Single Radar Sensor Research Articles

Fault Diagnosis of the Autonomous Driving Perception System Based on Information Fusion.

The reliability of autonomous driving sensing systems impacts the overall safety of the driving system. However, perception system fault diagnosis is currently a weak area of research, with limited attention and solutions. In this paper, we present an information-fusion-based fault-diagnosis method for autonomous driving perception systems. To begin, we built an autonomous driving simulation scenario using PreScan software, which collects information from a single millimeter wave (MMW) radar and a single camera sensor. The photos are then identified and labeled via the convolutional neural network (CNN). Then, we fused the sensory inputs from a single MMW radar sensor and a single camera sensor in space and time and mapped the MMW radar points onto the camera image to obtain the region of interest (ROI). Lastly, we developed a method to use information from a single MMW radar to aid in diagnosing defects in a single camera sensor. As the simulation results show, for missing row/column pixel failure, the deviation typically falls between 34.11% and 99.84%, with a response time of 0.02 s to 1.6 s; for pixel shift faults, the deviation range is between 0.32% and 9.92%, with a response time of 0 s to 0.16 s; for target color loss, faults have a deviation range of 0.26% to 2.88% and a response time of 0 s to 0.05 s. These results prove the technology is effective in detecting sensor faults and issuing real-time fault alerts, providing a basis for designing and developing simpler and more user-friendly autonomous driving systems. Furthermore, this method illustrates the principles and methods of information fusion between camera and MMW radar sensors, establishing the foundation for creating more complicated autonomous driving systems.

Indoor Positioning System Using a Single-Chip Millimeter Wave Radar

Estimation of indoor location is a key capability for indoor location-based services. Among various solutions, single-chip low-cost millimeter-wave (mmWave) radar as an emerging technology are getting more and more attention due to their inherent advantages such as penetration ability, low-power consumption. Currently, the vast majority of work is focused on hybrid positioning based on mmWave radar with other sensors, and there is a lack of research on single radar indoor positioning. In this article, we propose a novel indoor positioning system based on single-radar sensor, which determines the trajectory by estimating the velocity and heading of moving radar from sparse point clouds. Doppler velocity of detected object can be able to feedback the radar velocity, and for another hand, the proposed Cluster Center Association (CCA) method combined with normal distributions transform (NDT) can be used for heading estimation. In addition, we proposed the motion consistency constraints to enhance the robustness of the system. The experimental results show that the traditional NDT algorithm will fail due to the sparse point cloud and severe indoor multipath, and the proposed algorithm can provide the positioning accuracy within 1 m and the heading estimation error of 3°.

Velocity Estimation for Vehicle-Mounted SAR Based on Deep-Learning Framework

With the development of intelligent driving technology, vehicle-mounted synthetic aperture radar (SAR) imaging, which is regarded as one of the most fundamental assists, has become an important and focal topic. As for automotive SAR imaging, the primary task is to estimate the velocity of the moving platform. Due to the volatile and complex road conditions, it does expense tremendous efforts for accurate velocities acquired with additional motion sensors. In this article, based on exploiting the geometrical relations between the moving platform and near-ground echoes, a velocity estimation scheme is deduced with a single radar sensor only. Then, with the proposed geometric model, an accurate velocity can be obtained and quantitatively analyzed. In addition, to further improve the efficiency of velocity estimation, we establish a real-world SAR echo dataset and transform the conventional equipment-dependable problem into a data-driven method based on a deep neural network. The efficiency and effectiveness of the proposed methods are verified by SAR data collected from real-world scenes.

Near-Field Compensation for Coherent Radar Networks

With radar networks, the resolution of critical radar parameters such as Doppler and angle can be improved compared to a single radar sensor. As the network’s aperture is considerably larger than that of a single radar, a much higher angular resolution is achieved. However, with a large aperture, range-dependent phase deviations, that is, near-field effects, occur and affect the angle estimation. In this work, these near-field effects are evaluated exemplarily for a coherent network. Furthermore, a new strategy to compensate for those network near-field effects is proposed and demonstrated based on measurements. The benefits of the near-field compensation are emphasized by comparing the network’s angle-estimation capabilities with and without compensated near-field effects.

