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Two planar metasurface (MTS) antennas are designed to produce a pattern with optimal illumination in a radar scenario, given the a priori known path of the targets. The antennas are extremely flat, thin, with a monopole feed integrated in the plane of the structure. The monopole serves as a surface-wave (SW) launcher illuminating the slab. The excited SW progressively leaks after interacting with the MTS, forming a radiation pattern designed to cover uniformly a portion of road in a given scenario. The realized antennas radiation patterns are experimentally validated. The MTSs are then deployed in a frequency-modulated continuous wave (FMCW) radar system. The experiments showed that the echo of the targets in the range-Doppler map does not statistically depend on the distance between the target and the radar, while a clear range dependence is observed when classical patch array antennas are used. That means, given the capability to engineer a particular shape for the radiation pattern and the interest for simultaneously detecting small and big targets, the MTS antennas allow one to better resolve a small and distant target, next to a big and close one.

An active transmitarray antenna is presented for Ka-band monopulse tracking radar in the 34-36-GHz range. A novel contribution is the application of commercial SiGe-based 5G beamforming chips in a large-scale naval radar array, demonstrating how developments in Ka-band communications can benefit radar technology. Three arrays of 0.77 m diameter each with 24368, 19424, and 7824 radiating elements are presented to compare amplitude-tapered, space-tapered, and hybrid-tapered designs. All designs achieve a sub-1° half-power beamwidth (HPBW), peak sidelobe levels (SLL) below -26 dB, and effective isotropic radiated power (EIRP) of 100 dBm at broadside with a ±60° grating-lobe-free scan range. Furthermore, a small-scale demonstrator is realized and presented that consists of an eight-layer printed circuit board (PCB) with 8× 8 patch antennas on one side and a 4× 4 open-ended waveguide (OEWG) feed-array with beamforming chips and integrated liquid cooling channels on the other side. After over-the-air calibration, the realized array achieves a peak EIRP of 56 dBm, a 12° HPBW, and the ability to scan to ±60° in the H-plane with cos θ scan loss and no grating lobes. E-plane scanning is achieved up to ±45° with θ 1.1 scan loss.

In this paper, a new preamble waveform design and a corresponding minimum-point processing algorithm at receiver are proposed to perform radar sensing of communication signal echoes. In our design, we consider the millimeter wave (mmWave) 802.11ad/802.11ay communication frame format as a reference. Our proposed new preamble replaces the second pair of single-carrier (SC) Golay complementary code (GCC) used in the preamble of IEEE 802.11ad/802.11ay by orthogonal frequency-division multiplexing (OFDM) GCC waveform, resulting in a SC-OFDM GCC preamble waveform. At the receiver, a point-wise minimum operation is performed on the separate radar ambiguity functions (AF) of the SC-GCC and OFDM-GCC to improve the overall quality of the preamble AF. Furthermore, point-wise minimum processing is applied to fuse the communication data AF and the preamble AF, so as to further enhance the radar sensing quality of the entire communication frame. In addition, theoretical analysis of the performance gain of such radar AF fusion is provided. Both theoretical analysis and simulation results demonstrate that the newly proposed SC-OFDM GCC preamble design and the minimum-point fusion of data AF and preamble AF provide a significant performance improvement for automotive radar sensing. Specifically, the proposed SC-OFDM GCC preamble with minimum-point processing algorithm improves the peak-to-max-sidelobe ratio (PMSR) of radar AF over the conventional 802.11ad/802.11ay preamble. Moreover, the proposed preamble-data AF fusion technique further enhances the PMSR as well as the Doppler resolution over the existing joint radar communication systems (which process either the preamble echo or the data echo only). Finally, the proposed minimum-point processing and data AF fusion are extended from single-frame to multi-frame processing to further improve the Doppler resolution. Submitted/Accepted version The work of Pingzhi Fan was supported by NSFC Project No. 62020106001.

Vibration measurement is a crucial task in industrial systems, where vibration characteristics reflect health conditions and indicate anomalies of the devices. Previous approaches either work in an intrusive manner or fail to capture the micrometer-level vibrations. In this work, we propose mmVib, a practical approach to measure micrometer-level vibrations with mmWave radar. First, we derive a metric called Vibration Signal-to-Noise Ratio (VSNR) that highlights the directions of reducing measurement errors of tiny vibrations. Then, we introduce the design of mmVib based on the concept of Multi-Signal Consolidation (MSC) for the error reduction and multi-object measurement. We implement a prototype of mmVib, and the experiments show that it achieves 3.946% relative amplitude error and 0.02487% relative frequency error in median. Typically, the average amplitude error is only 3.174um when measuring the 100um-amplitude vibration at around 5 meters. Compared to two existing mmWave-based approaches, mmVib reduces the 80th-percentile amplitude error by 69.21% and 97.99% respectively.

