The electrocardiogram (ECG) signals P-wave, QRS wave and T-wave all reflect the activity of the heart, and the analysis of ECG signals can provide basic information for the diagnosis and prevention of heart disease. In the work of this paper, frequency-modulated continuous-wave (FMCW) radar and deep learning network are utilized to acquire ECG signals non-contactly, and we propose an improved differential and cross multiply (DACM) algorithm and a multi-neighbor differentiator for extracting cardiac motion acceleration information, as well as a partitioned reconstruction network incorporating an attention mechanism of encoder-decoder to achieve ECG signal reconstruction. The design principle is a combination of signal segmentation and deep learning (Sequence-to-sequence and attention) called SS-S2SA. firstly, a segmentation algorithm is applied to segment the acceleration signal and the ECG signal synchronously, and then the cardiac motion acceleration signal is mapped to the ECG signal using the SS-S2SA network. The method proposed in this paper is demonstrated to reconstruct ECG signals more accurately and finely by training more than 18,000 acceleration signal segments from 10 healthy subjects and evaluating the predictions from 5 subjects. The average correlation coefficient between the predicted signal and the real signal is about 0.92, and the mean absolute error (MAE) of the timing of the P-peak, R-peak, and T-peak are 13.9 ms, 8.1 ms, and 11.1 ms, respectively.

In this paper, a Doppler-tolerant waveform is proposed as a transmitting signal for joint radar and communication systems. In the proposed waveform, communication signals are multiplexed at the side band of a linear frequency modulated (LFM) pulse, based on the orthogonal frequency division multiplexing (OFDM) scheme. Therefore, the proposed waveform can maintain Doppler-tolerance in radar use as well as the original LFM pulse can. In addition, it is also capable of flexibly increasing the transmission rate in communication use by assigning more communication signals at the side-band subcarriers. Numerical simulations were carried out to comprehensively examine the proposed waveform in terms of the probability of detection in radar use and the symbol error rate in communication use. In conclusion, the proposed waveform is suited to the transmitting signal for joint radar and communication systems, especially with maintaining Doppler-tolerance to detect fast-moving targets.

The Doppler velocity enhanced 79 GHz band millimeter wave (MMW) radar imaging approach is presented here, assuming a human body imaging or recognition application. There are numerous situations in which the spatial resolution is insufficient, due to aperture angle limitations, especially for vehicle mounted MMW radar systems. As the 79 GHz band MMW radar has a definitive advantage for higher Doppler velocity resolution even with a short coherent processing interval (CPI), this study introduces the Doppler velocity decomposed imaging scheme, focusing on micro-Doppler variations of the walking human model. The real experimental data show that our proposed approach provides further improvement for accurate and high resolution radar imaging.

In recent times, there has been a significant focus on the development of automotive high-resolution 77 GHz CS (Chirp Sequence) radar, a technology essential for autonomous driving. However, with the increasing popularity of vehicle-mounted CS radars, the issue of intensive inter-radar wideband interference has emerged as a significant concern, leading to undesirable missed targe detection. To solve this problem, various algorithm and learning based approaches have been proposed for wideband interference suppression. In this study, we begin by conducting extensive simulations to assess the SINR (Signal to Interference plus Noise Ratio) and execution time of these approaches in highly demanding scenarios involving up to 7 interfering radars. Subsequently, to validate these approaches could generalize to real data, we perform comprehensive experiments on inter-radar interference using multiple 77 GHz MIMO (Multiple-Input and Multiple-output) CS radars. The collected real-world interference data is then utilized to validate the generalization capacity of these approaches in terms of SINR, missed detection rate, and false detection rate.

We consider the Doppler ambiguity compensation problem for weak moving target detection in passive bistatic radar. Detecting an unknown high-speed weak target has a high probability of the presence of Doppler ambiguity, which will decrease the integration performance and accordingly make the target detection difficult under low signal-to-noise ratio (SNR) environments. Resorting to the well-known keystone transform (KT) method, an approach to compensate for the Doppler ambiguity within the batch is proposed for the first time. The proposed approach establishes a good coupling between the reference and echo signals by adding a frequency shift related to the Doppler frequency in the procedure of computing the cross ambiguity function (CAF). Simulation results show that the coherent integration gain of our approach is close to the theoretical upper bound even in the presence of Doppler ambiguity.

