Haul truck alignment monitoring and operator warning system

This paper presents a haul truck alignment monitoring system that provides early warning signals to the operator when the truck is about to lose control. It is considered that a truck is in this condition when it crosses the center of the road or veers to the side of the road at speed in an uncontrolled manner. The system provides different levels of warnings to the driver and other haul trucks in visual contact. The system is based on a laser range and bearing sensor that measure the relative distance to standard polyvinyl chloride poles located at the side of the road. Using this approach, a high level of accuracy and reliability can be achieved with low cost since the installation and maintenance of the infrastructure does not require special expertise or expensive machinery. The system also logs raw sensor data and warning events generated by the truck. This information is downloaded using wireless interfaces to a base station for postprocessing purposes. This is essential to monitor the operation of the system and determine potential degradation of its components. Experimental results are shown demonstrating the robustness of the system. These results were obtained from actual data extracted from a database built with more that 12 months of continued operation of the system in two different mines.     

Automated Bayesian quality control of streaming rain gauge data

Radar-rainfall data are being used in an increasing number of real-time applications because of their wide spatial and temporal coverage. Because of uncertainties in radar measurements and the relationship between radar measurements and rainfall on the ground, radar-rainfall data are often combined with rain gauge data to improve their accuracy. However, while rain gauges can provide accurate estimates of rainfall, their data are sometimes corrupted with errors caused by the environment in which the gauges are deployed. This study develops a real-time method for identifying measurement errors in rain gauge data streams. This method employs a dynamic Bayesian network (DBN) model of the rain gauge data stream to sequentially forecast the next rain gauge measurement from both the rain gauge and weather radar data streams and a decision rule-based classifier to identify data errors. Because of the uncertainty in the relationship between the radar and rainfall measurements, this method uses a statistical learning method (expectation maximization) to determine the best parameters for this relationship, given an adaptively sized moving window of previous measurements. The performance of the error detector developed in this study is demonstrated using a precipitation sensor network composed of five telemetered tipping bucket rain gauges and a WSR-88D weather radar. Through an analysis using synthetic errors, the false alarm rate and false negative rate were calculated to be 0.90% and 1.5%, respectively.     

AF-CenterNet:基于交叉注意力机制的毫米波雷达和相机融合的目标检测

对于自动驾驶领域而言,确保在各种天气和光照条件下精确检测其他车辆目标是至关重要的。针对单个传感器获取信息的局限性,提出一种基于cross-attention注意力机制的融合方法(AF),用于在特征层面上融合毫米波雷达和相机信息。首先,将毫米波雷达和相机进行空间对齐,并将对齐后的点云信息投影成点云图像。然后,将点云图像在高度和宽度方向上进行扩展,以提高相机图像和点云图像之间的匹配度。最后,将点云图像和相机图像送入包含AF结构的CenterNet目标检测网络中进行训练,并生成一个空间注意力权重,以增强相机中的关键特征。实验结果表明,AF结构可以提高原网络检测各种大小目标的性能,特别是对小目标的检测提升更为明显,且对系统的实时性影响不大,是提高车辆在多种场景下检测精度的理想选择。     

Self-learning classification of radar features for scene understanding

Autonomous driving is a challenging problem in mobile robotics, particularly when the domain is unstructured, as in an outdoor setting. In addition, field scenarios are often characterized by low visibility as well, due to changes in lighting conditions, weather phenomena including fog, rain, snow and hail, or the presence of dust clouds and smoke. Thus, advanced perception systems are primarily required for an off-road robot to sense and understand its environment recognizing artificial and natural structures, topology, vegetation and paths, while ensuring, at the same time, robustness under compromised visibility. In this paper the use of millimeter-wave radar is proposed as a possible solution for all-weather off-road perception. A self-learning approach is developed to train a classifier for radar image interpretation and autonomous navigation. The proposed classifier features two main stages: an adaptive training stage and a classification stage. During the training stage, the system automatically learns to associate the appearance of radar data with class labels. Then, it makes predictions based on past observations. The training set is continuously updated online using the latest radar readings, thus making it feasible to use the system for long range and long duration navigation, over changing environments. Experimental results, obtained with an unmanned ground vehicle operating in a rural environment, are presented to validate this approach. A quantitative comparison with laser data is also included showing good range accuracy and mapping ability as well. Finally, conclusions are drawn on the utility of millimeter-wave radar as a robotic sensor for persistent and accurate perception in natural scenarios.     

