Radar Signal Processing Method Research Articles

Comparative Analysis of Audio Processing Techniques on Doppler Radar Signature of Human Walking Motion Using CNN Models.

Artificial intelligence (AI) radar technology offers several advantages over other technologies, including low cost, privacy assurance, high accuracy, and environmental resilience. One challenge faced by AI radar technology is the high cost of equipment and the lack of radar datasets for deep-learning model training. Moreover, conventional radar signal processing methods have the obstacles of poor resolution or complex computation. Therefore, this paper discusses an innovative approach in the integration of radar technology and machine learning for effective surveillance systems that can surpass the aforementioned limitations. This approach is detailed into three steps: signal acquisition, signal processing, and feature-based classification. A hardware prototype of the signal acquisition circuitry was designed for a Continuous Wave (CW) K-24 GHz frequency band radar sensor. The collected radar motion data was categorized into non-human motion, human walking, and human walking without arm swing. Three signal processing techniques, namely short-time Fourier transform (STFT), mel spectrogram, and mel frequency cepstral coefficients (MFCCs), were employed. The latter two are typically used for audio processing, but in this study, they were proposed to obtain micro-Doppler spectrograms for all motion data. The obtained micro-Doppler spectrograms were then fed to a simplified 2D convolutional neural networks (CNNs) architecture for feature extraction and classification. Additionally, artificial neural networks (ANNs) and 1D CNN models were implemented for comparative analysis on various aspects. The experimental results demonstrated that the 2D CNN model trained on the MFCC feature outperformed the other two methods. The accuracy rate of the object classification models trained on micro-Doppler features was 97.93%, indicating the effectiveness of the proposed approach.

Integrated Sensing and Communication Signals Toward 5G-A and 6G: A Survey

Integrated sensing and communication (ISAC) has the advantages of efficient spectrum utilization and low hardware cost. It is promising to be implemented in the fifth-generation-advanced (5G-A) and sixth-generation (6G) mobile communication systems, having the potential to be applied in intelligent applications requiring both communication and high-accurate sensing capabilities. As the fundamental technology of ISAC, ISAC signal directly impacts the performance of sensing and communication. This article systematically reviews the literature on ISAC signals from the perspective of mobile communication systems, including ISAC signal design, ISAC signal processing algorithms and ISAC signal optimization. We first review the ISAC signal design based on 5G, 5G-A and 6G mobile communication systems. Then, radar signal processing methods are reviewed for ISAC signals, mainly including the channel information matrix method, spectrum lines estimator method and super resolution method. In terms of signal optimization, we summarize peak-to-average power ratio (PAPR) optimization, interference management, and adaptive signal optimization for ISAC signals. This article may provide the guidelines for the research of ISAC signals in 5G-A and 6G mobile communication systems.

Main-Lobe Jamming Suppression Method for Phased Array Netted Radar Based on MSNR-BSS

Compared with the multisite radar, the monostatic radar has the disadvantages of a single viewing angle and less available information. In the presence of main-lobe jamming, the monostatic radar has the problems of poor antijamming effect and difficulty in achieving target detection. To solve the above problems, the radar signal processing method and data fusion method are combined in this article. A main-lobe jamming suppression method for phased array (PA) netted radar based on the maximum signal-to-noise ratio blind source separation (MSNR-BSS) is proposed. First, the received signals of a monostatic PA radar are separated into target signals and jamming signals by the MSNR-BSS method. Second, channel accumulation, pulse compression, and constant false-alarm rate (CFAR) target detection are performed on the separated signal channels to obtain the target radial distance. Finally, the data fusion theory of three global orientations (TGOs) is employed to determine the target spatial position. Simulation experiments evaluate the performance of main-lobe jamming suppression and verify the effectiveness of the proposed method. In the presence of noise amplitude modulation (NAM) jamming or smeared spectrum (SMSP) jamming (JNR = 100 dB), the target positioning accuracy (TPA) of the proposed method is 6.613 or 6.596 m, respectively. Also, the peak-to-sidelobe level ratios (PSLRs) in the two jamming environments are approximately equal to −8 dB.