Machine Learning‐Enabled Noncontact Sleep Structure Prediction

Automated, effective and efficient sleep‐stage monitoring and structure analysis is an essential enabling procedure for healthcare automation. Sleep diagnosis by polysomnography is a golden standard but expensive procedure involving huge effort from patients. There remain challenges for smart devices to precisely identify sleep stage and minimize intrusive effect on sleep progression. Herein, a novel noncontact sleep structure prediction system (NSSPS) using a single radar sensor is presented to analyze sleep structure without any tethered unit. The NSSPS is realized through training a convolutional recurrent neural network and neural conditional random fields using reflected radio frequency (RF) waves acquired by radar antennas. By capturing implicit temporal information in RF signals and transitions of sleep progression, high accuracy of sleep‐stage prediction is achieved and characteristics of sleep structure are extracted. The performance of the NSSPS is validated by transfer learning between radar signals with different frequency bands and crossvalidation among different subjects. Moreover, the NSSPS is demonstrated to estimate overnight parameters that are critical for sleep diagnosis. Benefiting from its low cost, convenient setup, and accurate prediction capability of sleep‐stage identification, the NSSPS can be widely deployed in “smart” homes and exploited to conduct daily sleep structure analysis.

A Traffic Information Awareness Approach Based on Video Data and Millimeter Wave Radar Data Fusion

In order to solve the problems of using a single radar or video sensor in the traffic information detection process, such as susceptibility to environmental influences and non-intuitive target reflections, we propose a detection method using target correlation matching, target tracking, and target data fusion, and we show how to adjust the detection weights of the video sensor and radar sensor by adjusting the noise matrix parameters, which forms a flexible, simple, and nimble method. The efficient architecture allows accurate results in challenging environments. We show the effect on radar detection data and video detection data before and after adjustment. We test the proposed method by building a hardware verification platform and finally demonstrate it on video images. The experimental results show that the proposed method can significantly reduce the testing time while providing high detection rate in more environments.

Vital Sign Detection during Large-Scale and Fast Body Movements Based on an Adaptive Noise Cancellation Algorithm Using a Single Doppler Radar Sensor.

The non-contact detection of human vital signs (i.e., respiration rate (RR) and heartbeat rate (HR)) using a continuous-wave (CW) Doppler radar sensor has great potential for intensive care monitoring, home healthcare, etc. However, large-scale and fast random body movement (RBM) has been a bottleneck for vital sign detection using a single CW Doppler radar. To break this dilemma, this study proposed a scheme combining adaptive noise cancellation (ANC) with polynomial fitting, which could retrieve the weak components of both respiration and heartbeat signals that were submerged under serious RBM interference. In addition, the new-type discrete cosine transform (N-DCT) was introduced to improve the detection accuracy. This scheme was first verified using a numerical simulation. Then, experiments utilizing a 10-GHz Doppler radar sensor that was built from general-purpose radio frequency (RF) and communication instruments were also carried out. No extra RF/microwave components and modules were needed, and neither was a printed circuit board nor an integrated-chip design required. The experimental results showed that both the RR and HR could still be extracted during large-scale and fast body movements using only a single Doppler radar sensor because the RBM noises could be greatly eliminated by utilizing the proposed ANC algorithm.

RADARODO: Ego-Motion Estimation From Doppler and Spatial Data in RADAR Images

Accurate and robust ego-motion estimation is critical for aiding autonomous vehicle localization in environments devoid of map landmarks or accurate GNSS measurements. RADAR measurements provide unique capabilities for ego-motion estimation while also posing unique challenges. This paper presents the novel real-time RADAR-based ego-motion estimation algorithm, RADARODO (RADAR Odometry). RADARODO is differentiated from similar techniques by two novel attributes. First, it decouples sensor translational and rotational motion by estimating them from Doppler and spatial data, respectively. Secondly, it directly analyzes RADAR images to estimate rotational motion via correspondence-free non-linear optimization. Unlike other methods, RADARODO estimates ego-motion along with its predicted uncertainty from a single RADAR sensor without depending on a lever-arm offset or the zero side-slip assumption. The real-world results demonstrate accurate ego-motion estimates that are particularly suited for integration within a “tightly-coupled” sensor fusion framework.