Ground penetrating radar (GPR) has the advantage of non-destructively and quickly inspecting internal structures such as voids and buried pipes under roads. However, it is necessary to estimate the internal structures from the GPR images. Recently, recognition and detection methods for GPR images using deep learning have been studied. This paper examines a data augmentation method using a cutout method necessary to estimate GPR images with deep learning accurately. We find that the cutout augmentation exhibits higher detection rates for all objects used in this study than a commonly used horizontal shift augmentation.

Phased-array technology is primarily employed in atmospheric and wind profiling radars for meteorological remote sensing. As a novel avenue of advancement in phased-array technology, the Multiple-Input Multiple-Output (MIMO) technique, originally developed for communication systems, has been applied to radar systems. A MIMO radar system can be used to create a virtual receive antenna aperture plane with transmission freedom. The MIMO technique requires orthogonal waveforms on each transmitter to identify the transmit signals using multiple receivers; various methods have been developed to realize the orthogonality. In this study, we focus on the Doppler Division Multiple Access (DDMA) MIMO technique by using slightly different frequencies for the transmit waveforms, which can be separated by different receivers in the Doppler frequency domain. The Middle and Upper atmosphere (MU) radar is a VHF-band phased array atmospheric radar with multi-channel receivers. Additional configurations are necessary, requiring the inclusion of multi-channel transmitters to enable its operation as a MIMO radar. In this study, a comparison between the brightness distribution of the beamformer, utilizing echoes reflected from the moon, and the antenna pattern obtained through calculations revealed a high degree of consistency, which means that the MU radar functions effectively as a MIMO radar. Furthermore, it is demonstrated that the simultaneous application of MIMO and Capon techniques has a mutually enhancing effect.

To address the challenge of target signals being completely submerged by ionospheric clutter during typhoon passages, this letter proposes a chaotic detection method for target signals in the background of ionospheric noise under typhoon excitation. Experimental results demonstrate the effectiveness of the proposed method in detecting target signals with harmonic characteristics from strong ionospheric clutter during typhoon passages.

Numerous meteorological disasters recur almost annually. One of the most effective means to observe these phenomena causing such disasters is meteorological radar. A group comprising Toshiba, the National Institute of Information and Communications Technology (NICT), and Osaka University has developed an X-band phased array radar, improving observation time from the conventional 10-minute duration to just 30 seconds by using phased array technology. The initial radar was installed at Osaka University in May 2012, and was recently replaced by a dual-polarization one. Phased array radar has demonstrated superior temporal and spatial resolution compared to conventional radars and has shown equivalent accuracy in observing variables such as rain rate. Future research is expected to illuminate the advantages and limitations of dual-polarization phased array radar networks, fostering their widespread adoption not only in Japan but also globally.

There has been a growing interest in the application of radar technology to the monitoring of humans and animals and their positions, motions, activities, and vital signs. Radar can be used, for example, to remotely measure vital signs such as respiration and heartbeat without contact. Radar-based human sensing is expected to be adopted in a variety of fields, such as medicine, healthcare, and entertainment, but what can be realized by radar-based animal sensing? This paper reviews the latest research trends in the noncontact sensing of animals using radar systems. We also present examples of our past radar experiments for the respiratory measurement of monkeys and the heartbeat measurement of chimpanzees. The trends in this field are reviewed in terms of the target animal species, type of vital sign, and radar type and selection of frequencies.

Radar emitter identification (REI) is a crucial function of electronic radar warfare support systems. The challenge emphasizes identifying and locating unique transmitters, avoiding potential threats, and preparing countermeasures. Due to the remarkable effectiveness of deep learning (DL) in uncovering latent features within data and performing classifications, deep neural networks (DNNs) have seen widespread application in radar emitter identification (REI). In many real-world scenarios, obtaining a large number of annotated radar transmitter samples for training identification models is essential yet challenging. Given the issues of insufficient labeled datasets and abundant unlabeled training datasets, we propose a novel REI method based on a semi-supervised learning (SSL) framework with virtual adversarial training (VAT). Specifically, two objective functions are designed to extract the semantic features of radar signals: computing cross-entropy loss for labeled samples and virtual adversarial training loss for all samples. Additionally, a pseudo-labeling approach is employed for unlabeled samples. The proposed VAT-based SS-REI method is evaluated on a radar dataset. Simulation results indicate that the proposed VAT-based SS-REI method outperforms the latest SS-REI method in recognition performance.