When the Dust Settles: The Four Behaviors of LiDAR in the Presence of Fine Airborne Particulates

This paper describes the behavior of a commercial light detection and ranging (LiDAR) sensor in the presence of dust. This work is motivated by the need to develop perception systems that must operate where dust is present. This paper shows that the behavior of measurements from the sensor is systematic and predictable. LiDAR sensors exhibit four behaviors that are articulated and understood from the perspective of the shape‐of‐return signals from emitted light pulses. We subject the commercial sensor to a series of tests that measure the return pulses and show that they are consistent with theoretical predictions of behavior. Several important conclusions emerge: (i) where LiDAR measures dust, it does so to the leading edge of a dust cloud rather than as a random noise; (ii) dust starts to affect measurements when the atmospheric transmittance is less than 71%–74%, but this is quite variable with conditions; (iii) LiDAR is capable of ranging to a target in dust clouds with transmittance as low as 2% if the target is retroreflective and 6% if it is of low reflectivity; (iv) the effects of airborne particulates such as dust are less evident in the far field. The significance of this paper lies in providing insight into how better to use measurements from off‐the‐shelf LiDAR sensors in solving perception problems.     

Passive target detection and tracking from electromagnetic field measurements

This paper presents a novel approach to the localization of moving targets in a complex environment based on the measurement of the perturbations induced by the target presence on an independently‐generated time‐varying electromagnetic field. Field perturbations are measured via a set of sensors deployed over the domain of interest and used to detect and track a possible target by resorting to a particle Bernoulli filter (PBF). To comply with real‐time operation, the PBF works along with an artificial neural network (ANN) model of the environment trained offline via finite elements (FEs). The performance of the proposed algorithm is assessed via simulation experiments.     

A new wind retrieval algorithm for Jason-1 at high wind speeds

The altimeter wind speed algorithm at high wind speeds remains unsolved because of lack of observed data. In this study data at high wind speeds were generated using Yin's typhoon model, which consists of the Rankine vortex model and the angular momentum model with typhoon parameters, provided by the Joint Typhoon Warning Centre (JTWC). The accuracy of Yin's typhoon model can be validated by comparing it with recorded data from a weather station. By comparing the normalized radar backscatter cross‐section (NRCS) detected by the Jason‐1 altimeter with wind speed data inferred by Yin's typhoon model, an empirical algorithm valid for a range of wind speeds between 10 and 40 m s^–1 is developed and proposed. The proposed algorithm is compared with the Jason‐1 operational algorithm and Young's altimeter wind retrieval algorithm. The study shows that, for the proposed algorithm and the operational algorithm for Jason‐1, the root mean square (RMS) errors are 3.38 and 3.60 m s^–1, respectively, and the average relative errors are 18% and 19%, respectively, for wind speeds less than 27 m s^–1. Hence, the proposed algorithm is in agreement with the operational algorithm for the Jason‐1 altimeter for wind speeds in the range 10–27 m s^–1. However, the Jason‐1 operational algorithm is inaccurate for wind speeds above 27 m s^–1 because the wind speeds used in the algorithm training process came from scatterometer wind products, and are significantly lower than those in strong wind and heavy rain conditions. Comparison of the proposed algorithm and Young's algorithm shows that the RMS errors are 6.27 and 15.18 m s^–1, respectively, and the average relative errors are 16% and 59%, respectively, for wind speeds greater than 20 m s^–1. The Holland typhoon model cannot accurately determine the outer wind field of typhoons since it extends cyclonic wind speeds to infinity. Young's altimeter wind retrieval algorithm depends on the Holland typhoon model, and the latter results in some errors. Compared with Young's altimeter wind retrieval algorithm, the proposed algorithm retrieves wind speeds with better accuracy. Therefore, the proposed algorithm, suitable for retrieving sea surface wind speeds in typhoons and other strong wind conditions, can be considered as supplementary to the Jason‐1 operational algorithm.     