Doppler Cardiogram: A Remote Detection of Human Heart Activities

Most medical instruments invented to measure human heart activities, such as electrocardiograms (ECGs), rely on contact electrodes. This causes discomfort and limits application scenarios. Remote acquisition of $\mu \text{V}$ -level bioelectrical cardiac signals through ECG measurement is theoretically challenging. Based on the analysis of magnetic resonance imaging to the volume change of human hearts, we found that a single radar sensor can be used to remotely detect a Doppler cardiogram (DCG) at a distance up to 1 m, by retrieving Doppler signals induced by combined atrial and ventricular motions conducted to the skin of the back chest. This DCG can provide all the timing information of the P-wave, QRS-waves, and T-wave carried in ECGs. The implemented miniature remote sensor could be an ideal portable instrument for patients, such as burn victims, extending application scenarios to personal healthcare, battlefield rescue, and others.

Estimating above ground biomass as an indicator of carbon storage in vegetated wetlands of the grassland biome of South Africa

Wetlands store higher carbon content relative to other terrestrial ecosystems, despite the small extent they occupy. The increase in temperature and changes in rainfall pattern may negatively affect their extent and condition, and thus the process of carbon accumulation in wetlands. The introduction of the Sentinel series (S1 and S2) and WorldView space-borne sensors (WV3) have enabled monitoring of herbaceous above ground biomass (AGB) in small and narrow wetlands in semi-arid areas. The objective of this study was to assess (i) the capabilities of the high to moderate resolution sensors such as WV3, S1A and S2A in estimating herbaceous AGB of vegetated wetlands using SAR backscatter, optical reflectance bands, vegetation spectral indices (including Leaf Area Index or LAI measurements) and band ratio datasets and (ii) whether significant differences exists between the AGB ranges of wetland and surrounding dryland vegetation. A bootstrapped Random Forest modelling approach, with variable importance selection, was utilised which incorporated ground collected grass AGB for model calibration and validation. WorldView-3 (WV3) yielded the highest AGB prediction accuracies (R2 = 0.63 and RMSE = 169.28 g/m2) regardless of the incorporation of bands only, indices only or the combination of bands and indices. In general, the optical sensors yielded higher modelling accuracies (improvement in R2 of 0.04-0.07 and RMSE of 11.48–17.28 g/m2) than the single Synthetic Aperture Radar (SAR) sensor but this was marginal depending on the scenario. Incorporating Sentinel 1A (S1) dual polarisation channels and Sentinel 2A (S2) reflectance bands, in particular, yielded higher accuracies (improvement in R2 of 0.03–0.04 and RMSE of 5.4–16.88 g/m2) than the use of individual sensors alone and was also equivalent to the performance of the high resolution WV3 sensor results. Wetlands had significantly higher AGB compared to the surrounding terrestrial grassland (with a mean of about 80 g/m2 more). Monitoring herbaceous AGB at the scale of the wetland extent in semi-arid to arid grasslands enables improved understanding of their carbon sequestration potential, the contributions to global carbon accounting policies and also serving as a proxy for functional intactness.

