Developing selective mining capability for longwall shearers using thermal infrared-based seam tracking

Longwall mining continues to remain the most efficient method for underground coal recovery. A key aspect in achieving safe and productive longwall mining is to ensure that the shearer is always correctly positioned within the coal seam. At present, this machine positioning task is the role of longwall personnel who must simultaneously monitor the longwall coal face and the shearer's cutting drum position to infer the geological trends of the coal seam. This is a labour intensive task which has negative impacts on the consistency and quality of coal production. As a solution to this problem, this paper presents a sensing method to automatically track geological coal seam features on the longwall face, known as marker bands, using thermal infrared imaging. These non-visible marker bands are geological features that link strongly to the horizontal trends present in layered coal seams. Tracking these line-like features allows the generation of a vertical datum that can be used to maintain the shearer in a position for optimal coal extraction. Details on the theory of thermal infrared imaging are given, as well as practical aspects associated with machine-based implementation underground. The feature detection and tracking tasks are given with real measurements to demonstrate the efficacy of the approach. The outcome is important as it represents a new selective mining capability to help address a long-standing limitation in longwall mining operations.

Deep Learning Based Target Tracking and Classification for Infrared Videos Using Compressive Measurements

Although compressive measurements save data storage and bandwidth usage, they are difficult to be used directly for target tracking and classification without pixel reconstruction. This is because the Gaussian random matrix destroys the target location information in the original video frames. This paper summarizes our research effort on target tracking and classification directly in the compressive measurement domain. We focus on one particular type of compressive measurement using pixel subsampling. That is, original pixels in video frames are randomly subsampled. Even in such a special compressive sensing setting, conventional trackers do not work in a satisfactory manner. We propose a deep learning approach that integrates YOLO (You Only Look Once) and ResNet (residual network) for multiple target tracking and classification. YOLO is used for multiple target tracking and ResNet is for target classification. Extensive experiments using short wave infrared (SWIR), mid-wave infrared (MWIR), and long-wave infrared (LWIR) videos demonstrated the efficacy of the proposed approach even though the training data are very scarce.

Multipoint Infrared Telemetry System for Measuring the Piston Temperature in Internal Combustion Engines

A high precision, high antijamming multipoint infrared telemetry system was developed to measure the piston temperature in internal combustion engine. The temperature at the measuring point is converted into corresponding voltage signal by the thermo-couple first. Then after the V/F stage, the voltage signal is converted into the frequency signal to drive the infrared light-emitting diode to transmit infrared pulses. At the receiver end, a photosensitive audion receives the infrared pulses. After conversion, the voltage recorded by the receiver stands for the magnitude of temperature at the measuring point. Test results of the system indicate that the system is practical and the system can perform multipoint looping temperature measurements for the piston.

High resolution tracking of adult horseshoe crabs Limulus polyphemus in a New Hampshire estuary using fixed array ultrasonic telemetry

While several studies have documented the large-scale, seasonal movements of horseshoe crabs, little is known about their fine-scale, daily movement patterns. In this study we used a fixed array ultrasonic telemetry system to track the movements of 12 male and 16 female horseshoe crabs in the Great Bay estuary, New Hampshire. Data were obtained during the mating season, as well as during the remainder of the summer and fall, in the years 2005-2008. During the mating season animals were often, but not always, active during the high tides when they were approaching and leaving the spawning beaches. On average, both males and females approached mating beaches during 33% of the high tides they experienced and they most often made the tran- sition from being inactive to active during the last two hours of an incoming tide. From April-October horseshoe crabs were significantly more active during high tide periods vs low tide periods, with no clear preference for diurnal vs nocturnal activity. After the mating season ended horseshoe crabs continued to move into shallower water at high tide and then return to deeper water at low tide. Observations by SCUBA divers suggest that during these excursions into the mudflats horseshoe crabs were digging pits in the sediment while foraging for food. Thus, the tidal rhythm of activity that has been so well documented during the mating season probably persists into the fall, and primarily involves foraging activities

Target Tracking and Classification Using Compressive Measurements of MWIR and LWIR Coded Aperture Cameras

Pixel-wise Code Exposure (PCE) camera is one type of compressive sensing camera that has low power consumption and high compression ratio. Moreover, a PCE camera can control individual pixel exposure time that can enable high dynamic range. Conventional approaches of using PCE camera involve a time consuming and lossy process to reconstruct the original frames and then use those frames for target tracking and classification. In this paper, we present a deep learning approach that directly performs target tracking and classification in the compressive measurement domain without any frame reconstruction. Our approach has two parts: tracking and classification. The tracking has been done using YOLO (You Only Look Once) and the classification is achieved using Residual Network (ResNet). Extensive experiments using mid-wave infrared (MWIR) and long-wave infrared (LWIR) videos demonstrated the efficacy of our proposed approach.

