Synthetic aperture radar (SAR) is a device for observing the ground surface and is one of the important technologies in the field of microwave remote sensing. In SAR observation, a platform equipped with a small-aperture antenna flies in a straight line and continuously radiates pulse waves to the ground during the flight. After that, by synthesizing the series of observation data obtained during the flight, one realize high-resolution ground surface observation. In SAR observation, there are two spatial resolutions defined in the range and azimuth directions and they are limited by the bandwidth of the SAR system. The purpose of this study is to improve the resolution of SAR by sparse reconstruction. In particular, we aim to improve the resolution of SAR without changing the frequency parameters. In this paper, we propose to improve the resolution of SAR using the deconvolution iterative shrinkage-thresholding algorithm (ISTA) and verify the proposed method by carrying out an experimental analysis using an actual SAR dataset. Experimental results show that the proposed method can improve the resolution of SAR with low computational complexity.

Radar-based sensing and concealed weapon detection technologies have been attracting attention as a measure to enhance security screening in public facilities and various venues. For these applications, the security check must be performed without impeding the flow of people, with minimum human effort, and in a non-contact manner. We developed technologies for a high-throughput walk-through security screening called Invisible Sensing (IVS) and implemented them in a prototype system. The IVS system consists of dual planar radar panels facing each other and carries out an inspection based on a multi-region screening approach as a person walks between the panels. Our imaging technology constructs a high-quality radar image that compensates for motion blur caused by a person's walk. Our detection technology takes multi-view projected images across the multiple regions as input to enable real-time whole-body screening. The IVS system runs its functions by pipeline processing to achieve real-time screening operation. This paper presents our IVS system along with these key technologies and demonstrates its empirical performance.

A multifunctional radar (MFR) with varying pulse sequences can change its signal characteristics and/or pattern, based on the presence of targets and to avoid being jammed. To take a countermeasure against an MFR, it is crucial for an electronic warfare (EW) system to be able to identify and separate a MFR's modes via analyzing intercepted radar signals, without a priori knowledge. In this article, two correlation-based methods, one taking the signal's order into account and another one ignoring the signal's order, are proposed and investigated for this task. The results demonstrate their great potential.

The ionospheric clutter in High Frequency Surface Wave Radar (HFSWR) is the reflection of electromagnetic waves from the ionosphere back to the receiver, which should be suppressed as much as possible for the primary purpose of target detection in HFSWR. However, ionospheric clutter contains vast quantities of ionospheric state information. By studying ionospheric clutter, some of the relevant ionospheric parameters can be inferred, especially during the period of typhoons, when the ionospheric state changes drastically affected by typhoon-excited gravity waves, and utilizing the time-frequency characteristics of ionospheric clutter at typhoon time, information such as the trend of electron concentration changes in the ionosphere and the direction of the typhoon can be obtained. The results of the processing of the radar data showed the effectiveness of this method.

With the rapid increase in the amount of data communication in 5G networks, there is a strong demand to reduce the power of the entire network, so the use of highly power-efficient millimeter-wave (mm-wave) networks is being considered. However, while mm-wave communication has high power efficiency, it has strong straightness, so it is difficult to secure stable communication in an environment with blocking. Especially when considering use cases such as autonomous driving, continuous communication is required when transmitting streaming data such as moving images taken by vehicles, it is necessary to compensate the blocking problem. For this reason, the authors examined an optimum radio access technology (RAT) selection scheme which selects mm-wave communication when mm-wave can be used and select wide-area macro-communication when mm-wave may be blocked. In addition, the authors implemented the scheme on a prototype device and conducted field tests and confirmed that mm-wave communication and macro communication were switched at an appropriate timing.

To enhance safety and efficiency in the traffic environment, developing intelligent transportation systems (ITSs) is of paramount importance. In ITSs, roadside units (RSUs) are critical components that enable the environment awareness and connectivity via using radar sensing and communications. In this paper, we focus on RSUs with multiple radar systems. Specifically, we propose a parameter selection method of multiple radar systems to enhance the overall sensing performance. Furthermore, since different radars provide different sensing and tracking results, to benefit from multiple radars, we propose fusion algorithms to integrate the tracking results of different radars. We use two commercial frequency-modulated continuous wave (FMCW) radars to conduct experiments at Hsinchu city in Taiwan. The experimental results validate that our proposed approaches can improve the overall sensing performance.

