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.

In a previous study, we proposed a new method based on copula theory to evaluate the detection performance of distributed-processing multistatic radar systems, in which the dependence of local decisions was modeled by a Gaussian copula with linear dependence and no tail dependence. However, we also noted that one main limitation of the study was the lack of investigations on the tail-dependence and nonlinear dependence among local detectors' inputs whose densities have long tails and are often used to model clutter and wanted signals in high-resolution radars. In this work, we attempt to overcome this shortcoming by extending the application of the proposed method to several types of multivariate copula-based dependence models to clarify the effects of tail-dependence and different dependence models on the system detection performance in detail. Our careful analysis provides two interesting and important clarifications: first, the detection performance degrades significantly with tail dependence; and second, this degradation mainly originates from the upper tail dependence, while the lower tail and nonlinear dependence unexpectedly improve the system performance.

This paper aims to design multiple-input multiple-output (MIMO) radar receiving weights and transmitting waveforms, in order to obtain better spatial filtering performance and enhance the robustness in the case of signal-dependent interference and jointly inaccurate estimated angles of target and interference. Generally, an alternate iterative optimization algorithm is proposed for the joint design problem. Specifically, the receiving weights are designed by the generalized eigenvalue decomposition of the matrix which contains the estimated information of the target and interference. As the cost function of the transmitting waveform design is fractional, the fractional optimization problem is first converted into a secondary optimization problem. Based on the proposed algorithm, a closed-form solution of the waveform is given using the alternating projection. At the analysis stage, in the presence of estimated errors under the environment of signal-dependent interference, a robust signal-to-interference and noise ratio (SINR) performance is obtained using a small amount of calculation with an iterative procedure. Numerical examples verify the effectiveness of the performances of the designed waveform in terms of the SINR, beampattern and pulse compression.

This paper presents a novel blind signal separation method for the measurement of pulse waves at multiple body positions using an array radar system. The proposed method is based on a mathematical model of pulse wave propagation. The model relies on three factors: (1) a small displacement approximation, (2) beam pattern orthogonality, and (3) an impulse response model of pulse waves. The separation of radar echoes is formulated as an optimization problem, and the associated objective function is established using the mathematical model. We evaluate the performance of the proposed method using measured radar data from participants lying in a prone position. The accuracy of the proposed method, in terms of estimating the body displacements, is measured using reference data taken from laser displacement sensors. The average estimation errors are found to be 10-21% smaller than those of conventional methods. These results indicate the effectiveness of the proposed method for achieving noncontact measurements of the displacements of multiple body positions.

Intelligent transportation systems (ITSs) have been extensively studied in recent years to improve the safety and efficiency of transportation. The use of a radar system to enable the ITSs monitor the environment is robust to weather conditions and is less invasive to user privacy. Moreover, equipping the roadside units (RSUs) with radar modules has been deemed an economical and efficient option for ITS operators. However, because the detection and tracking parameters can significantly influence the radar system performance and the best parameters for different scenarios are different, the selection of appropriate parameters for the radar systems is critical. In this study, we investigated radar parameter selection and consequently proposes a parameter selection approach capable of automatically choosing the appropriate detection and tracking parameters for radar systems. The experimental results indicate that the proposed method realizes appropriate selection of parameters, thereby significantly improving the detection and tracking performance of radar systems.

This paper proposes two accurate source-number estimation methods for array antennas and multi-input multi-output radar. Direction of arrival (DOA) estimation is important in high-speed wireless communication and radar imaging. Most representative DOA estimation methods require the source-number information in advance and often fail to estimate DOAs in severe environments such as those having low signal-to-noise ratio or large transmission-power difference. Received signals are often bandlimited or narrowband signals, so the proposed methods first involves denoising preprocessing by removing undesired components then comparing the original and denoised signal information. The performances of the proposed methods were evaluated through computer simulations.

The electromagnetic environment is increasingly complex and changeable, and radar needs to meet the execution requirements of various tasks. Modern radars should improve their intelligence level and have the ability to learn independently in dynamic countermeasures. It can make the radar countermeasure strategy change from the traditional fixed anti-interference strategy to dynamically and independently implementing an efficient anti-interference strategy. Aiming at the performance optimization of target tracking in the scene where multiple signals coexist, we propose a countermeasure method of cognitive radar based on a deep Q-learning network. In this paper, we analyze the tracking performance of this method and the Markov Decision Process under the triangular frequency sweeping interference, respectively. The simulation results show that reinforcement learning has substantial autonomy and adaptability for solving such problems.

High Frequency Surface Wave Radar (HFSWR) can achieve over-the-horizon detection, which can effectively detect and track the ships and ultra-low altitude aircrafts, as well as the acquisition of sea state information such as icebergs and ocean currents and so on. However, HFSWR is seriously affected by the clutters, especially sea clutter and ionospheric clutter. In this paper, we propose a deep learning image semantic segmentation method based on optimized Deeplabv3+ network to achieve the automatic detection of sea clutter and ionospheric clutter using the measured R-D spectrum images of HFSWR during the typhoon as experimental data, which avoids the disadvantage of traditional detection methods that require a large amount of a priori knowledge and provides a basis for subsequent the clutter suppression or the clutter characteristics research.

Heart rate measurement for mm-wave FMCW radar based on phase analysis comprises a variety of noise. Furthermore, because the breathing and heart frequencies are so close, the harmonic of the breathing signal interferes with the heart rate, and the band-pass filter cannot solve it. On the other hand, because heart rates vary from person to person, it is difficult to choose the basic function of WT (Wavelet Transform). To solve the aforementioned difficulties, we consider performing time-frequency domain analysis on human skin surface displacement data. The PA-LI (Phase Accumulation-Linear Interpolation) joint ICEEMDAN (Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise) approach is proposed in this paper, which effectively enhances the signal's SNR, estimates the heart rate, and reconstructs the heartbeat signal. The experimental findings demonstrate that the proposed method can not only extract heartbeat signals with high SNR from the front direction, but it can also detect heart rate from other directions (e.g., back, left, oblique front, and ceiling).

In this paper, we propose a new method based on copula theory to evaluate the detection performance of a distributed-processing multistatic radar system (DPMRS). By applying the Gaussian copula to model the dependence of local decisions in a DPMRS as well as data fusion rules of AND, OR, and K/N, the performance of a DPMRS for detecting Swerling fluctuating targets can be easily evaluated even under non-Gaussian clutter with a nonuniform dependence matrix. The reliability and flexibility of this method are validated by applying the proposed method to a previous problem by other authors, and our other investigation results indicate its high potential for evaluating DPMRS performance in various cases involving different models of target and clutter.

This paper proposes a method for cooperative multi-static Multiple Input Multiple Output (MIMO) radar that can estimate the number of targets. The purpose of this system is to monitor humans in an indoor environment. First, target positions within the estimation range are roughly detected by the Capon method and the mode vector corresponding to the detected positions is calculated. The mode vector is multiplied by the eigenvector to eliminate the virtual image. The spectrum of the evaluation function is calculated from the remaining positions, and the number of peaks in the spectrum is defined as the number of targets. Experiments carried out in an indoor environment confirm that the proposed method can estimate the number of targets with high accuracy.