FMCW Radar Sensor Research Articles

Evaluation of the Interference Performance of FMCW Radar Sensors in Dense Indoor Environments

Evaluation of the Interference Performance of FMCW Radar Sensors in Dense Indoor Environments

A 19‐GHz low‐phase‐noise frequency synthesizer for a K‐band FMCW radar sensor of detecting micro unmanned aerial vehicles

AbstractA 19‐GHz low‐phase‐noise PLL‐frequency synthesizer is presented in this letter that is intended to be used in a K‐band FMCW radar which requires strict phase noise for detecting a small unmanned aerial vehicle. The 17.9 to 19.5‐GHz PLL includes a Darlington cross‐coupled capacitive‐degeneration VCO which presents higher maximum attainable oscillation frequency (ƒtrans) and larger negative transconductance (gm), thus resulting in low phase noise. Manufactured using the SiGe BiCMOS process, the K‐band synthesizer demonstrates −114 dBc/Hz at 1 MHz offset and −138 dBc/Hz at 10 MHz offset from 19.456 GHz locking carrier frequency.

FMCW Radar Sensors with Improved Range Precision by Reusing the Neural Network.

This paper addresses the challenge of enhancing range precision in radar sensors through supervised learning. However, when the range precision surpasses the range resolution, it leads to a rapid increase in the number of labels, resulting in elevated learning costs. The removal of background noise in indoor environments is also crucial. In response, this study proposes a methodology aiming to increase range precision while mitigating the issue of a growing number of labels in supervised learning. Neural networks learned for a specific section are reused to minimize learning costs and maximize computational efficiency. Formulas and experiments confirmed that identical fractional multiple patterns in the frequency domain can be applied to analyze patterns in other FFT bin positions (representing different target positions). In conclusion, the results suggest that neural networks trained with the same data can be repurposed, enabling efficient hardware implementation.

Digit Recognition Using FMCW and UWB Radar Sensors: A Transfer Learning Approach

Digit Recognition Using FMCW and UWB Radar Sensors: A Transfer Learning Approach

Remote Material Characterization with Complex Baseband FMCW Radar Sensors

Remote Material Characterization with Complex Baseband FMCW Radar Sensors

AirLoop: A Simulation Framework for Testing of UAV Services

Sensor fusion is a critical aspect in autonomous drone navigation as several tasks, such as object detection and self-pose estimation, require combining information from heterogeneous sources. The performance of these solutions depends on several factors, such as the characteristics of the sensors and the environment, as well as the computing platforms, which can heavily impact their accuracy and response time. Carrying out such performance evaluations through real flight tests can be a resource-demanding, time-consuming, and, at times, risky process, which is why researchers often rely on simulation environments for testing and validating sensor fusion algorithms. The simulation environment should provide photorealistic environmental features, as well as a comprehensive set of sensors, in order to allow to test the most extensive set of sensor fusion algorithms. This paper presents AirLoop, an AirSim-based flight simulator for Hardware-in-the-Loop and Software-in-the-Loop algorithm testing and validation. AirLoop extends the sensor setup provided by AirSim with an FMCW RADAR sensor simulation, which has been evaluated based on the Infineon Technologies BGT60TR13C RADAR. Furthermore, this work provides several Software-in-the-Loop (SITL) and Hardware-in-the-Loop (HITL) demonstrations, including interfacing with the Pixhawk 2 flight controller and an extensive evaluation of the communication of the engine with the NVIDIA Jetson Nano, which has been evaluated in various use cases, including dataset creation, object detection, Path Planning, and Simultaneous Localization and Mapping (SLAM).

High-precision 1D distance monitoring with radar data

ABSTRACT A measurement setup based on a high-precision, low-cost FMCW radar sensor has been developed for radar-based 1D deformation monitoring of engineering structures. The method enables contactless and time-continuous scans, but has lower acquisition costs compared to commercially available terrestrial laser and radar technologies. The article contains the instrument-related description of the sensor with accuracy tests for 1D distance measurements on a laboratory scale as well as a case study for monitoring the movements of a headframe over the course of the day. The detectability of deformations was statistically verified by means of a hypothesis test and applied to the case study under real conditions. With a defined target object (corner reflector), measuring accuracies of up to 10 μm with a 99,7% confidence level could be achieved. The case study shows absolute movements of the headframe during the day of 1 mm. Changes of 0,2 mm can be detected in time intervals of 9 min with a statistical significance level of 95%. Abbreviations : CA-CFAR: Cell-averaging constant false alarm rate; CR-I, CR-II: Corner reflectors; CUT: Cell Under Test; DFT: Discrete Fourier transformation; EXP: Experiment; FMCW: Frequency-modulated continuous-wave; iSDR-C:indurad ScanningDynamicRadar-Compact; LOD: Limit of detection; PI: Peak interpolation

Multimodal prediction of trait emotional intelligence-Through affective changes measured using non-contact based physiological measures.