Time-series maps of aboveground biomass in dipterocarps forests of Malaysia from PALSAR and PALSAR-2 polarimetric data

BackgroundMalaysia typically suffers from frequent cloud cover, hindering spatially consistent reporting of deforestation and forest degradation, which limits the accurate reporting of carbon loss and CO2 emissions for reducing emission from deforestation and forest degradation (REDD+) intervention. This study proposed an approach for accurate and consistent measurements of biomass carbon and CO2 emissions using a single L-band synthetic aperture radar (SAR) sensor system. A time-series analysis of aboveground biomass (AGB) using the PALSAR and PALSAR-2 systems addressed a number of critical questions that have not been previously answered. A series of PALSAR and PALSAR-2 mosaics over the years 2007, 2008, 2009, 2010, 2015 and 2016 were used to (i) map the forest cover, (ii) quantify the rate of forest loss, (iii) establish prediction equations for AGB, (iv) quantify the changes of carbon stocks and (v) estimate CO2 emissions (and removal) in the dipterocarps forests of Peninsular Malaysia.ResultsThis study found that the annual rate of deforestation within inland forests in Peninsular Malaysia was 0.38% year−1 and subsequently caused a carbon loss of approximately 9 million Mg C year−1, which is equal to emissions of 33 million Mg CO2 year−1, within the ten-year observation period. Spatially explicit maps of AGB over the dipterocarps forests in the entire Peninsular Malaysia were produced. The RMSE associated with the AGB estimation was approximately 117 Mg ha−1, which is equal to an error of 29.3% and thus an accuracy of approximately 70.7%.ConclusionThe PALSAR and PALSAR-2 systems offer a great opportunity for providing consistent data acquisition, cloud-free images and wall-to-wall coverage for monitoring since at least the past decade. We recommend the proposed method and findings of this study be considered for MRV in REDD+ implementation in Malaysia.

FMCW 송수신 칩을 이용한 단일 안테나 레이다 센서

This paper presents a single antenna radar sensor with Ku-band radar transceiver IC realized in 0.13 μm CMOS processes. In the radar receiver, a sensitivity time control using a DC offset cancellation feedback loop is employed for constant SNR irrelevant on the distance. To reduce the direct coupled Tx signal to Rx in single antenna radar, stub tuned hybrid coupler is adopted instead of circulator. The measured distance is 8.2 m with horn antenna and metal plate target.

Analysis of the Potential Space Resolution in Multi-position Surface Movement Radar System

Specialized surface movement radar (SMR) systems for visual observations of the airfield are one of the mandatory systems ensuring the safety of flights and control service of aerodromes. Design of multi-position SMR systems comprising a set of simple and low-cost radar sensors (RS) is a promising trend. A problem to estimate a potential resolution in a multi-position SMR system is relevant.The paper describes two approaches to estimate the potential resolution using the "joint" ambiguity function, namely additive and multiplicative. Both approaches allow taking into account a type of the radar signal used, an antenna radiation pattern (ARP), and the location of radar sensors on the airfield.To implement the considered approaches to the resolution estimation, was used a software package to analyse the multi-position SMR system characteristics. For a particular example to locate radar sensors, the calculation results of the ambiguity function are given.The paper presents an estimated space resolution in the multi-position SMR system in Cartesian coordinate system. Draws conclusions about the possibility for obtaining the multi-position SMR system resolution that exceeds the resolution of a single radar sensor with the exception of the isotropic radiation pattern of radar sensor and the additive estimation method.The developed software package can be used to analyse and justify the following:- space resolution requirements to the radar sensor;- recommendations on the location of the radar sensors;- resolution of the multi-position SMR system space including a radar sensor with known characteristics.

Experimental Low-Terahertz Radar Image Analysis for Automotive Terrain Sensing

In this letter, we report initial experimental results which provide the foundation for low-terahertz (low-THz) radar imagery for outdoor unstructured scenarios as expected in automotive sensing. The requirements and specifications for a low-THz single imaging radar sensor are briefly outlined. The imaging capabilities of frequency-modulated continuous-wave (FMCW) radar operating at 150 GHz are discussed. A comparison of experimental images of on-road and off- road scenarios made by a 150-GHz FMCW radar and a reference 30-GHz stepped-frequency radar is implemented, and their performance is analyzed.