In recent years, microwave wireless power transfer (WPT) has attracted considerable attention due to the increasing demand for various sensors and Internet of Things (IoT) applications. Microwave WPT requires technology that can detect and avoid human bodies in the transmission path. Using a phantom is essential for developing such technology in terms of standardization and human body protection from electromagnetic radiation. In this study, a simple and lightweight phantom was developed focusing on its radar cross-section (RCS) to evaluate human body avoidance technology for use in microwave WPT systems. The developed phantom's RCS is comparable to that of the human body.

Intelligent traffic monitoring provides information support for autonomous driving, which is widely used in intelligent transportation systems (ITSs). A method for estimating vehicle moving target parameters based on millimeter-wave radars is proposed to solve the problem of low detection accuracy due to velocity ambiguity and Doppler-angle coupling in the process of traffic monitoring. First of all, a MIMO antenna array with overlapping elements is constructed by introducing them into the typical design of MIMO radar array antennas. The motion-induced phase errors are eliminated by the phase difference among the overlapping elements. Then, the position errors among them are corrected through an iterative method, and the angle of multiple targets is estimated. Finally, velocity disambiguation is performed by adopting the error-corrected phase difference among the overlapping elements. An accurate estimation of vehicle moving target angle and velocity is achieved. Through Monte Carlo simulation experiments, the angle error is 0.1° and the velocity error is 0.1m/s. The simulation results show that the method can be used to effectively solve the problems related to velocity ambiguity and Doppler-angle coupling, meanwhile the accuracy of velocity and angle estimation can be improved. An improved algorithm is tested on the vehicle datasets that are gathered in the forward direction of ordinary public scenes of a city. The experimental results further verify the feasibility of the method, which meets the real-time and accuracy requirements of ITSs on vehicle information monitoring.

The degree of reception of BS signals is affected by various factors. After routinely recording it at two observation points at two locations, we found that momentary upward and downward level shifts occurred multiple times, mainly during daytime. These level shifts were observed at one location. No such signal was sensed at the other location. After producing an algorithm to extract such momemtary level shifts, their statistical properties were investigated. Careful analyses, including assessment of the signal polarity, amplitude, duration, hours, and comparison with actual flight schedules and route information implied that these level shifts are attributable to the interference of direct and reflected waves from aircraft flying at approximately tropopause altitude. This assumption is further validated through computer simulations of BS signal interference.

Automatic modulation recognition(AMR) of radar signals is a currently active area, especially in electronic reconnaissance, where systems need to quickly identify the intercepted signal and formulate corresponding interference measures on computationally limited platforms. However, previous methods generally have high computational complexity and considerable network parameters, making the system unable to detect the signal timely in resource-constrained environments. This letter firstly proposes an efficient modulation recognition network(EMRNet) with tiny and low latency models to match the requirements for mobile reconnaissance equipments. One-dimensional residual depthwise separable convolutions block(1D-RDSB) with an adaptive size of receptive fields is developed in EMRNet to replace the traditional convolution block. With 1D-RDSB, EMRNet achieves a high classification accuracy and dramatically reduces computation cost and network paraments. The experiment results show that EMRNet can achieve higher precision than existing 2D-CNN methods, while the computational cost and parament amount of EMRNet are reduced by about 13.93× and 80.88×, respectively.

This study proposes a low-complexity permittivity estimation for ground penetrating radar applications based on a contrast source inversion (CSI) approach, assuming multilayered ground media. The homogeneity assumption for each background layer is used to address the ill-posed condition while maintaining accuracy for permittivity reconstruction, significantly reducing the number of unknowns. Using an appropriate initial guess for each layer, the post-CSI approach also provides the dielectric profile of a buried object. The finite difference time domain numerical tests show that the proposed approach significantly enhances reconstruction accuracy for buried objects compared with the traditional CSI approach.