A combined passive–active microwave retrieval of quantitative rainfall from Topex/Poseidon radar altimeter and Topex microwave radiometer

Synergistic measurements of both active and passive nadir looking microwave sensors onboard Topex/Poseidon (T/P) satellite are explored for their instantaneous rain estimation capability over tropical oceans. Data of T/P altimeter (the differential radar backscatter at C and Ku band frequencies, i.e. δσ°?=?σ°_C?σ°_Ku), and Topex microwave radiometer (TMR) (brightness temperatures at 18, 21 and 37?GHz frequencies) coincident with special sensor microwave/imager (SSM/I) and tropical rainfall measuring mission (TRMM)–microwave radiometer (TMI) have been analysed for this purpose. The rainfall response has been separated from the surface variability and atmospheric water content (in terms of water vapour and liquid water), by analysing the TMR data in view of the radiative response of its channels under different ocean–atmospheric conditions over the Indian oceanic region. Among a large collocated data on different spatial and temporal scales, the most restrictive criteria (<?10?km spatial, <?30?min temporal difference) produce the better statistics (in terms of correlation and rms errors) from the rainfall rate from SSM/I and TMI with the combined radar and radiometric observations from T/P and TMR respectively. The analysis thus shows the added advantage of optimally integrated active and passive microwave measurements for instantaneous rain estimation as compared to our earlier study (Varma et al. ) by passive TMR measurements alone, for both better estimation of rain and corrections to T/P radar backscatter due to its path attenuation. The equation is further used to estimate monthly averaged global rain rate maps for its qualitative and quantitative assessment. Typical rain rate maps from blended radar and radiometer for two contrasting seasons for the months of January and July for the year 2000 (during the north‐east and south‐west monsoon respectively), are compared with similar maps of TRMM–precipitation radar of monthly rainfall accumulations, showing most of the regional and global tropical features delineated.     

Seeing through dust and water vapor: Millimeter wave radar sensors for mining applications

This paper defines the issues required for the development of successful visualization sensors for use in open cut and underground mines. It examines the mine environment and considers both the reflectivity of the rock and attenuation effects of dust and water droplets. Millimeter wave technology, as an alternative to the more commonly used laser and sonar implementations, is selected due to its superior penetration through adverse atmospheric conditions. Of the available radar techniques, frequency modulated continuous wave (FMCW) is selected as being the most robust. The theoretical performance of a number of 77 and 94 GHz FMCW millimeter wave radar systems is determined and these confirm the capability of these sensors in the mining environment. Implementations of FMCW radar sensors for simple ranging and three‐dimensional surface profiling are discussed before data obtained during field trials in mines is presented to justify the selection of this technology. © 2007 Wiley Periodicals, Inc.     

Combining Intelligent Techniques for Sensor Fusion

Mobile robots rely on sensor data to build a representation of their environment. However, sensors usually provide incomplete, inconsistent or inaccurate information. Sensor fusion has been successfully employed to enhance the accuracy of sensor measures. This work proposes and investigates the use of Artificial Intelligence techniques for sensor fusion. Its main goal is to improve the accuracy and reliability of the distance measure between a robot and an object in its work environment, based on measures obtained from different sensors. Several Machine Learning algorithms are investigated to fuse the sensors data. The best model generated by each algorithm is called estimator. It is shown that the employment of estimators based on Artificial Intelligence can improve significantly the performance achieved by each sensor alone. The Machine Learning algorithms employed have different characteristics, causing the estimators to have different behaviors in different situations. Aiming to achieve an even more accurate and reliable behavior, the estimators are combined in committees. The results obtained suggest that this combination can further improve the reliability and accuracy of the distances measured by the individual sensors and estimators used for sensor fusion.     

Multi‐frequency scatterometer measurements on water surfaces agitated by artificial and natural rain

Images of rain events over the ocean acquired by a multi‐frequency/multi‐polarization Synthetic Aperture Radar (SAR) show different radar contrasts at different frequencies and polarizations. In order to better understand these effects, field and laboratory experiments were performed at different rain rates and wind speeds with scatterometers working at different radar frequencies, polarizations, and incidence angles. Our results show that the dominant scattering mechanism on a rain‐roughened water surface, observed at VV polarization, at all incidence angles is Bragg scattering from ring waves. At HH polarization the radar backscatter is caused by both ring waves and non‐propagating splash products, with the dominating effect depending on incidence angle. The reduction and enhancement of the surface roughness by ring waves and sub‐surface phenomena, respectively, result in a transition wavenumber between reduction of the radar backscattering and its enhancement of about 100 rad m^?1. We assume that this transition wavenumber depends on the drop‐size distribution of the rain. Taking into consideration the different dependencies of the radar backscatter at different frequencies and polarizations on rain rate, we suggest a method to estimate rain rates by calculating the ratio of the radar cross‐sections at L band, VV polarization and at C band, HV polarization. Provided an availability of SAR data at the respective frequency–polarization combinations, this method allows for investigating the nature of small‐scale (convective) rain events over the ocean.     