Research on Ultra-Wideband Radar Echo Signal Processing Method Based on P-Order Extraction and VMD

As a new method to detect vital signs, Ultra-wideband (UWB) radar could continuously monitor human respiratory signs without contact. Aimed at addressing the problem of large interference and weak acquisition signal in radar echo signals from complex scenes, this paper adopts a UWB radar echo signal processing method that combines strong physical sign information extraction at P time and Variational Mode Decomposition (VMD) to carry out theoretical derivation. Using this novel processing scheme, respiration and heartbeat signals can be quickly reconstructed according to the selection of the appropriate intrinsic mode functions (IMFs), and the real-time detection accuracy of human respiratory signs is greatly improved. Based on an experimental platform, the data collected by the UWB radar module were first verified against the measured values obtained at the actual scene. The results of a validation test proved that our UWB radar echo signal processing method effectively eliminated the respiratory clutter signal and realized the accurate measurement of respiratory and heartbeat signals, which would prove the existence of life and further improve the quality of respiration and heartbeat signal and the robustness of detection.

Deep Learning-Based Posture Recognition for Motion-Assisted Evaluation

With the development of computer vision technology, human action pose recognition has gradually become a popular research direction, but there are still some problems in the application research based on pose recognition in sports action assisted evaluation. In this paper, the human motion pose recognition technology based on deep learning is introduced into this field to realize the intelligence of sports-assisted training. Firstly, we analyze the advantages and limitations of the state-of-the-art human motion pose recognition algorithms in computer vision in specific fields. On this basis, a human motion space recognition method based on periscope neural network is proposed. Firstly, the classical radar signal processing method is used to preprocess the echo signal of human spatial position and generate the frequency image in the process of human spatial position. Then, the periscope neural network (CNN) is constructed, and the time-frequency image is used as the input data of CNN to train the network parameters. Finally, the method is tested by using the open dataset in the network. The experimental results show that the designed CNN can accurately identify four different types of physical motion, and the accuracy coefficient is at least 97%.

A Reanalysis of the October 2016 “Meteotsunami” in British Columbia With Help of High-Frequency Radars and Autoregressive Modeling

On October 14, 2016, the coastal high-frequency radar system in Tofino (British Columbia, Canada) triggered an automatic tsunami warning based on the identification of abnormal surface current patterns. This occurred in the absence of any reported seismic event but coincided with a strong atmospheric perturbation, which qualified the event as meteotsunami. We reanalyze this case in the light of a new radar signal processing method, which was designed recently for inverting fast-varying sea surface currents from the complex voltage time series received on the antennas. This method, based on autoregressive modeling combined with a maximum entropy method, yields a dramatic improvement in both the signal-to-noise ratio and the quality of the surface current estimation for very short integration time. This makes it possible to evidence the propagation of a sharp wavefront of surface current during the event and to map its magnitude and arrival time over the radar coverage. We show that the amplitude and speed of the inferred residual current do not comply with a Proudman resonance mechanism but are consistent with the propagation of a low-pressure atmospheric front. This supports the hypothesis of a storm surge rather than a true meteotsunami to explain this event. Beyond this specific case, another outcome of the analysis is the promising use of HF radars as proxy’s for the characterization of atmospheric fronts.

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.

Vital Sign Monitoring Using FMCW Radar in Various Sleeping Scenarios.