Synchronous line-tracking robots based on STM 32

A pair of synchronous line-tracking robots based on STM32 are designed. Each robot is actually a small intelligent car with seven reflective infrared photoelectric sensors ST188 installed in the front to track the line. Two rear wheels each driven by a moter are the driving wheels, while each rooter is driven by an H-bridge circuit. The running direction is con- trolled by the turning of a servo fastened to the front wheel and the adjustment of speed difference between the rear wheels. Besides, the light-adaptive line-tracking can be performed. The speeds of the motors are controlled by adjusting pulse-width modulation （PWM） values and an angular displacement transducer is used to detect the relative position of the cars in real time. Thus, the speeds of the cars can be adjusted in time so that the synchronism of the cars can be achieved. Through ex-periments, the fast and accurate synchronous tracking can be well realized.

基于改进PP-YOLOE和ByteTrack算法的红外船舶目标检测跟踪方法

针于无人机侦察采集到的红外影像,如何快速准确地检测到船舶目标,并进行连续跟踪,提出了一种基于改进PP-YOLOE和ByteTrack算法的红外船舶目标检测跟踪方法。对红外船舶目标检测过程中存在的多尺度、小目标、有遮挡的情况,在PP-YOLOE算法基础上,通过采用任务对齐学习(task alignment learning,TAL)、增加多采样路径、加强检测头的方式进行了相应的改进;对跟踪过程中通常存在遮挡容易导致跟踪丢失的情况,在ByteTrack算法基础上,通过将卡尔曼滤波与匈牙利算法相结合、增加ReID特征计算外观相似度的方法进行了一定的强化。实验结果表明,所提方法检测精度较高,跟踪效果较好,能够满足现实任务的需要。

基于改进ByteTrack算法的红外地面多目标跟踪方法

红外目标智能检测跟踪技术研究一直是同领域中的热点问题,尤其是在精确制导、海面监视和天空预警等方面。针对红外地面多目标跟踪场景中,由地面杂波干扰、多目标遮挡干扰、平台晃动等复杂场景造成的跟踪精度降低等问题,提出了一种基于改进ByteTrack算法的红外地面多目标跟踪方法。首先引用一种自适应调制噪声尺度的卡尔曼滤波器,缓解低质量检测对vanilla卡尔曼滤波器的影响;其次引入增强相关系数最大化算法对帧间图像进行配准,来补偿平台晃动产生的影响;然后增加了基于长短期记忆网络的运动模型,减小了卡尔曼滤波在非线性运动状态中产生的预测误差;最后引入连接模型和高斯平滑算法这两种轻量级离线算法来完善跟踪结果。在红外地面多目标数据集上进行了实验,结果表明,与Sort和Deepsort算法相比,改进算法的MOTA值分别提升了8.3%和10.2%,IDF1值分别提升了6.5%和5.6%。与同类算法相比,改进算法表现出了更好的有效性,在红外目标智能检测跟踪场景中会有较大应用。

Snake-Hot-Eye-Assisted Multi-Process-Fusion Target Tracking Based on a Roll-Pitch Semi-strapdown Infrared Imaging Seeker

Aiming at intercepting large maneuvering targets precisely,the guidance law of advanced self-seeking missiles requires not only inertial line-of-sight(LOS)angular rate but also target maneuvering acceleration.Moreover,the semi-strapdown stabilization platform has lost the ability to measure the inertial LOS angular rate directly,which needs to be extracted by numerical calculation.The differential operation commonly used in traditional methods can magnify the measurement error of the sensor,resulting in insufficient calculation accuracy of the line-of-sight angular rate.By analyzing the mathematical relationship between the missile-target relative motion and the angle tracking system,a multi-process-fusion integrated filter model of relative motion and angle tracking is presented.To overcome the defect that the infrared seeker cannot directly measure the missile-target distance,following the snake-hot-eye visual mechanism,a visual bionic imaging guidance method of estimating the missile-target relative distance from the infrared images is proposed to improve the observability of the filter model.Finally,target-tracking simulations verify that the estimation accuracy of target acceleration is improved by four times.