The integration of wireless sensors into the automotive industry is making vehicles increasingly safe and autonomous. At present, vehicles are equipped with external sensors that facilitate maneuverability and braking and internal sensors that detect presence, seat belt state, and occupant vital signs. Early detection of dysfunctional breathing patterns can reduce the risk of accidents caused by drowsiness or fatigue. In this regard, this work presents an MIMO FMCW Radar System able to distinguish dysfunctional breathing patterns from human torso motion. The torso has been divided into six regions, and the amplitude of the elongation and the frequency of motion of each region have been measured during breathing. An elongation map is then constructed in order to graphically show the dysfunctional breathing patterns. The signal processing includes a peak search algorithm to detect elongation amplitude and a bandpass frequency filter to minimize dc components, random driver motion, and vehicle vibration. A torso elongation emulator phantom has been assembled with materials of skin-like relative permittivity and dc motors for calibration and validation of basic breathing patterns. Finally, the signal of the system is compared with that of a commercial respiration belt, and then the system is tested by performing measurements on people. The MIMO radar system is able to measure, differentiate, and classify patterns associated with dysfunctional breathing such as hyperventilation syndrome and thoracic dominant breathing. This work was supported in part by the Spanish “Comision Interministerial de Ciencia y Tecnologia” (CICYT) under Project PID2019-107885GB-C31 and Project MDM2016- 0600, in part by the Catalan Research Group under Grant 2017 SGR 219, and in part by the “Secretaría Nacional de Educación Superior, Ciencia, Tecnología e Innovación” (SENESCYT) from the Ecuadorian Government

The ambiguity problem of targets in Doppler beam sharpening (DBS) with forward-looking radar is considered. While DBS is proposed earlier to improve the angular resolution of the radar while keeping the antenna aperture size limited, such a solution suffers from ambiguities in the case of targets positioned symmetrically with respect to the platform movement. To address this problem, an approach named unambiguous Doppler-based forward-looking multiple-input multiple-output (MIMO) radar beam sharpening scan (UDFMBSC) is proposed, based on the combination of MIMO processing and DBS. The performance of the proposed method is compared to existing approaches using simulated data with point-like and extended targets. The method is successfully verified using experimental data.

Linked to the increasing availability of datasets for radar-based human activity recognition (HAR), in this Student Highlights contribution, we report on a classification project that a group of 23 graduate students performed at TU Delft. The students were asked to work in groups of 2-3 members and to use the publicly available University of Glasgow dataset to develop the best classification pipeline as possible. This involved development and justification of both choices for the preprocessing techniques on the radar data (e.g., time-frequency distributions and cleaning of the signatures), and for the classification algorithms (e.g., the type of the algorithm, the hyperparameters' selection, the training-validation-testing split). While this student activity was performed at a small scale and with educational rather than research aims, we are happy to report it to the AESS readership, as we believe that such initiatives with open datasets sharing and classification algorithm benchmarking are beneficial for the wider radar research community. Furthermore, a list of publicly available datasets for radar-based HAR that can be used for similar initiatives is also reported in this article.

Mutual interference between different radar waveforms used in automotive radar applications is studied. The existing interference analysis is extended to a generalised radar‐to‐radar interference equation that covers most of the common interference scenarios for automotive radar systems. The outcome of the generalised equation is demonstrated for a number of typical scenarios where radars with different continuously transmitting waveforms are involved. The proposed equation can be used to characterise the received interference and its features by analysing the instantaneous beat frequency of the victim radar. Moreover, an interference analysis of phase‐coded frequency‐modulated continuous waveforms is performed and demonstrated experimentally by using real‐time automotive radars for the first time in the literature. The experimental results corroborate the interference analysis of different waveforms and validate the proposed generalised interference equation under various conditions.

The Doppler shift obtained from synthetic aperture radar (SAR) measurements comprises the combined contribution to the radial motion of the ocean surface induced by the sea state (wind waves and swell) and underlying surface currents. Hence, to obtain reliable estimates of the ocean surface current, the sea-state-induced Doppler shifts must be accurately estimated and eliminated. In this study, we use a semiempirical dual co-polarization Doppler velocity (DPDop) model, presented in the companion paper, to calculate sea-state-induced Doppler shifts using buoy-measured wind speed, wind direction, and ocean wave spectra. The DPDop model-simulated Doppler shifts are compared with the collocated Sentinel-1B SAR Wave (WV) mode observations at the 24° and 37° incidence angles, showing a bias of -0.24 Hz and a root-mean-square error (RMSE) of 5.55 Hz. This evaluation is also implemented on a simplified DPDop model at the same incidence angles. The model inputs include wind fields from the European Center for Medium-Range Weather Forecasts (ECMWF) and wave characteristic parameters (e.g., significant wave height, mean wave direction, and mean wavenumber) from WAVEWATCH III (WW3). The estimated Doppler shifts are validated using the ascending and descending observations of Sentinel-1B WV over the global ocean. Furthermore, the comparisons show that the bias and RMSE are -0.71 Hz and 9.25 Hz, respectively. Based on accurate wave bias correction, we obtain the radial current speeds of the ocean surface from the Doppler shift measurements. The estimated current speeds are compared with the collocated high-frequency radar measurements, with a bias of -0.04 m/s and an RMSE of 0.15 m/s. These results suggest that the original and simplified DPDop models can be used to estimate sea-state-induced Doppler shifts and, thus, derive accurate surface current retrievals.

This study developed a novel S-band radar system for planar location applications. High-resolution range imaging and target angle estimation were achieved by using a stepped frequency continuous wave (SFCW) signal and single input multiple output (SIMO) architecture with a linear sparse array layout, respectively. An improved time-frequency method was utilized to link the independent range profile and angle spectrum results to obtain the plane positions of the targets. The radar hardware was composed of the antenna array with one transmit element and five receive elements, an RF transceiver, and a signal processing component. Under the proposed waveform parameters and signal processing scheme, a 16-ms process cycle, 0.3-m ranging error, and 0.4° angle estimation error for target positioning were achieved in field tests. These results demonstrate the effectiveness and advantages of the proposed radar system.