One key to implementing the smart city is letting the smart space know where and how many people are. The visual method is a scheme to recognize people with high accuracy, but concerns arise regarding potential privacy leakage and user nonacceptance. Besides, being functional in a limited environment in an emergency should also be considered. We propose a real-time people counting and tracking system based on a millimeter wave radar (mmWave) as an alternative to the optical solutions in a restaurant. The proposed method consists of four main procedures. First, capture the point cloud of obstacles and generate them using a low-cost, commercial off-the-shelf (COTS) mmWave radar. Next, cluster the individual point with similar properties. Then the same people in sequential frames would be associated with the tracking algorithm. Finally, the estimated people would be counted, tracked, and shown in the next frame. The experiment results show that our proposed system provided a median position error of 0.17 m and counting accuracy of 83.5% for ten insiders in various scenarios in an actual restaurant environment. In addition, the real-time estimation and visualization of people's numbers and positions show a potential capability to help prevent crowds during the pandemic of Covid-19 and analyze customer visitation patterns for efficient management and target marketing.

In this letter, we consider the problem of joint selection of transmitters and receivers in a distributed multi-input multi-output radar network for localization. Different from previous works, we consider a more mathematically challenging but generalized situation that the transmitting signals are not perfectly orthogonal. Taking Cramér Rao lower bound as performance metric, we propose a scheme of joint selection of transmitters and receivers (JSTR) aiming at optimizing the localization performance under limited number of nodes. We propose a bi-convex relaxation to replace the resultant NP hard non-convex problem. Using the bi-convexity, the surrogate problem can be efficiently resolved by nonlinear alternating direction method of multipliers. Simulation results reveal that the proposed algorithm has very close performance compared with the computationally intensive but global optimal exhaustive search method.

In this brief paper, we examine polarimetric scattering characteristics for understanding seasonal change of paddy rice growth by using quad-polarimetric synthetic aperture radar (SAR) data in the X-band. Here we carry out polarimetric scattering measurement for a simplified paddy rice model in an anechoic chamber at X-band frequency to acquire the the quad polarimetric SAR data from the model. The measurements are performed several times for each growth stage of the paddy rice corresponding to seasonal change. The model-based scattering power decomposition is used for the examination of polarimetric features of the paddy rice model. It is found from the result of the polarimetric SAR image analysis for the measurement data that the growth state of the paddy rice in each stage can be understood by considering the ratio of the decomposition powers, when the planting direction of the paddy rice is not only normal but also oblique to radar direction. We can also see that orientation angle compensation (OAC) is useful for improving the accuracy of the growth stage observation in late vegetative stage for oblique planting case.

Under the background of non-homogenous and dynamic time-varying clutter, the processing ability of the traditional constant false alarm rate (CFAR) detection algorithm is significantly reduced, as well as the detection performance. This paper proposes a CFAR detection algorithm based on clutter knowledge (CK-CFAR), as a new CFAR, to improve the detection performance adaptability of the radar in complex clutter background. With the acquired clutter prior knowledge, the algorithm can dynamically select parameters according to the change of background clutter and calculate the threshold. Compared with the detection algorithms such as CA-CFAR, GO-CFAR, SO-CFAR, and OS-CFAR, the simulation results show that CK-CFAR has excellent detection performance in the background of homogenous clutter and edge clutter. This algorithm can help radar adapt to the clutter with different distribution characteristics, effectively enhance radar detection in a complex environment. It is more in line with the development direction of the cognitive radar.

A method of radar-based contactless vital-sign sensing and electrocardiogram (ECG) signal reconstruction using deep learning is proposed. A radar system is an effective tool for contactless vital-sign sensing because it can measure a small displacement of the body surface without contact. However, most of the conventional methods have limited evaluation indices and measurement conditions. A method of measuring body-surface-displacement signals by using frequency-modulated continuous-wave (FMCW) radar and reconstructing ECG signals using a convolutional neural network (CNN) is proposed. This study conducted two experiments. First, we trained a model using the data obtained from six subjects breathing in a seated condition. Second, we added sine wave noise to the data and trained the model again. The proposed model is evaluated with a correlation coefficient between the reconstructed and actual ECG signal. The results of first experiment show that their ECG signals are successfully reconstructed by using the proposed method. That of second experiment show that the proposed method can reconstruct signal waveforms even in an environment with low signal-to-noise ratio (SNR).