Multipath exploited Bayesian and Cramér-Rao bounds for single-sensor target localization

In urban scenarios, radar returns consist of a direct path return along with multipath returns from signal reflections off surfaces such as building walls or floors. When multipath is resolvable, and given the knowledge of the geometry of the reflecting surfaces, it has recently been demonstrated that multipath returns create additional “virtual” radar sensors, thereby permitting target localization with a single radar sensor. The goal of this article is to determine theoretical variance lower bounds on how well a single-sensor system is able to localize a target in the presence of exploitable multipath and discuss several practical issues that arise in this context, including the multipath association problem, clutter, and the impact of wall roughness. Exploiting multipath, rather than viewing it strictly as a hindrance, is an emerging topic in the radar community whose potential is not yet fully understood. Towards this goal, we first derive the Cramér-Rao and the Bayesian Cramér-Rao bounds on target localization using a single-sensor which exploits resolvable multipath. For a wide class of radar-target geometries, functions termed multipath preservers are derived which indicate when multipath is physically observable in the radar returns; these functions assist in evaluating the potential of multipath exploitation in urban sensing. Given a reflecting geometry, the obtained lower bounds allow the radar operator to anticipate blind spots, place confidence levels on the localization results, and permit sensor positioning to optimally aid in exploiting multipath for target localization. It is shown that variance bounds on the location parameters improve with richer resolvable multipath generating mechanisms.

Design and Analysis of Distributed Radar Sensor Networks

In this paper, we design a network of distributed radar sensors that work in an ad hoc fashion, but are grouped together by an intelligent clusterhead. This system is named Radar Sensor Network (RSN). A RSN not only provides spatial resilience for target detection and tracking compared to traditional radars, but also alleviates inherent radar defects such as the blind speed problem. This interdisciplinary area offers a new paradigm for parallel and distributed sensor research. We propose both coherent and noncoherent RSN detection systems applying selection combination algorithm (SCA) performed by clusterhead to take the advantage of spatial diversity. Monte Carlo simulations show that proposed RSN can provide much better detection performance than that of single radar sensor for fluctuating targets, in terms of probability of false alarm and miss detection. We also analyze the impact of Doppler shift on both coherent and noncoherent RSN detection systems at the presence of clutter. The result is that the coherent system is more robust to the noncoherent RSN.

Moving Targets Processing in SAR Spatial Domain

This paper presents a novel technique to estimate the initial coordinates and velocity vector of moving targets, including those with velocities above the Nyquist limit, using a single synthetic aperture radar (SAR) sensor without increasing the pulse repetition frequency (PRF). The basic reasoning is that, although the returned echoes may be undersampled in the azimuth direction, their phase and amplitude are informative with respect to the moving target trajectory parameters. Therefore, the so-called blind angle ambiguity, inherent to systems using a single SAR sensor, is overcome. The proposed method samples the data in the spatial domain, along the signature curve which depends on the moving target trajectory parameters. The resulting algorithm is a highly efficient (from the computational point of view) ID matched filter. The effectiveness of the proposed scheme is illustrated using simulated SAR data and real data from the MSTAR public release data set, corresponding to a static SAR scene and a static BTR-60 with simulated motion.

Multiple moving target detection and trajectory estimation using a single SAR sensor

A novel methodology is presented for determining the velocity and location of multiple moving targets using a single strip-map synthetic aperture radar (SAR) sensor. The so-called azimuth position uncertainty problem is therefore solved. The method exploits the structure of the amplitude and phase modulations of the returned echo from a moving target in the Fourier domain. A crucial step in the whole processing scheme is a matched filtering, depending on the moving target parameters, that simultaneously accounts for range migration and compresses two-dimensional signatures into one-dimensional ones without losing moving target information. A generalized likelihood ratio test approach is adopted to detect moving targets and derive their trajectory parameters. The effectiveness of the method is illustrated with synthetic and real data covering a wide range of targets velocities and signal-to-clutter ratios (SCRs). Even in the case of parallel to platform moving target motion, the most unfavorable scenario, the proposed method yields good results for, roughly, SCR > 10 dB.