RFID tags rely mostly on the processing capabilities of application-specific integrated circuits (ASICs). In cases where more than one chipless tag exists in the same area, traditional methods are not sufficient to successfully read and distinguish the IDs, while the direction of each chipless tag can be obtained by applying MIMO technology to the backscattering signal. In order to read the IDs of the tags, beamforming is used to change the main beam direction of the antenna array and to receive the tag backscattered signal. On this basis, the RCS of the tags can be retrieved, and associated IDs can be identified. In the simulation, two tags with different IDs were placed away from each other. The IDs of the tags were successfully identified using the presented algorithm. The simulation result shows that tags with a distance of 0.88 m in azimuth can be read by a MIMO reader with eight antennas from 3 m away.

FPGA Implementation of an Efficient FFT Processor for FMCW Radar Signal Processing

This paper presents the design and implementation results of an efficient fast Fourier transform (FFT) processor for frequency-modulated continuous wave (FMCW) radar signal processing. The proposed FFT processor is designed with a memory-based FFT architecture and supports variable lengths from 64 to 4096. Moreover, it is designed with a floating-point operator to prevent the performance degradation of fixed-point operators. FMCW radar signal processing requires windowing operations to increase the target detection rate by reducing clutter side lobes, magnitude calculation operations based on the FFT results to detect the target, and accumulation operations to improve the detection performance of the target. In addition, in some applications such as the measurement of vital signs, the phase of the FFT result has to be calculated. In general, only the FFT is implemented in the hardware, and the other FMCW radar signal processing is performed in the software. The proposed FFT processor implements not only the FFT, but also windowing, accumulation, and magnitude/phase calculations in the hardware. Therefore, compared with a processor implementing only the FFT, the proposed FFT processor uses 1.69 times the hardware resources but achieves an execution time 7.32 times shorter.

A Method of UWB Radar Vital Detection Based on P Time Extraction of Strong Vital Signs

The vital sign information in the echo signal of the UWB radar is weak, because of the interference of complex noise. In this paper, a method named P times extraction of strong vital signs for processing echo signals of UWB radars is proposed. Different noises can be distinguished by the cumulative probability distribution of the echo signal and using different methods for processing according to corresponding characteristics. The vital sign information which most clearly represents the trapped person is selected using P times extraction of strong vital signs; then, the respiration and heartbeat rates are extracted. At 5 different distances, multiple sets of tests were carried out on static trapped persons and micromovement trapped persons and using a computer to extract vital signs from the obtained data. Experimental data shows that the algorithm proposed in this paper can extract the respiration and heartbeat rates of trapped persons, with small relative errors and variances, and has a certain reference value for UWB radar signal processing.