Magnetic survey and autonomous target reacquisition with a scalar magnetometer on a small AUV

A scalar magnetometer payload has been developed and integrated into a two‐man portable autonomous underwater vehicle (AUV) for geophysical and archeological surveys. The compact system collects data from a Geometrics microfabricated atomic magnetometer, a total‐field atomic magnetometer. Data from the sensor is both stored for post‐processing and made available to an onboard autonomy engine for real‐time sense and react behaviors. This system has been characterized both in controlled laboratory conditions and at sea to determine its performance limits. Methodologies for processing the magnetometer data to correct for interference and error introduced by the AUV platform were developed to improve sensing performance. When conducting seabed surveys, detection and characterization of targets of interest are performed in real‐time aboard the AUV. This system is used to drive both single‐ and multiple‐vehicle autonomous target reacquisition behaviors. The combination of on‐board target detection and autonomous reacquire capability is found to increase the effective survey coverage rate of the AUV‐based magnetic sensing system.     

Parameter measurements of moving targets using a 24-GHz radar

We designed a 24-GHz traffic surveillance radar (TSR-24) to monitor automobile and pedestrian. The range, radial velocity and direction-of-arrival (DOA) of the moving targets can be measured by the radar in real-time. The radar consists of a radar sensor, a waveform generation module, a filter and amplifier module and a digital signal processor (DSP) platform. The radar can be configured to work in Doppler mode or frequency modulated continuous wave (FMCW) mode through software configuration. The phase-monopulse approach is used for the target DOA estimation. A DOA error reduction method and the Rife method are proposed to improve the range and DOA accuracy. The parameter estimation algorithms are implemented in DSP. The radar is used to detect and track motorcycles, cars, trucks and even pedestrians. The measurement of an electric vehicle shows that the velocity error is 0.022 m/s. Various tests indicate that the range and DOA error is no more than 0.25 m and 1°, respectively.     

A shadow detection and extraction algorithm using digital elevation models and x‐band weather radar measurements

The aviation industry has been investigating the potential of synthetic and enhanced vision systems (SVS and EVS) to increase the situational awareness of pilots who are operating in low‐visibility weather conditions. Synthetic vision displays provide a real‐time depiction of a terrain model from the pilot's perspective. To ensure the integrity of this terrain depiction, consistency checking using remote sensing of the terrain environment has been suggested. This requires the detection and extraction of terrain features from both the model and the sensor measurements. Further, the features must be represented in the same reference domain.

Terrain shadowing occurs when areas are not in the line‐of‐sight of the observer. It is these shadowed regions and their morphological characteristics that are identified as the feature domain in which consistency can be assessed between two sources of terrain information. This paper describes an algorithm to extract shadow features from digital elevation models during flight to enable direct comparison with x‐band radar modus operandi. Results are presented using flight‐test data acquired from two platforms with different radar equipment.

The proposed algorithm not only has application to the consistency‐checking problem, but also to terrain navigation, image fusion, and digital elevation model accuracy assessments.     

Estimating tropical rain attenuation on the Earth-satellite path using radar data

Radar-return echoes, known as ‘reflectivity’, are exploited in the course of estimating rain attenuation along a slant path. Relevant radar gates or ‘range bins’ are identified to correlate a specific satellite path. The reflectivity value of each range bin is converted to rainfall rate using established radar reflectivity values – rainfall rates, (Z–R relation). Specific attenuation is then derived for all associated range bins. The attenuation for each bin is the product of specific attenuation and its effective path length. The summation of attenuation endured by all range bins is inferred as the attenuation along the slant path. In this study, an X-band slant path rain attenuation was estimated using 2.85 GHz (S-band) Terminal Doppler Weather Radar (TDWR) data. A technique to estimate rain attenuation by exploitation of radar information is elaborated in this article. Comparisons between the radar-derived attenuation estimations and actual satellite signal measurements are also presented. The findings were verified by comparing the generated values to the directly measured rain attenuation from the Razak satellite (RazakSAT). Radar reflectivity data were obtained from Kuala Lumpur International Airport (KLIA) radar station operated by the Malaysian Meteorology Department (MMD). Preliminary findings using the most recent Z–R relation (i.e. the generated radar-derived rain attenuation estimations) appear to show lower values than the actual measurements.     