Radar-based gesture recognition can play a vital role in autonomous vehicles’ interaction with vulnerable road users (VRUs). However, in automotive scenarios the same gesture produces strongly differing radar responses owed to the wide range of variations such as position, orientation, or ego-motion. Since including all kinds of modifications in a measured dataset is laborious, gesture simulations alleviate the measurement effort and increase the robustness against edge and corner cases. Hence, this paper presents a flexible geometric human target model allowing the direct introduction of a wide range of modifications, while it facilitates the handling of shadowing effects and multiradar constellations. Using the proposed simulation model, a dataset recorded with a radar sensor network consisting of three chirp sequence (CS) radars is resimulated based on motion data simultaneously captured with a stereo video system. Completely substituting the measured by simulated data for training, a convolutional neural network (CNN) classifier still achieves 80.4% cross-validation accuracy on a challenging gesture set, compared to 89.4% for training on measured data. Moreover, using simulated data the classifier is shown to successfully generalize to new scenarios not observed in measurements., acceptedVersion

This work investigates the problem of unmanned aerial vehicles (UAVs) recognition using their radar cross-section (RCS) signature. The RCS of six commercial UAVs are measured at 15 GHz and 25 GHz in an anechoic chamber, for both vertical-vertical and horizontal-horizontal polarization. The RCS signatures are used to train 15 different recognition algorithms, each belonging to one of three different categories: statistical learning (SL), machine learning (ML), and deep learning (DL). The study shows that while the average accuracy of all the algorithms increases with the signal-to-noise ratio (SNR), the ML algorithm achieved better accuracy than the SL and DL algorithms. For example, the classification tree ML achieves an accuracy of 98.66% at 3 dB SNR using the 15 GHz VV-polarized RCS test data from the UAVs. We investigate the recognition accuracy using Monte Carlo analysis with the aid of boxplots, confusion matrices, and classification plots. On average, the accuracy of the classification tree ML model performed better than the other algorithms, followed by Peter Swerling’s statistical models and the discriminant analysis ML model. In general, the accuracy of the ML and SL algorithms outperformed the DL algorithms (Squeezenet, Googlenet, Nasnet, and Resnet 101) considered in the study. Furthermore, the computational time of each algorithm is analyzed. The study concludes that while the SL algorithms achieved good recognition accuracy, the computational time was relatively long when compared to the ML and DL algorithms. Also, the study shows that the classification tree achieved the fastest average recognition time of about 0.46 ms.

This article presents and experimentally evaluates a frequency error elimination technique suitable for unsynchronized bistatic Multiple-Input Multiple-Output (MIMO) radar for human-body detection. First, a mathematical expression of human-body localization using bistatic MIMO radar is presented. Then the direct path is used to eliminate the phase error created by the frequency difference between the transmitter and receiver. A new Doppler-shifted component of the MIMO channel without phase error is derived, and the locations of the multiple targets are calculated by the 2-dimensional MUltiple SIgnal Classification (MUSIC) method. Next, the results of simulations that examine frequency error versus power ratios are discussed to illustrate the effectiveness of the proposed method. An experiment is carried out in an indoor multipath-rich environment. To emulate the unsynchronized condition, the transmitter and receiver use independent Signal Generators (SGs). One to six targets are tested. The experiments demonstrate that our unsynchronized radar system can identify the locations of multiple targets with high accuracy.

Recently, radar frequency bands have attracted attention as candidates for cognitive radio owing to their wide bandwidth but low utilization. When spectrum sensing is performed in radar bands, sliding-window-based detection can be used to exploit the sparsity of the radar pulse signal in the time domain and obtain a sufficient number of samples. The detection performance and sensing time depend on the configuration of the windows. The detection performance was optimized when the window size was equal to the pulsewidth. However, the pulsewidth is generally unknown. Another way to increase the detection performance is to obtain more samples of the window. However, this causes large computation overhead owing to the sparsity of radar signals. Therefore, we propose an adaptive-sliding-window-based detection scheme to address these problems. First, the window is adaptively applied. Second, a pulsewidth estimation algorithm is proposed to approximate the window size to the pulsewidth for each detection. Additionally, we demonstrate the performance improvement of the proposed scheme and evaluate the estimation error of the proposed algorithm via simulations.

Reasonable route planning and resource allocation strategy in the airborne radar systems (ARS), can sufficiently utilize the limited resources and promote the multitarget tracking (MTT) performance. However, using separately the route planning and resource optimization method cannot take full advantage of the airborne platform. Considering this issue, we propose a joint online route planning and resource optimization strategy in the ARS to improve the system capability for MTT. First, a kinematic model of the ARS, including the mathematical expression between the radar states and the system control parameters, is introduced, which integrates the route planning to the radar scheduling scheme. Next, the posterior Cramer–Rao lower bound about route planning and resource optimization variables for the tracking targets is derived. Then, a scaled-based utility function is established to quantify the MTT performance. Hereafter, a nonconvex problem is formulated by minimizing the utility function with route and resource constraints, and then a efficient three-stage partition-based solution is proposed. Finally, simulation experiments demonstrate the effectiveness of the proposed algorithm. Furthermore, over the traditional benchmark algorithm, the tracking performance of the proposed approach improves 30.44%.

Target detection is an important problem in multipleinput multiple-output (MIMO) radar. Many existing target detection algorithms were proposed without taking into consideration the quantization error caused by analog-to-digital converters (ADCs). This paper addresses the problem of target detection for MIMO radar with one-bit ADCs and derives a Rao's test-based detector. The proposed method has several appealing features: 1) it is a closed-form detector; 2) it allows us to handle sign measurements straightforwardly; 3) there are closed-form approximations of the detector's distributions, which allow us to theoretically evaluate its performance. Moreover, the closed-form distributions allow us to study the performance degradation due to the one-bit ADCs, yielding an approximate 2 dB loss in the low-signal-to-noise-ratio (SNR) regime compared to 34-bit ADCs. Simulation results are included to showcase the advantage of the proposed detector and validate the accuracy of the theoretical results. The work of David Ramírez was supported in part by the Ministerio de Ciencia e Innovación, jointly with the European Commission (ERDF) under Grant PID2021-123182OB-I00 (EPiCENTER) and in part by the Comunidad de Madrid under Grant Y2018/TCS-4705 (PRACTICO-CM). The work of Lei Huang was supported in part by the National Science Fund for Distinguished Young Scholars under Grant 61925108 and in part by the Joint fund of the National Natural Science Foundation of China and Robot Fundamental Research Center of Shenzhen Government under Grant U1913203.