Automatic detection and recognition systems have numerous applications in smart city implementation. Despite the accuracy and widespread use of device-based and optical methods, several issues remain. These include device limitations, environmental limitations, and privacy concerns. The FMWC sensor can overcome these issues to detect and track moving people accurately in commercial environments. However, single-chip mmWave sensor solutions might struggle to recognize standing and sitting people due to the necessary static removal module. To address these issues, we propose a real-time indoor people detection and tracking fusion system using mmWave radar and cameras. The proposed fusion system approaches an overall detection accuracy of 93.8% with a median position error of 1.7 m in a commercial environment. Compared to our single-chip mmWave radar solution addressing an overall accuracy of 83.5% for walking people, it performs better in detecting individual stillness, which may feed the security needs in retail. This system visualizes customer information, including trajectories and the number of people. It helps commercial environments prevent crowds during the COVID-19 pandemic and analyze customer visiting patterns for efficient management and marketing. Powered by an IoT platform, the system can be deployed in the cloud for easy large-scale implementation.

In recent years, high-resolution 77 GHz band automotive radar, which is indispensable for autonomous driving, has been extensively investigated. In the future, as vehicle-mounted CS (chirp sequence) radars become more and more popular, intensive inter-radar wideband interference will become a serious problem, which results in undesired miss detection of targets. To address this problem, learning-based wideband interference mitigation method has been proposed, and its feasibility has been validated by simulations. In this paper, firstly we evaluated the trade-off between interference mitigation performance and model training time of the learning-based interference mitigation method in a simulation environment. Secondly, we conducted extensive inter-radar interference experiments by using multiple 77 GHz MIMO (Multiple-Input and Multiple-output) CS radars and collected real-world interference data. Finally, we compared the performance of learning-based interference mitigation method with existing algorithm-based methods by real experimental data in terms of SINR (signal to interference plus noise ratio) and MAPE (mean absolute percentage error).

The concept of dual function radar-communication (DFRC) provides solution to the problem of spectrum scarcity. This paper examines a multiple-input multiple-output (MIMO) DFRC system with the assistance of a reconfigurable intelligent surface (RIS). The system is capable of sensing multiple spatial directions while serving multiple users via orthogonal frequency division multiplexing (OFDM). The objective of this study is to design the radiated waveforms and receive filters utilized by both the radar and users. The mutual information (MI) is used as an objective function, on average transmit power, for multiple targets while adhering to constraints on power leakage in specific directions and maintaining each user’s error rate. To address this problem, we propose an optimal solution based on a computational genetic algorithm (GA) using bisection method. The performance of the solution is demonstrated by numerical examples and it is shown that, our proposed algorithm can achieve optimum MI and the use of RIS with the MIMO DFRC system improving the system performance.

High Frequency Surface Wave Radar holds significant potential in sea detection. However, the target signals are often surpassed by substantial sea clutter and ionospheric clutter, making it crucial to address clutter suppression and extract weak target signals amidst the strong noise background.This study proposes a novel method for separating weak harmonic target signals based on local tangent space, leveraging the chaotic feature of ionospheric clutter.The effectiveness of this approach is demonstrated through the analysis of measured data, thereby validating its practicality and potential for real-world applications.

Integrated Sensing and Communication at terahertz band (ISAC-THz) has been considered as one of the promising technologies for the future 6G. However, in the phase-shifters (PSs) based massive multiple-input-multiple-output (MIMO) hybrid precoding system, due to the ultra-large bandwidth of the terahertz frequency band, the subcarrier channels with different frequencies have different equivalent spatial directions. Therefore, the hybrid beamforming at the transmitter will cause serious beam split problems. In this letter, we propose a dual-function radar communication (DFRC) precoding method by considering recently proposed delay-phase precoding structure for THz massive MIMO. By adding delay phase components between the radio frequency chain and the frequency-independent PSs, the beam is aligned with the target physical direction over the entire bandwidth to reduce the loss caused by beam splitting effect. Furthermore, we employ a hardware structure by using true-time-delayers (TTDs) to realize the concept of frequency-dependent phase shifts. Theoretical analysis and simulation results have shown that it can increase communication performance and make up for the performance loss caused by the dual-function trade-off of communication radar to a certain extent.