Task performance in a head-mounted display: The impacts of varying latency

The purpose of this study was to determine how latency in a head-mounted display (HMD) affects human performance. Virtual environments (VEs) are used frequently for training. However, VEs can cause simulator sickness. Prior work in our laboratory has examined the role of varying latency in simulator sickness. However, the effect of varying latency on task performance has not been examined. Subjects participated in a repeated measures study where they were exposed to two different latency conditions in an HMD: constant (70 ms) and varying (70–270 ms). During each HMD exposure, subjects used a laser pointer to repeatedly “shoot” at laser targets while accuracy and time-to-hit were recorded. Subjects scored fewer hits and took longer to hit targets in the varying latency condition. These findings indicate that individuals exposed to varying latency perform worse than individuals exposed to a lower constant latency.     

基于PCI总线的雷达杂波模拟器

该文介绍了一种基于PCI总线和FPGA技术的雷达杂波模拟器.该模拟器主要用于在实验室环境下对雷达进行工作效能测试.在该模拟器的设计中主要采用了通用的雷达杂波数学模型,通过该模型计算出雷达在特定环境下的杂波数据.同时该模拟器具备实际记录杂波的播放功能.杂波数据通过PCI总线,传输给杂波模拟器.通过时域和频域的数字信号处理的方法,用FPGA器件构成卷积器或FFT运算器,还原出与雷达信号形式相关的杂波信号.该输出的杂波信号连同模拟的目标回波信号将被注入到雷达的接收机前端,用于检测雷达在各种杂波及无源干扰条件下对目标的处理能力.在该文中还介绍了该模拟器的典型应用情况.     

Robust filter design for piecewise discrete‐time systems with time‐varying delays

A novel delay‐dependent filtering design approach is developed for a class of linear piecewise discrete‐time systems with convex‐bounded parametric uncertainties and time‐varying delays. The time‐delays appear in the state as well as the output and measurement channels. The filter has a linear full‐order structure and guarantees the desired estimation accuracy over the entire uncertainty polytope. The desired accuracy is assessed in terms of either ??_∞‐performance or ??₂–??_∞ criteria. A new parametrization procedure based on a combined Finsler's Lemma and piecewise Lyapunov–Krasovskii functional is established to yield sufficient conditions for delay‐dependent filter feasibility. The filter gains are determined by solving a convex optimization problem over linear matrix inequalities. In comparison to the existing design methods, the developed methodology yields the least conservative measures since all previous overdesign limitations are almost eliminated. By means of simulation examples, the advantages of the developed technique are readily demonstrated. Copyright © 2009 John Wiley & Sons, Ltd.     

Target tracking with fast adaptive revisit time based on steady state IMM filter

Time is a valuable resource in phased-array radar and proper use of it enables the radar to track more targets simultaneously. Appropriate usage of time in the tracking means the radar can minimize the revisit rate of the tracked targets while the accuracy of targets position estimation is still acceptable. In this paper, a new method for determination of target revisiting time is proposed which has relatively low computational cost and uses steady state filters with Interactive Multiple Models structure. The proposed algorithm allows the radar designer to determine the filter parameters by some criteria such as the desired accuracy and targets maneuver. The performance of the proposed method is compared with the conventional filters for standard maneuverable flying targets. Simulations show the better accuracy and reliability of the proposed method beside its less target revisit time and computational load.     

Autonomous wireless radar sensor mote for target material classification

Autonomous wireless sensor networks consisting of different types of sensor modalities have been receiving greater attention from researchers due to their versatility and portability. These autonomous sensor networks commonly include passive sensors such as infrared, acoustic, vibration, and magnetic nodes. However, fusion of active sensors in the integrated sensor network, such as Doppler radars, may offer powerful capabilities for many different sensing and classification tasks. In this work, we demonstrate the design and implementation of an autonomous wireless sensor network integrating a Doppler sensor with commercial off-the-shelf components. We investigate the effect of various types of target materials on the measured radar signal as one of the applications of the newly designed radar–mote network. Different types of materials affect the amount of energy reflected back to the source of an electromagnetic wave. We obtain mathematical and simulation models for the reflectivity of different homogeneous non-conducting materials and study the effect of such reflectivity on the classification of targets. We validate our simulation results using real experimental data collected through our autonomous radar–mote sensor network using various types of targets.