Wideband two‐dimensional multiple input multiple output (MIMO) radar can be used for target three‐dimensional (3‐D) imaging. As the echo of wideband MIMO radar is derived from both time and spatial sampling, the time‐frequency coupling is contained, resulting in imaging defocussing. To solve this problem, a time‐frequency decoupling algorithm based on matrix enhancement and matrix pencil (MEMP) is proposed to achieve high‐resolution 3‐D imaging. By combining the enhanced matrix constructed via MEMP with the modern spectral estimation theory, the time‐frequency coupling term is regarded as a space rotation factor and is eliminated in the process of target position estimation. Simulation results show that the proposed algorithm can reduce the negative influence of time‐frequency coupling and improve the imaging accuracy of wideband two‐dimensional MIMO radar.

In-band full-duplex (IBFD) technology has the potential to not only double the communication throughput but also enable additional capabilities. The fusion of radar and communication subsystems is an excellent example of how IBFD facilitates integration of radar functionality into a communication system. Developing and analysis of a joint IBFD radar-communication (RadCom) system is at the core of this article. This system derives the range-Doppler image from the state of an adaptive filter, which already exists in a typical IBFD transceiver. Our approach is waveform-independent and reuses the radio frequency (RF) front-end while it requires little additional digital logic. The proposed system is prototyped to assess the modem-radar coexistence in a real-world IBFD communication link budget. Employing the prototyped system, we quantify the additional required logic resources and investigate whether such a RadCom approach dictates a tradeoff between the two intended functionalities. The experimental results show that the proposed solution enables a communication system to detect targets within 20 m while maintaining an IBFD link with another communication node, info:eu-repo/semantics/published

High-performance IC-to-antenna interconnection is one of the key enablers for the mass production of high-end millimeter wave (mmW) radar systems above 100 GHz. In this work, a radar system with an on-package antenna array working at 122 GHz is presented. The antenna is placed on top of the molded package and the antenna-to-chip interconnection is realized by through-polymer via (TPV) technology. The detailed fabrication process of the radar antenna-in-package (AiP) with TPV is discussed. The results from the functional tests of the radar AiP are presented and benchmarked to a commercial quad-flat no-leads (QFN) package with an open antenna cavity. The detection margin between the echo signal and the constant false alarm rate (CFAR) threshold is approximately 10 dB higher for the TPV radar AiP compared with the benchmarked commercial QFN package.

This study focusses on the target detection problem in the distributed multiple‐input multiple‐output (MIMO) radar. The observation environment is assumed to be partially homogeneous in one transmitter‐receiver path and simultaneously non‐homogeneous for different paths. By characterising the disturbance signals with a series of auto‐regressive (AR) processes, two parametric detectors based on the Wald test are developed. The first one utilises an iterative scheme to estimate the unknown parameters that were involved in the derivation, and the second one obtains these estimations sequentially. One of the most attractive features of the two distributed MIMO radar detectors is that they can be used in the presence/absence of the training data. More specifically, they can take advantage of the training data to boost the detection performances effectively even if the training data are very limited. The simulation results of several scenarios demonstrate the remarkable detection performances of authors’ proposed methods compared with that of the competitors. Furthermore, the amplitude estimation performances of the two detectors are evaluated via the corresponding Cramér–Rao bound (CRB).

The main‐lobe jamming causes serious degradation of radar system performance in practical applications. In recent years, the Blind Source Separation (BSS) method has been applied to suppress the main‐lobe jamming. However, the received echoes of target and jamming cannot be effectively identified while the robustness of the conventional BSS method is poor due to the influence of system noise. To tackle these problems, a radar main‐lobe jamming suppression and identification method is proposed based on robust whitening BSS and Convolutional Neural Networks methods. First, the target echoes and jamming are separated by the proposed robust whitening BSS method. An iterative optimisation method is proposed to obtain the robust whitening matrix, and sufficient conditions are provided for solvability. Then, a Target echo and Jamming Identification Network (TJINet) is proposed to identify the signals after the robust whitening BSS. The proposed TJINet method uses residual blocks and branch structures to improve classification performance and utilises asymmetric convolution blocks to extract subtle features between the target echo and deceptive jamming. Simulation and experimental results show that the proposed method has better jamming suppression performance than the traditional methods under low signal‐to‐noise ratio (SNR) conditions, the algorithm converges faster, and also can stably identify the target echo and jamming signals.

Unimodular waveforms with good correlation properties are desired for multiple‐input‐multiple‐output radar to achieve an increased transmitting/receiving virtual aperture. In this study, a new optimisation framework is introduced to design waveforms with good correlations. It is shown that many existing waveform design problems can be incorporated into this framework. Since the considered problem is in general highly non‐linear and non‐convex, an iterative optimisation method based on majorisation‐minimisation is developed. By carefully devising the surrogate function, the only requirement is to deal with a series of simple problems, which have closed‐form solutions. The proposed algorithm can be implemented via fast Fourier transform and hence is computationally efficient. In addition, the proposed algorithm is extended to deal with spectral constraints. Numerical results demonstrate the efficiency of the proposed algorithm compared with the state‐of‐the‐art techniques.