Inability to efficiently deal with emotionally laden situations, often leads to poor interpersonal interactions. This adversely affects the individual’s psychological functioning. A higher trait emotional intelligence (EI) is not only associated with psychological wellbeing, educational attainment, and job-related success, but also with willingness to seek professional and non-professional help for personal-emotional problems, depression and suicidal ideation. Thus, it is important to identify low (EI) individuals who are more prone to mental health problems than their high EI counterparts, and give them the appropriate EI training, which will aid in preventing the onset of various mood related disorders. Since people may be unaware of their level of EI/emotional skills or may tend to fake responses in self-report questionnaires in high stake situations, a system that assesses EI using physiological measures can prove affective. We present a multimodal method for detecting the level of trait Emotional intelligence using non-contact based autonomic sensors. To our knowledge, this is the first work to predict emotional intelligence level from physiological/autonomic (cardiac and respiratory) response patterns to emotions. Trait EI of 50 users was measured using Schutte Self Report Emotional Intelligence Test (SSEIT) along with their cardiovascular and respiratory data, which was recorded using FMCW radar sensor both at baseline and while viewing affective movie clips. We first examine relationships between users’ Trait EI scores and autonomic response and reactivity to the clips. Our analysis suggests a significant relationship between EI and autonomic response and reactivity. We finally attempt binary EI level detection using linear SVM. We also attempt to classify each sub factor of EI, namely–perception of emotion, managing own emotions, managing other’s emotions, and utilization of emotions. The proposed method achieves an EI classification accuracy of 84%, while accuracies ranging from 58 to 76% is achieved for recognition of the sub factors. This is the first step towards identifying EI of an individual purely through physiological responses. Limitation and future directions are discussed.

A Cooperative Radar System With Active Reference Target Synchronization for Kinematic Target Analysis

This article presents a short-range cooperative radar system in the 77-GHz band for the estimation of kinematic parameters, such as position, velocity, and acceleration of a target. The proposed system uses two FMCW radar sensors that are operated incoherently and only depend on a common trigger for coarse synchronization. The resulting frequency offset and phase offset in the beat spectrum of the radar sensors are eliminated through calibration with a known active reference target. For easy detection of the reference target in the beat spectrum, a novel interchirp modulation method is proposed. The sampled data of a single receive channel of only one of the sensors of the system allow for a complete estimation of the kinematic target parameters. The theoretical analysis of incoherent FMCW radar systems is carried out, and the constraints and signal processing algorithms are derived from the results. The theory and operating principle of the proposed system are verified through measurements in an anechoic chamber. The accuracies for the position, velocity, and direction of movement (DoM) estimation are better than 10 mm, 0.03 m/s, and 1.4°, respectively.

X-RIO: Radar Inertial Odometry with Multiple Radar Sensors and Yaw Aiding

A robust and accurate real-time navigation system is crucial for autonomous robotics. In particular, GNSS denied and poor visual conditions are still very challenging as vision based approaches tend to fail in darkness, direct sunlight, fog or smoke. Therefore, we are taking advantage of inertial data and FMCW radar sensors as both are not affected by such conditions. In this work, we propose a framework, which uses several 4D mmWave radar sensors simultaneously. The extrinsic calibration of each radar sensor is estimated online. Based on a single radar scan, the 3D ego velocity and optionally yaw measurements based on Manhattan world assumptions are fused. An extensive evaluation with real world datasets is presented. We achieve even better accuracies than state of the art stereo Visual Inertial Odometry (VIO) while being able to cope with degraded visual conditions and requiring only very little computational resources.

Multi-Sensor Spatial Association Using Joint Range-Doppler Features

We investigate the problem of localizing multiple targets using a single set of measurements from a network of radar sensors. Such "single snapshot imaging" provides timely situational awareness, but can utilize neither platform motion, as in synthetic aperture radar, nor track targets across time, as in Kalman filtering and its variants. Associating measurements with targets becomes a fundamental bottleneck in this setting. In this paper, we present a computationally efficient method to extract 2D position and velocity of multiple targets using a linear array of FMCW radar sensors by identifying and exploiting inherent geometric features to drastically reduce the complexity of spatial association. The proposed framework is robust to detection anomalies, and achieves order of magnitude lower complexity compared to conventional methods. While our approach is compatible with conventional FFT-based range-Doppler processing, we show that more sophisticated techniques for range-Doppler estimation lead to reduced data association complexity as well as higher accuracy estimates of target positions and velocities.