Distributed array radars consist of multiple sub-arrays separated by tens to hundreds of wavelengths and can match narrow beamwidths with large-aperture, high-gain antennas. The physical independence of the sub-arrays contributes to significant structure flexibility and is one of the advantages of such radars. However, a typical problem is the grating lobes in the digital beam forming (DBF) beam pattern. Unfortunately, the need to suppress the generation of grating lobes makes the design of acceptable sub-array arrangements very difficult. A sigma-delta beam former direction of arrival (DOA) estimation method is proposed in this study to solve this problem. The proposed method performs DOA estimation by acquiring the difference signals in addition to the sum signals of all sub-arrays. The difference signal is typically used for monopulse DOA estimation in the phased array radar. The sigma-delta beamformer simultaneously has both advantages of DOA estimations using a distributed array with a large aperture length and using a sub-array that is not affected by the grating lobe. The proposed method can improve the DOA estimation accuracy over the conventional method under grating lobe situations and help the distributed array radar achieve flexibility in the sub-array arrangement. Numerical simulations are presented to verify the effectiveness of the proposed DOA estimation method.

In this study, we propose an accurate range-Doppler analysis algorithm for moving multiple objects in a short range using microwave (including millimeter wave) radars. As a promising Doppler analysis for the above model, we previously proposed a weighted kernel density (WKD) estimator algorithm, which overcomes several disadvantages in coherent integration based methods, such as a trade-off between temporal and frequency resolutions. However, in handling multiple objects like human body, it is difficult to maintain the accuracy of the Doppler velocity estimation, because there are multiple responses from multiple parts of object, like human body, incurring inaccuracies in range or Doppler velocity estimation. To address this issue, we propose an iterative algorithm by exploiting an output of the WKD algorithm. Three-dimensional numerical analysis, assuming a human body model in motion, and experimental tests demonstrate that the proposed algorithm provides more accurate, high-resolution range-Doppler velocity profiles than the original WKD algorithm, without increasing computational complexity. Particularly, the simulation results show that the cumulative probabilities of range errors within 10mm, and Doppler velocity error within 0.1m/s are enhanced from 34% (by the former method) to 63% (by the proposed method).

To mitigate the interference caused by range sidelobes in multiple-input multiple-output (MIMO) radar, we propose a new method to construct Doppler resilient complementary waveforms from complete complementary code (CCC). By jointly designing the transmit pulse train and the receive pulse weights, the range sidelobes can vanish within a specified Doppler interval. In addition, the output signal-to-noise ratio (SNR) is maximized subject to the Doppler resilience constraint. Numerical results show that the designed waveforms have better Doppler resilience than the previous works.

In this paper, a passive multiple-input multiple-output (MIMO) radar system with widely separated antennas that estimates the positions and velocities of multiple moving targets by utilizing time delay (TD) and doppler shift (DS) measurements is proposed. Passive radar systems can detect targets by using multiple uncoordinated and un-synchronized illuminators and we assume that all the measurements including TD and DS have been known by a preprocessing method. In this study, the algorithm can be divided into three stages. First, based on location information within a certain range and utilizing the DBSCAN cluster algorithm we can obtain the initial position of each target. In the second stage according to the correlation between the TD measurements of each target in a specific receiver and the DSs, we can find the set of DS measurements for each target. Therefore, the initial speed estimated values can be obtained employing the least squares (LS) method. Finally, maximum likelihood (ML) estimation of a first-order Taylor expansion joint TD and DS is applied for a better solution. Extensive simulations show that the proposed algorithm has a good estimation performance and can achieve the Cramér-Rao lower bound (CRLB) under the condition of moderate measurement errors.