Remote monitoring of vital signs for studying sleep is a user-friendly alternative to monitoring with sensors attached to the skin. For instance, remote monitoring can allow unconstrained movement during sleep, whereas detectors requiring a physical contact may detach and interrupt the measurement and affect sleep itself. This study evaluates the performance of a cost-effective frequency modulated continuous wave (FMCW) radar in remote monitoring of heart rate and respiration in scenarios resembling a set of normal and abnormal physiological conditions during sleep. We evaluate the vital signs of ten subjects in different lying positions during various tasks. Specifically, we aim for a broad range of both heart and respiration rates to replicate various real-life scenarios and to test the robustness of the selected vital sign extraction methods consisting of fast Fourier transform based cepstral and autocorrelation analyses. As compared to the reference signals obtained using Embla titanium, a certified medical device, we achieved an overall relative mean absolute error of % (86% correlation) and % (91% correlation) for the heart rate and respiration rate, respectively. Our results promote radar-based clinical monitoring by showing that the proposed radar technology and signal processing methods accurately capture even such alarming vital signs as minimal respiration. Furthermore, we show that common parameters for heart rate variability can also be accurately extracted from the radar signal, enabling further sleep analyses.

Extended Object Tracking Using Spatially Resolved Micro-Doppler Signatures

Recent progress in radar development enable high-resolution radar sensors to resolve multiple detections for an extended object. One of the main challenges in radar-based object detection and tracking is the handling of an uncertain measurement model problem dealing with wandering dominant scatter points on the extended object surface depending on the relative orientation and motion. This phenomenon needs to be considered for near extended object tracking in critical driving situations, e.g., for safety applications, which require fast and accurate target state estimation in high dynamic scenarios. This paper presents a method to detect rotating wheels of approaching targets. Using the position and velocity of detected wheels as input for a tracking procedure, instead of target detections, relaxes the uncertain measurement model problem. A generic radar signal processing method to detect the position and bulk velocities of rotating wheels for approaching target vehicles is presented. The method evaluates the micro-Doppler signals, generated by rotating wheels, of close targets. These wheel hypotheses serve as input for a subsequent sequential Monte Carlo tracking framework. The presented methods are evaluated on real experimental data where a target vehicle is used to reconstruct safety-critical dynamic evading maneuvers.

Entomological Radar Overview: System and Signal Processing

Insect migration is a significant ecological phenomenon in nature, and difficult to directly observe because insects are small and fly hundreds of meters above the ground at night. Radar, working in all-weather and all-time conditions, remains one of the most effective tools for observing insect flight. This paper reviews the developments of radar systems and signal processing methods in radar entomology. Typical insect radar systems and parameter retrieval methods are presented, including that the scanning insect radar and Vertical Looking Radar (VLR) achieve the retrieval of the insect parameters, such as density, mass, orientation, wingbeat frequency. In addition, some cases of advanced radar techniques and signal processing are provided to give insight on current developments of insect parameter retrieval. For example, a new insect radar system now is being researched and developed in China, which consists of one high-resolution-phased array radar and three multi-frequency and fully polarized radars, by adopting modern radar techniques, such as high resolution technology, full polarization technology and distributed radar technology and so on. The new insect radar system will be crucial to monitor insect migration, both for scientific research and as an early warning system for pests.

Adaptive Matching of the Radar Signal and Image Display Device Dynamic Ranges

This article presents nonlinear radar signal processing method to form an image of the Earth's surface. The method proposes to match the dynamic ranges of the received signal and of the visualization device. The essence of the method is adaptive nonlinear signal processing, which provides better local contrast of radar images and improves discrimination of individual objects. The computational complexity of the proposed algorithm is optimized and allows real-time implementation in the airborne computing systems with limited computational power. Objects with large RCS merged into large illuminated "spots"; their visibility on the surrounding background has been reduced, unwanted effects are due to the fact that the above algorithms have a single point effect. To overcome the problems, the «Retinex» algorithm is usually used. They do not take into account the local neighborhood of pixels; therefore, in cases where the image contains both highly dark and strongly light local areas, these algorithms cannot provide high-quality matching of dynamic ranges.