The Role of Millimeter-Waves in the Distance Measurement Accuracy of an FMCW Radar Sensor.

High-accuracy, short-range distance measurement is required in a variety of industrial applications e.g., positioning of robots in a fully automated production process, level measurement of liquids in small containers. An FMCW radar sensor is suitable for this purpose, since many of these applications involve harsh environments. Due to the progress in the field of semiconductor technology, FMCW radar sensors operating in different millimeter-wave frequency bands are available today. An important question in this context, which has not been investigated so far is how does a millimeter-wave frequency band influence the sensor accuracy, when thousands of distance measurements are performed with a sensor. This topic has been dealt with for the first time in this paper. The method used for analyzing the FMCW radar signal combines a frequency- and phase-estimation algorithm. The frequency-estimation algorithm based on the fast Fourier transform and the chirp-z transform provides a coarse estimate of the target distance. Subsequently, the phase-estimation algorithm based on a cross-correlation function provides a fine estimate of the target distance. The novel aspects of this paper are as follows. First, the estimation theory concept of Cramér-Rao lower bound (CRLB) has been used to compare the accuracy of two millimeter-wave FMCW radars operating at 60 GHz and 122 GHz. In this comparison, the measurement parameters (e.g., bandwidth, signal-to-noise ratio) as well as the signal-processing algorithm used for both the radars are the same, thus ensuring an unbiased comparison of the FMCW radars, solely based on the choice of millimeter-wave frequency band. Second, the improvement in distance measurement accuracy obtained after each step of the combined frequency- and phase-estimation algorithm has been experimentally demonstrated for both the radars. A total of 5100 short-range distance measurements are made using the 60 GHz and 122 GHz FMCW radar. The measurement results are analyzed at various stages of the frequency- and phase-estimation algorithm and the measurement error is calculated using a nanometer-precision linear motor. At every stage, the mean error values measured with the 60 GHz and 122 GHz FMCW radars are compared. The final accuracy achieved using both radars is of the order of a few micrometers. The measured standard deviation values of the 60 GHz and 122 GHz FMCW radar have been compared against the CRLB. As predicted by the CRLB, this paper experimentally validates for the first time that the 122 GHz FMCW radar provides a higher repeatability of micrometer-accuracy distance measurements than the 60 GHz FMCW radar.

A Low-Phase-Noise 77-GHz FMCW Radar Transmitter With a 12.8-GHz PLL and a <inline-formula> <tex-math notation="LaTeX">$\times$</tex-math> </inline-formula>6 Frequency Multiplier

In this letter, a 77-GHz transmitter (TX) with a 12.8-GHz phase-locked-loop (PLL) and a $\times$ 6 frequency multiplier is presented for a FMCW radar sensor in a 65-nm CMOS process. To realize the low-phase-noise TX, a voltage controlled oscillator (VCO) with an excellent phase noise performance at a lower fundamental frequency (12.8 GHz) is designed and scaled up ( $\times$ 6) for the desired target frequency (77 GHz). The measured FMCW modulation range with an external triangular chirp signal (1-ms sweep time) is 601 MHz. The output power and the total DC power consumption of the TX are 8.9 dBm and 116.7 mW, respectively. Here, a good phase noise level of −91.16 dBc/Hz at a 1-MHz offset frequency from a 76.81-GHz carrier is achieved.

Radar-Based High-Accuracy Angle Measurement Sensor Operating in the K-Band

The growing need for high-precision measurement instruments in modern industrial facilities leads to new measurement principles to reduce sensor costs while maintaining accuracy. A novel measurement principle suited for these applications is based on radar, which originally is associated with measurements in free space. For high precision, guided waves can be used to prevent disturbing reflections from the environment. Another advantage of a waveguide-based measurement is that the electromagnetic wave can be guided in a circle to measure rotational motions, which are in particular relevant in machine tools. This paper describes an FMCW radar sensor in the K-band for angle measurements. The sensor principle is described in detail, including its high-frequency components and the mechanical constraints to achieve a full angle range of 360°. The propagation characteristics of the radar signal in a curved waveguide with an adapted feeding structure using a Riblet coupler are presented. In addition to simulations, measurements with a prototype setup are presented to verify the radar-based angle measurement principle. The achieved accuracy is ±0.05° which corresponds to an accuracy in distance of ±125 μm.