With the mass adoption of automotive vehicles, road accidents have become a common occurrence. One solution to this problem is to employ safety systems that can provide early warning for potential accidents. These systems alert drivers to brake or take active control of a vehicle in order to make braking safer and smoother, thereby protecting drivers and all other road traffic participants. Most such safety systems utilize millimeter-wave radar as primary sensors, and one of the main challenges is real-time data processing from multiple sensors integrated into a single passenger car. When an obstacle is too close to a vehicle, often there is insufficient time to run higher-order digital signal processing algorithms; hence, the decision to brake must be made based on low-level hardware processing only. For that purpose, a hardware generator for the early detection of automotive obstacles that does not impede the operation of higher-order signal processing algorithms is described. The proposed generator is captured in the Chisel hardware design language and a method for reducing the overall ranging latency is presented. The system constraints are calculated using an exemplary radar front-end and the proposed generator parameters. The obtained analytical results are experimentally confirmed with a prototype composed of a typical industrial radar front-end while the signal processing back-end instance of the described generator was implemented on an FPGA board. The measurements demonstrate that with the fast proximity alert, objects can be detected in less than a hundred microseconds, thus considerably reducing the system reaction delay and braking distance.

A respiratory disorder that attacks COVID-19 patients requires intensive supervision of medical practitioners during the isolation period. A non-contact monitoring device will be a suitable solution for reducing the spread risk of the virus while monitoring the COVID-19 patient. This study uses Frequency-Modulated Continuous Wave (FMCW) radar and Machine Learning (ML) to obtain respiratory information and analyze respiratory signals, respectively. Multiple subjects in a room can be detected simultaneously by calculating the Angle of Arrival (AoA) of the received signal and utilizing the Multiple Input Multiple Output (MIMO) of FMCW radar. Fast Fourier Transform (FFT) and some signal processing are implemented to obtain a breathing waveform. ML helps the system to analyze the respiratory signals automatically. This paper also compares the performance of several ML algorithms such as Multinomial Logistic Regression (MLR), Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), eXtreme Gradient Boosting (XGB), Light Gradient Boosting Machine (LGBM), CatBoosting (CB) Classifier, Multilayer Perceptron (MLP), and three proposed stacked ensemble models, namely Stacked Ensemble Classifier (SEC), Boosting Tree-based Stacked Classifier (BTSC), and Neural Stacked Ensemble Model (NSEM) to obtain the best ML model. The results show that the NSEM algorithm achieves the best performance with 97.1% accuracy. In the real-time implementation, the system could simultaneously detect several objects with different breathing characteristics and classify the respiratory signals into five different classes.

This study presents a design of a 94-GHz high-performance and highly compact frequency-modulated continuous-wave radar sensor. In this sensor, an X-band CMOS-based all-digital phase-locked loop chip, W-band SiGe-based transceiver monolithic microwave integrated circuit (MMIC), and W-band GaN-based MMIC power amplifier (PA) are heterogeneously integrated (HI). Each part of the 130-nm bipolar complementary metal-oxide-semiconductor (BiCMOS) SiGe-based transceiver MMIC is designed to include a low-noise amplifier, PA, octupler, mixer, and lange coupler. The fabrication process of our in-house silicon-based MEMS photosensitive composite film is developed to provide very high-density integration and very low insertion loss of interconnections between chips. The HI front end of the radar sensor is measured on-wafer with a high output power of 22 dBm. Moreover, a low double-sideband noise figure (NF) of 10.2 dB is obtained. Bonding-substrate integrated waveguide (SIW)-WG transitions between the heterogeneously integrated front end and the antenna are specially designed and verified. A 2.3-dB degradation is observed, where 19.7 dBm is measured at the transmitter WG WR-10-flange. Thus, the sensor offers a 55-dB dynamic range at a 2-m position with an expectation of a long-distance target-detection range. The radar sensor has an 11.7-cm range resolution and is only 60 x 40 x 8 mm³ in size, with a weight of only 78 g.

Miniature radar has demonstrated its great potential in smart homes to understand the wellness of the residents and provide ubiquitous interactions. While it has many promising applications, it also results in congested RF (radio frequency) environments as there is an unprecedented amount of traffic in a smart home. To ease the strain on the limited spectrum, we ask the question that, can we reuse the sensing signals for data communication With such a capability, we can improve the spectrum utilization by sharing the spectrum between sensing and communication systems. However, radar signals are customized for the sensing purpose and are incompatible with legacy communication standards. To address this challenge, we have an observation that, non-linearity effect in RF circuits can convert wideband radar signals into a LoRa signal. Based on this observation, in this paper, we present LoRadar, which enables an FMCW (Frequency-Modulated Continuous Wave) radar to carry LoRa signals in sensing waves. We present both the downlink and uplink design, enabling a LoRadar device to communicate with LoRa nodes in a bi-directional way. We implement LoRadar and evaluation results show that LoRadar can achieve home-level coverage with 3:4kbps data rate while it preserves the sensing resolution of the radar.