Velocity monitoring signal processing method of track traffic based on Doppler effect

In order to improve the monitoring efficiency of track traffic speed, a signal processing method based on Doppler effect is purposed to meet the accurate velocity measurement of high speed trains. As a result, the track traffic radar monitoring signal processing method based on the Doppler effect is studied. First of all, the Doppler effect and the Doppler principles are analysed. Then, the research of signal processing algorithm of rail traffic radar system is focused on, and the improvement method of a real sequence fast Fourier transform (FFT) imaginary part arithmetic algorithm is put forward. The process of FFT algorithm is simplified, and finally, through the MATLAB software simulation, the improved FFT algorithm spectrum analysis effect is further verified. The test results showed that the use of improved FFT algorithm ensured the measurement accuracy, and improved the Doppler frequency calculation speed. In addition, it can meet the processing requirements of rail traffic radar velocity measurement system on the rail traffic velocity monitoring signal.

Velocity monitoring signal processing method of track traffic based on Doppler effect

In order to improve the monitoring efficiency of track traffic speed, a signal processing method based on Doppler effect is purposed to meet the accurate velocity measurement of high speed trains. As a result, the track traffic radar monitoring signal processing method based on the Doppler effect is studied. First of all, the Doppler effect and the Doppler principles are analysed. Then, the research of signal processing algorithm of rail traffic radar system is focused on, and the improvement method of a real sequence fast Fourier transform (FFT) imaginary part arithmetic algorithm is put forward. The process of FFT algorithm is simplified, and finally, through the MATLAB software simulation, the improved FFT algorithm spectrum analysis effect is further verified. The test results showed that the use of improved FFT algorithm ensured the measurement accuracy, and improved the Doppler frequency calculation speed. In addition, it can meet the processing requirements of rail traffic radar velocity measurement system on the rail traffic velocity monitoring signal.

Target Detection and Localization Using Non-Cooperative Frequency Agile Phased Array Radar Illuminator

Recently, passive bistatic radar (PBR) has become an emerging technology because it allows target detection and localization with advantages of low cost, covert detection, low vulnerability to electronic jamming and high valuable counter to stealth technology. However, these researchers mainly focus on the illuminators of opportunity such as FM radio, television broadcast, mobile network basestation, wireless fidelity, long term evolution, global navigation satellite, etc. Different from the aforementioned illuminators, this paper introduces a new PBR using a non-cooperative frequency agile phased array radar illuminator. The types of illuminator of opportunity are enriched, whereas the difficulties in PBR signal processing also come along. On one hand, since PBR works in a non-cooperative mode, there is a problem that the instantaneous parameters of transmitting signal are unknown at the receiver. On the other hand, the frequency agility technology destroys the coherency between pulses and the ability of rapidly changing beam scanning makes it difficult for PBR to realize space synchronization. Under such scenarios, conventional radar signal processing methods cannot be conducted effectively. To address these problems, this paper then proposes a feasible target detection and localization method, which is tested by field experiments. Preliminary results validate the proposed method can be applied to PBR system successfully.

On a solution to improve the object detection ability of radars by dynamic polarization method

One of the most importan problems of modern radar is increasing object detection, object distinction ability. In the last years, the used traditional radar signal processing methods are seem to use up. For solving this question, specialists are interested in analyzing the fine structure of radar signal, and the first is po larization structure. The use of polarization information allows to raise the information ability of radar systems and provides high probability of radar objects detection, high contrast of small-scale man-made objects on the radar map and radar objects classification.The article deals with the detection of radar objects by exploiting the information on polarization based on the scattering matrix (SM) of the object. A solution to improve the object detection ability of radars by dynamic polarization method (to modify the polarization of radiative wave) is introduced.