INVESTIGATION ON CIRCULAR MAPPING BY FMCW-SAR ON SMALL AIRPLANES

Abstract. The evaluation of local damages after natural disasters by using remote sensing demands for flexible platforms as well as sensor systems, which guarantee both weather- and daylight-independence. Due to the fact of small energy consumption, small size and light weight millimeter-wave FMCW radar sensors on small airplanes are very promising for this task. Especially in urban environments the side looking SAR geometry causes shadowing and foreshortening effects, which lead to gaps in the reconstructed scene and misinterpretation. Multiple views from different aspect angles can reduce the shadowing effects but especially in unknown areas the best line of sight cannot be investigated in advance. This is achievable by perfoming circular flight trajectories. However, as millimeter-wave FMCW radar sensors typically have very narrow beams and small airplanes are sensitive to air turbulences, a beam stabilization method is necessary to provide full illumination of the target scene. In this paper the calculations for system specific optimal flight parameters are demonstrated and validated by simulations. The impact of air turbulences, causing angular deviations to the sensor and thus misplacement of the main beam lobe on the ground are explored by experimental data performed with our SUMATRA system. The effects of a potential mechanical beam stabilization are visualized and requirements for such a system are formulated. Our experiments show that for typical flight conditions a stabilized platform is well suitable to stabilize a narrow radar beam in order to keep a target scene constantly illuminated over a full circular trajectory. Typically these stabilized platforms can handle angular corrections in all three geometries (pitch, roll, squint) of up to 12 °−15 ° by a speed of 15 ° per second. Therefore a more cost intensive full gimbal system which is known to be used in optical applications and which can handle a full 360 ° tracking is not neccessarily needed.

FM-CW radar sensors for vital signs and motor activity monitoring

The article summarizes on-going research on vital signs and motor activity monitoring based on radar sensors embedded in wheelchairs, walkers and crutches for in home rehabilitation. Embedded sensors, conditioning circuits, real-time platforms that perform data acquisition, auto-identification, pr

High LO-to-RF isolation 94 GHz coplanar monolithic down-converter for FMCW radar sensor applications

This paper presents a 94 GHz monolithic down-converter with low conversion loss and high local oscillator (LO)-to-RF isolation using the 0.1 μm T-gate metamorphic high electron-mobility transistor (MHEMT) technology. The down-converter consists of a one-stage amplifier and a single-balanced mixer based on the high-directivity tandem coupler structure using the air-bridge crossovers, thereby amplifying the RF signals and maximizing the LO-to-RF isolation by using an inherent S 12 isolation characteristic of the amplifier and good phase balance of the tandem coupler. The fabricated one-stage amplifier using a 30 μm × 2 MHEMT shows a small signal gain of 7 dB at 94 GHz. The single-balanced mixer comprising two 20 μm × 2 MHEMT Schottky diodes and the tandem coupler with an additional λ/4-length line exhibits the conversion loss less than 7.8 dB and the LO-to-RF isolation higher than 30 dB in a RF frequency range of 91–96 GHz. Two circuits designed both for a 50 Ω impedance system are integrated into the down-converter of a 2.6 × 2.5 mm 2 chip size, and it shows a low conversion loss of ∼1 dB at 94 GHz and excellent LO-to-RF isolation above 40 dB in a frequency range of 90–100 GHz. This is the best isolation among the W-band monolithic down-converters reported to date.

A FM-CW radar proximity sensor for use in mechatronic products

The need for a low cost medium range proximity sensor for use in mechatronic products is identified and discussed with particular reference to automotive applications. One candidate solution to this need, a 27 GHz frequency modulated continuous wave radar system, is presented for use as a range finder. The method of releasing the range information from the returned signal is described together with the results from off-line data analysis. A real-time system for implementation in a typical product is outlined.

The use of low cost FM-CW radar sensors in navigation

Navigation of Autonomous Guided Vehicles (AGVs) has traditionally been accomplished by means of fixed path navigation systems. The trend towards the use of such vehicles in unstructured external environments has led to a requirement for increasingly sophisticated sensory inputs. This paper describes the use of FM-CW radar for autonomous vehicle navigation and the development of a real time test platform for their evaluation. Experimental results have been processed off-line using Matlab to extract range information. Real time performance was achieved using a dual processor system comprising a Motorola 68000 single board computer for system control and supervision and a Texas Instruments Digital Signal Processor, TMS320C50, for performing 1024 point Fast Fourier Transformation to convert a digitised radar return signal into a frequency domain signal.

Simulation of an adaptive MTI in a monopulse FMCW radar sensor

A range-doppler spread target model is modified to obtain a clut ter model by including the effect of beam and range gate shape. This clutter modeling is then applied in a simulation of an FMCW radar sensor that uses an adaptive moving target indicator (AMTI) and a monopulse antenna to detect slow-moving ground targets. The ability of the AMTI to discriminate a slow-moving point target from ground clutter is shown via simulation.