Deinterleaving mixtures of radar pulse sequences is the first and the most vital step for modern electronic reconnaissance systems to intercept and analyze the intentions of non-cooperative radars. Currently, these systems are facing great challenges due to the emerging complexity of radar modulations. This paper proposes a novel method for deinterleaving mixtures of radar pulse sequences based on the time series characteristics of each source (component pulse sequences). Firstly, the mathematical representations are established to describe the structural characteristics of each source. Then, the deinterleaving problem is formulated as a minimization of a maximum-likelihood cost function that can be solved efficiently through a supervised neural machine translation (NMT) network, i.e. to translate each received pulse in the interleaved pulse sequence to the corresponding source label. A sequence-to-sequence NMT model is proposed to capture the structural relationships among the non-adjacent pulses originated from the same source in the mixtures and assign the corresponding label to each pulse. The proposed method does not require the exact knowledge of each component pulse sequence. The experimental results based on the time of arrival sequence of mixed pulse sequences show that the proposed method outperforms the state-of-the-art deinterleaving methods and achieves satisfactory performance under highly non-ideal situations with measuring noise and lost pulse conditions. The proposed method can be directly applied to other fields involving deinterleaving problems and multi-variate input conditions.

In recent years, machine learning theory has set off a wave of research in the field of radar signal processing. In this study, a novel algorithm for sea‐surface target detection based on a stacked autoencoder (SAE) is proposed, which has already been applied in the authors’ coastal defense radar system. In the proposed algorithm, the sea surface echo data are cut into a large number of two‐dimensional (2‐D) images through a sliding window, mapping the cell under test (CUT) and the 2‐D images one by one and performing classification or detection from the perspective of 2‐D signal processing. Experimental results of simulated data and real radar data show that the proposed algorithm based on the SAE has better target detection performance compared with the traditional cell averaging constant false alarm rate (CA‐CFAR) algorithm. Besides, the proposed algorithm shows certain interference suppression ability in real data processing. As far as it is known, there is no public report on the detection of 2‐D sea surface targets based on the SAE algorithm in coastal defense radar.

In this work, a new method for hand character gesture recognition based on a single millimetre‐wave radar chip is proposed. Most current radar‐based gesture recognition approaches use Doppler spectrograms or the feature extraction of Doppler spectrograms. In contrast, the authors propose using the trajectory points of gesture movements. Compared with the previous methods, the variations in the gesture movement speed, distance and direction during data acquisition have relatively little impact on the generated trajectories. The character trajectories drawn by gestures in the air output a fixed shape, and this fixed output facilitates the classification of gestures at a later stage. In this work, a single millimetre‐wave radar is used to collect data and a three‐dimensional fast Fourier Transform (3D‐FFT) algorithm is used to obtain the gesture trajectories. For the trajectory points, a trajectory conversion image method is proposed and finally the improved Xception network proposed in this work is used for image recognition. Compared with traditional methods, the proposed method does not require large‐scale data acquisition to facilitate different users. This work is concluded by recognising hand character gestures and comparing them with other commonly used classification methods to verify the gesture recognition accuracy of the proposed method.

The Back‐Projection Algorithm (BPA) is a time‐domain‐matched filtering technique to form synthetic aperture radar (SAR) images. To produce high‐quality BPA images, precise navigation data for the radar platform must be known. Errors in position, velocity, or attitude result in improperly formed images that are corrupted by shifting and blurring. The contribution of this paper is the development of analytical expressions that characterise the relationship between navigation errors and image formation errors from an inertial navigation point of view, where trajectory estimation errors in position, velocity, and attitude propagate through time and cause compounding errors in the vehicle state vector. These analytical expressions are verified via simulated image formation and real‐data image formation.

Using a microwave or millimetre wave radar system, this study aims to achieve highly accurate Doppler velocity estimation and radar imaging that would be suitable for various remote‐sensing sensors, such as self‐driving, surveillance, or security applications. In particular, micro‐Doppler signatures are one of the most promising approaches for human recognition; however, the traditional limitations of both velocity and temporal resolution need to be addressed. The weighted kernel density (WKD) algorithm using super‐resolution Doppler velocity estimation has been one solution. Using WKD, this study incorporates the range points migration (RPM) method of radar imaging to enhance accuracy in both Doppler velocity and radar imaging using an iterative data selection scheme. Furthermore, to obtain more informative Doppler‐associated RPM‐based images using less data by exploiting a unique feature of WKD and RPM methods, this study introduces the image integration approach along the slow‐time profile. In this framework, bi‐directional data processing between Doppler velocity and imaging analysis is achieved. At each pulse hit sequence, both numerical and experimental tests demonstrate that the proposed method yields a more accurate Doppler‐associated radar image compared with the methods in previous studies.

Synthetic Impulse and Aperture Radar (SIAR) is a kind of Multiple Input Multiple Output (MIMO) radar. It is a four‐dimensional radar that has advantages against stealth targets, multi targets, anti‐radiated missile, reconnaissance and interference, as well as valuable features in high range resolution and high detection probability. SIAR uses special frequency coding separately in each antenna. This code has an essential function in SIAR, such as separating frequency bandwidth, satisfying orthogonality in transmitted signals, suppressing range‐angle and range‐Doppler frequency coupling. Pulse‐to‐pulse frequency code agility is a kind of radar signal coding that is used to suppress the sidelobes in range‐Doppler frequency. In this study, a design method is suggested to transmit waveform based on non‐sequential positive, negative and random carrier frequency coding with the dot product approach in pulse‐to‐pulse frequency code agility, to reduce sidelobes in range‐Doppler frequency coupling. The simulation results showed that this method could effectively remove the strong coupling between range‐Doppler frequency. Removing coupling in the range‐Doppler frequency decreases the sidelobes.