The subject must be motionless for conventional radar-based non-contact vital signs measurements. Additionally, the measurement range is limited by the design of the radar module itself. Although the accuracy of measurements has been improving, the prospects for their application could have been faster to develop. This paper proposed a novel radar-based adaptive tracking method for measuring the heart rate of the moving monitored person. The radar module is fixed on a circular plate and driven by stepping motors to rotate it. In order to protect the user’s privacy, the method uses radar signal processing to detect the subject’s position to control a stepping motor that adjusts the radar’s measurement range. The results of the fixed-route experiments revealed that when the subject was moving at a speed of 0.5 m/s, the mean values of RMSE for heart rate measurements were all below 2.85 beat per minute (bpm), and when moving at a speed of 1 m/s, they were all below 4.05 bpm. When subjects walked at random routes and speeds, the RMSE of the measurements were all below 6.85 bpm, with a mean value of 4.35 bpm. The average RR interval time of the reconstructed heartbeat signal was highly correlated with the electrocardiography (ECG) data, with a correlation coefficient of 0.9905. In addition, this study not only evaluated the potential effect of arm swing (more normal walking motion) on heart rate measurement but also demonstrated the ability of the proposed method to measure heart rate in a multiple-people scenario.

With the development of drone technology, concerns have arisen about the possibility of drones being equipped with threat payloads for terrorism and other crimes. A drone detection system that can detect drones carrying payloads is needed. A drone’s propeller rotation frequency increases with payload weight. Therefore, a method for estimating propeller rotation frequency will effectively detect the presence or absence of a payload and its weight. In this paper, we propose a method for classifying the payload weight of a drone by estimating its propeller rotation frequency from radar images obtained using a millimeter-wave fast-chirp-modulation multiple-input and multiple-output (MIMO) radar. For each drone model, the proposed method requires a pre-prepared reference dataset that establishes the relationships between the payload weight and propeller rotation frequency. Two experimental measurement cases were conducted to investigate the effectiveness of our proposal. In case 1, we assessed four drones (DJI Matrice 600, DJI Phantom 3, DJI Mavic Pro, and DJI Mavic Mini) to determine whether the propeller rotation frequency of any drone could be correctly estimated. In case 2, experiments were conducted on a hovering Phantom 3 drone with several payloads in a stable position for calculating the accuracy of the payload weight classification. The experimental results indicated that the proposed method could estimate the propeller rotation frequency of any drone and classify payloads in a 250 g step with high accuracy.

This letter deals with the joint direction of arrival and direction of departure estimation problem for overloaded target in bistatic multiple-input multiple-output radar system. In order to achieve the purpose of effective estimation, the presented Khatri-Rao (KR) MUSIC estimator with the ability to handle overloaded targets mainly combines the subspace characteristics of the target reflected wave signal and the KR product based on the array response. This letter also presents a computationally efficient KR noise subspace projection matrix estimation technique to reduce the computational load due to perform high-dimensional singular value decomposition. Finally, the effectiveness of the proposed method is verified by computer simulation.

A method for bandwidth enhancement of radar cross section (RCS) reduction by metasurfaces was studied. Scattering cancellation is one of common methods for reducing RCS of target scatterers. It occurs when the wave scattered by the target scatterer and the wave scattered by the canceling scatterer are the same amplitude and opposite phase. Since bandwidth of scattering cancellation is usually narrow, we proposed the bandwidth enhancement method using metasurfaces, which can control the frequency dependence of the scattering phase. We designed and fabricated a metasurface composed of a patch array on a grounded dielectric substrate. Numerical and experimental evaluations confirmed that the metasurface enhances the bandwidth of 10dB RCS reduction by 52% bandwidth ratio of the metasurface from 34% bandwidth ratio of metallic cancelling scatterers.