Compressed sensing application in interferometric synthetic aperture radar

A novel interferometric synthetic aperture radar (InSAR) signal processing method based on compressed sensing (CS) theory is investigated in this paper. InSAR image formation provides the scene reflectivity estimation along azimuth and range coordinates with the height information. While surveying the height information of the illuminated scene, the data volume enlarges. CS theory allows sparse sampling during the data acquisition, which can reduce the data volume and release the pressure on the record devices. InSAR system which configures two antennas to cancel the common backscatter random phase in each resolution element implies the sparse nature of the complex-valued InSAR image. The complex-valued image after conjugate multiplication that only a phase term proportional to the differential path delay is left becomes sparse in the transform domain. Sparse sampling such as M-sequence can be implemented during the data acquisition. CS theory can be introduced to the processing due to the sparsity and a link between raw data and interferometric complex-valued image can be built. By solving the CS inverse problem, the magnitude image and interferometric phase are generated at the same time. Results on both the simulated data and real data are presented. In comparison with the conventional SAR interferometry processing results, CS-based method shows the ability to keep the imaging quality with less data acquisition.

Noise properties of HF radar measurement of ocean surface currents

AbstractHigh‐frequency (HF) radars are commonly used for coastal circulation monitoring. The objective of the study is to assess what is the minimum timescale of variability of the geophysical surface currents that are accessible to the radar measurement given the intrinsic noise of this measurement. Noise properties are derived from the power density spectra (PDSs) of radial current records, which are compared to a model of the PDS of idealized currents contaminated by an additive white noise. The data were collected by two radar systems operating in the Northwestern Mediterranean. Periods of 3 weeks to 7 months are considered. Most of measured currents are affected by a white noise effect. Noise properties vary in time and space and are not specific to a particular radar station or to the radar signal processing method used (beam forming or direction finding). An increase of the noise level reduces the effective temporal resolution of radar‐derived currents and then increases the minimum observable timescale of variability of geophysical currents. Our results are consistent with results of comparison found in literature between in situ sensors and radar measurements as well as between two radars operating along a same base line. The study suggests a self‐sufficient method, requiring no external data, to estimate the minimum sampling period to consider for getting data sets having a minimized contamination by instrumental noise. This period can also be taken for smoothing or filtering measured currents.

Application of Wavelet Transform in Radar Echo Signal Level Meter Denoising

Radar Level Meter is being more and more widely used in the industry field. However, due to the complex radar system, the noise contained in the clutter echo signal, and the interference within the system and hardware, it is quietly significant to study the research of radar echo reduction technology. Based on the introduction of radar level meter ‘working theory, This paper presents a wavelet transform analysis of the echo signal de-noising method. Studies have shown that the radar echo signal processing method of using wavelet transform can effectively suppress noise, remove interference and achieve echo signal de-noising processing.

Non-contact physiological signal detection using continuous wave Doppler radar

The aim of this work is to show non-contact physiological signal monitoring system based on continuous-wave (CW) Doppler radar, which is becoming highly attractive in the field of health care monitoring of elderly people. Two radar signal processing methods were introduced in this paper: one to extract respiration and heart rates of a single person and the other to separate mixed respiration signals. To verify the validity of the methods, physiological signal is obtained from stationary human subjects using a CW Doppler radar unit. The sensor operating at 24 GHz is located 0.5 meter away from the subject. The simulation results show that the respiration and heart rates are clearly extracted, and the mixed respiration signals are successfully separated. Finally, reference respiration and heart rate signals are measured by an ECG monitor and compared with the results tracked by the CW Doppler radar monitoring system.

Calibration of Mst Radar Using Radio Source Virgo-A With Digital Receiver

MST Radar has been established to study the dynamics of atmospheric processes. In this paper, a method, by recording the noise temperature of a radio source during its transit over the radar, to find errors in antenna beam orientation is discussed. The source used for Radar at Gadanki is Virgo-A (3C274) which has appropriate right ascension and declination to be able to pass through the antenna array beam. This paper outlines the Indian MST Radar signal and data processing methods, method of offline data processing, VIRGO transit recording methodology with the help of digital receiver. Keywords: MST, Yagi antenna, signal processing, Concept of Interferometry, Virgo-A