This paper presents an accurate, highly efficient, and learning-free method for large-scale odometry estimation using spinning radar, empirically found to generalize well across very diverse environments -- outdoors, from urban to woodland, and indoors in warehouses and mines - without changing parameters. Our method integrates motion compensation within a sweep with one-to-many scan registration that minimizes distances between nearby oriented surface points and mitigates outliers with a robust loss function. Extending our previous approach CFEAR, we present an in-depth investigation on a wider range of data sets, quantifying the importance of filtering, resolution, registration cost and loss functions, keyframe history, and motion compensation. We present a new solving strategy and configuration that overcomes previous issues with sparsity and bias, and improves our state-of-the-art by 38%, thus, surprisingly, outperforming radar SLAM and approaching lidar SLAM. The most accurate configuration achieves 1.09% error at 5Hz on the Oxford benchmark, and the fastest achieves 1.79% error at 160Hz., Comment: Published in Transactions on Robotics. Edited 2022-11-07: Updated affiliation and citation

This paper presents a review of radar applications in high-accuracy distance measurement of a target. The radars included in this review are frequency modulated continuous wave (FMCW) radar sensors operating in four different millimeter-wave frequency bands, namely 24 GHz, 61 GHz, 80 GHz and 122 GHz. The radar sensors are used to measure the distance of standard and complex targets in a short range of a few meters, thus indicating that the choice of target and the medium used for radar signal propagation also play a key role in determining the distance measurement accuracy of an FMCW radar. The standard target is a trihedral corner reflector in a laboratory-based free space measurement setup and the complex targets include a piston in an oil-filled hydraulic cylinder and a planar positioning stage used in micromachining. In each of these measurement scenarios, a distance measurement accuracy in micrometer range is achieved due to the use of a sophisticated signal processing algorithm that is based on a combined frequency and phase estimation method. The paper is concluded with a technical comparison of the accuracy achieved by the FMCW radars reviewed in this article with other related works.

The performances of a multiple-input multiple-output (MIMO) radar, employing 16 equivalent antennas, and multiple-input single-output (MISO) radar, employing 10 antennas, for through-the-wall imaging applications are analyzed. In particular, imaging algorithms based on the Fourier transform (FT) and the multiple signal classification (MUSIC) available in the literature are compared with the FT-MUSIC hybrid algorithm recently developed by the authors. Three different investigations have been performed. The first, performed analytically, refers to a scenario in which a point scatterer is placed in free space, and the second, addressed numerically using the CST full-wave software, refers to a scenario in which two targets are present, while the last was executed in a real scenario where a metal panel is placed behind a tuff wall. All the algorithms and radar configurations were found to be suitable for accurately reconstructing the position of the investigated target. In particular, applying the FT technique, the MISO configuration has a lower cross-range half-power beamwidths (HPBW) than the MIMO one, while the range HPBW is the same for the two radar configurations. Despite the different number of elements present in the two radar configurations, similar range and cross-range HPBW are obtained for both configurations when MUSIC and FT-MUSIC techniques are employed. The field of view for FT and FT-MUSIC is about 45°, while it is less than 15° for the MUSIC algorithm. The HPBWs obtained with the experimental setup are very close to those obtained in the analytical study. Finally, the proposed experimental MISO radar acquires the data in half the time required by the MIMO one. The numerical results, confirmed by the experimental measurements, seem to indicate in the FT-MUSIC technique the one that provides the best performance for the considered radar configurations.

We draw your attention to the fact that meteorological radar does not actually measure a commonly used quantity “radar reflectivity factor,” (which is not dependent on frequency) but a different quantity called “radar reflectivity.” We present the usual recalculation which is based on frequency dependency used by Rayleigh approximation of radar cross-sections (back scattering cross section of rain, cloud, fog drop). But this approximation is valid in Rayleigh region only. We concluded that for admitting error lower than 2 dB in the radar reflectivity factor determination we can use the “effective radar reflectivity factor” for frequencies up to 19 GHz only. Otherwise the error will increase. As we use (and present in this article) the Mie algorithm we can replace the Rayleigh frequency dependence estimation by more accurate radar reflectivity factor determination using the Mie scattering. The correction is presented in the form of “Correction function C” dependent on frequency and rain rate in the graphical form and polynomial approximation. Beside this we present the simplification of back scattering cross sections for Rayleigh and Optical regions and the borders values of size parameter for these regions. We added the meteorological radar equation derivation. This should support the radar measurement understanding.

This article presents a radar testbed for speed detection through micro-doppler effect in a controlled environment using software defined radio (SDR) technology. The target moves along a conveyor belt with software-controlled speed. The speed is detected by an SDR radar, and it is possible to compare it to an encoder-based sensor implemented on the testbed. The testbed as well as the SDR radar are reconfigurable and a (continuous wave) CW radar was implemented for the validation of the testbed; however, the testbed is not limited to this implementation. The testbed can be remotely operated because it includes the mechanism to move the target and control its velocity. The article shows the way in which the testbed was designed and implemented, the generation and processing of the radar signal using a limeSDR, and the validation of the radar measurements compared to the encoder-based speed sensor. The maximum speed obtained by the target in the testbed is 15.69cm/s. Results show a difference in the speed measured with the SDRadar of no more than 5% compared to the sensor measurememt. Results obtained allow characterizing the behavior of the SDR platform in the detection of low speeds.

Range-dependent clutter suppression is a challenging problem in non-sidelooking (NSL), bistatic, and conformal antenna array airborne radar, especially in the presence of range ambiguity. Superimposed stepped frequency (SSF) radar employs a small frequency increment across the stepped signals in each pulse, which induces a dimension related to slant range. In this paper, an airborne radar framework is established with SSF signal as the transmit waveform. Thus, a two-stage adaptive clutter suppression method is proposed which utilizes the degrees of freedom in range, space and time domains provided by airborne SSF radar. In the first stage, the secondary range dependence compensation (SRDC) approach is adopted to distinguish the clutter of each range region in the carrier frequency domain. Then, a target-free covariance matrix is estimated by the compensated data, and the covariance matrix is used for the range-ambiguous clutter separation in the carrier frequency domain. Thus, the space-time snapshot of each range region can be extracted. In the second stage, a clutter segmentation processing method is devised for residual clutter suppression.

There are two types of important information in a polarimetric synthetic aperture radar (PolSAR) image: spatial features in two dimensions and polarimetric characteristics in the scattering dimension. Considering both polarimetric and spatial information is important for PolSAR image classification. Convolutional kernels show superior performance for extraction of spatial information from two dimensions of an image in convolutional neural networks (CNNs). But learning CNNs needs large enough training sets to achieve the optimum weights of kernels while there are not usually sufficient training samples for PolSAR images. To deal with this difficulty, a convolutional kernel‐based covariance descriptor (CKCD) is introduced for PolSAR image classification in this study. To extract contextual characteristics, compatible with the original image, the fixed‐valued convolutional kernels randomly selected from the image are used, which do not require any learning, and so do not need any training samples. To include more local spatial information and find the relation among the polarimetric features, the covariance descriptor is constructed on the extracted feature maps. Then, the polarimetric‐contextual features are given to a support vector machine with a matrix logarithm‐based kernel. Finally, the guided filter is applied to the initial classification map to result a smoothed classification map with preserved edges. The experiments on three real PolSAR images show superiority of the proposed CKCD method compared to several PolSAR classification methods such as 2DCNN and 3DCNN in small sample size situations.

For autonomous driving vehicles, highly accurate representations of the environment are essential for both trajectory planning and self-localization. Different possibilities allow to generate detailed maps of the environment based on chirp-sequence radar sensors for advanced driver assistance systems (ADASs). For the first time, this letter shows a qualitative comparison between synthetic aperture radar (SAR)- and occupancy grid map (OGM)-based environment representation using identical measurement data. The differences of existing signal processing chains as well as a visual measurement-based comparison of the resulting environmental maps is presented., acceptedVersion

Funding Information: This work was supported in part by Saab AB. The work of Mikko K. Leino was supported in part by the Walter Ahlstr?m Foundation, in part by the Jenny and Antti Wihuri Foundation, and in part by the Nokia Foundation. Publisher Copyright: © 1963-2012 IEEE. A periodically loaded, low-scattering dipole suitable for the secondary surveillance radar, particularly for identification-friend-or-foe antenna, is presented along with the methods to design such an antenna. The dipole is chopped into the pieces with length that minimizes scattering at 10 GHz, i.e., at the operation frequency of the primary antenna. In addition, optimal inductive loading is found to connect the pieces together so that the formed entity can radiate at 1 GHz, i.e., at the operation of frequency of the secondary antenna. Two meander-line designs are presented to realize the required inductances, and the proposed structures are realized on a printed circuit board. Designed prototypes are analyzed through simulations and measurements, which are in good agreement. The reduction in radar cross section at 10 GHz with the inductively loaded dipoles is at best more than 15 dB, when compared to the reference half-wave dipole. As a consequence, the radiation efficiency at 1 GHz decreases by 0.4 dB withthe inductively loaded dipoles.

Accurately detecting falls and providing clear directions for the fall can greatly assist medical staff in promptly developing rescue plans and reducing secondary injuries during transportation to the hospital. In order to facilitate portability and protect people’s privacy, this paper presents a novel method for detecting fall direction during motion using the FMCW radar. We analyze the fall direction in motion based on the correlation between different motion states. The range–time (RT) features and Doppler–time (DT) features of the person from the motion state to the fallen state were obtained by using the FMCW radar. We analyzed the different features of the two states and used a two-branch convolutional neural network (CNN) to detect the falling direction of the person. In order to improve the reliability of the model, this paper presents a pattern feature extraction (PFE) algorithm that effectively eliminates noise and outliers in RT maps and DT maps. The experimental results show that the method proposed in this paper has an identification accuracy of 96.27% for different falling directions, which can accurately identify the falling direction and improve the efficiency of rescue.

Automotive radar pursues low cost and high performance, and especially hopes to improve the angular resolution under the condition of a limited number of multiple-input–multiple-output (MIMO) radar channels. Conventional time division multiplexing (TDM) MIMO technology has a limited ability to improve the angular resolution without increasing the number of channels. In this paper, a random time division multiplexing MIMO radar is proposed. First, the non-uniform linear array (NULA) and random time division transmission mechanism are combined in the MIMO system, and then a three-order sparse receiving tensor of a range-virtual aperture-pulse sequence is obtained during echo receiving. Next, this sparse three-order receiving tensor is recovered by using tensor completion technology. Finally, the range, velocity and angle measurements are completed for the recovered three-order receiving tensor signals. The effectiveness of this method is verified via simulations.

For the investigation of moisture and salt content in historic masonry, destructive drilling samples followed by a gravimetric investigation is still the preferred method. In order to prevent the destructive intrusion into the building substance and to enable a large-area measurement, a nondestructive and easy-to-use measuring principle is needed. Previous systems for moisture measurement usually fail due to a strong dependence on contained salts. In this work, a ground penetrating radar (GPR) system was used to determine the frequency-dependent complex permittivity in the range between 1 and 3 GHz on salt-loaded samples of historical building materials. By choosing this frequency range, it was possible to determine the moisture in the samples independently of the salt content. In addition, it was possible to make a quantitative statement about the salt level. The applied method demonstrates that with ground penetrating radar measurements in the frequency range selected here, a salt-independent moisture determination can be carried out.