Ultrastable Near-Infrared Aggregation-Induced Emission Nanoparticles as a Fluorescent Probe:Long-Term Tumor Monitoring and Lipid Droplet Tracking

Effective real-time tumor monitoring and cell tracking are of great importance for precise diagnosis and therapy of tumors,and also for the surveillance of biological processes.In this study,a new organic fluorescent nanoprobe(named TPATBT NPs)with unique aggregation-induced emission(AIE)characteristics has been obtained for the first time via facile synthesis to achieve real-time and long-term monitoring in living cells.The advantages of TPATBT NPs include small size(∼80 nm),a large Stokes shift(∼150 nm),high stability,good dispersibility in aqueous media,and biocompatibility.In addition,such NPs have showed excellent bioimaging performance and unusual long-term tumor monitoring properties.The red fluorescence signals inside MDA-MB-231 cells last for longer than 10 generations(18 days).Moreover,the cellular uptake of TPATBT NPs has been found to highly rely on energy-dependent endocytosis and clathrin-mediated endocytosis,and to primarily accumulate in lipid droplets(LDs),which can lead to targeted LD cellular imaging and therapy.Thus,TPATBT NPs can work as an excellent fluorescent nanoprobe for long-term monitoring of malignant tumor growth and dynamic biological processes.

Enhanced target tracking algorithm for autonomous driving based on visible and infrared image fusion

In autonomous driving,target tracking is essential to environmental perception.The study of target tracking algorithms can improve the accuracy of an autonomous driving vehicle’s perception,which is of great significance in ensuring the safety of autonomous driving and promoting the landing of technical applications.This study focuses on the fusion tracking algorithm based on visible and infrared images.The proposed approach utilizes a feature-level image fusion method,dividing the tracking process into two components:image fusion and target tracking.An unsupervised network,Visible and Infrared image Fusion Network(VIF-net),is employed for visible and infrared image fusion in the image fusion part.In the target tracking part,Siamese Region Proposal Network(SiamRPN),based on deep learning,tracks the target with fused images.The fusion tracking algorithm is trained and evaluated on the visible infrared image dataset RGBT234.Experimental results demonstrate that the algorithm outperforms training networks solely based on visible images,proving that the fusion of visible and infrared images in the target tracking algorithm can improve the accuracy of the target tracking even if it is like tracking-based visual images.This improvement is also attributed to the algorithm’s ability to extract infrared image features,augmenting the target tracking accuracy.

基于红外相机和毫米波雷达融合的烟雾遮挡无人驾驶车辆目标检测与跟踪

战场环境下无人驾驶车辆的感知系统易受烟雾、扬尘等天气的影响,对关键目标的检测与跟踪能力大大下降,造成目标漏检、目标误检、目标丢失等严重后果。针对该问题,开发毫米波雷达和红外相机融合系统,采用目标级融合方式建立简洁有效的融合规则,提炼和组合各传感器的优势信息,最终输出稳定的目标感知结果。对毫米波雷达的目标进行有效性检验和提取,并提出改进的基于密度的含噪声空间聚类应用算法,以减少毫米波雷达噪音干扰。以YOLOv4网络为基础,引入MobileNetv2主干网络,在网络训练过程中运用迁移学习方法,同时对红外数据样本进行扩充,解决了红外图像训练样本少的问题。试验结果表明,相较于仅基于红外相机的算法,融合检测算法在烟雾环境下的精度显著提升,且算法实时性高,实现了烟雾环境下毫米波雷达与红外相机融合的目标检测与跟踪,提高了无人驾驶车辆目标检测与跟踪系统的抗烟雾干扰能力。

红外地面目标智能抗遮挡跟踪算法研究

针对背景遮挡导致红外地面目标跟踪失败的问题,提出了一种基于改进轨迹预测网络的红外地面目标抗遮挡跟踪器。提出了遮挡判断准则,准确判断红外地面目标的遮挡情况;改进BiTrap轨迹预测网络,一方面通过孪生网络结构引入速度信息,采用单向预测的方法,提出了SiamTrap轨迹预测网络,提高了轨迹预测的精度;另一方面,通过改进训练方法和应用方法,可以更准确地预测地面目标的轨迹。对于短期遮挡,利用SiamTrap网络基于时间上下文信息预测目标遮挡位置。对于长期遮挡,提出了搜索扩展策略来处理真实目标信息缺乏导致的预测误差积累。提出了“二次判定”准则,实现了目标的精确捕获和正常跟踪。在含有遮挡的红外目标跟踪序列上对算法进行了对比测试。与基准跟踪器相比,文中所提算法在OPE评价指标下,成功率和准确率分别提高了5.2%和5.9%。这表明文中算法在应对红外地面目标被遮挡情况下具有良好的鲁棒性。

基于GLMB滤波的复杂场景下红外弱小目标自适应跟踪算法

针对红外弱小目标在复杂场景下受到漏检和杂波影响,导致跟踪不连续甚至失效的问题,本文提出一种红外弱小目标自适应跟踪算法。在预处理阶段,为了减少不必要的计算,首先定义一种衡量图像复杂度的算法。然后该算法通过计算红外图像多个特征得到场景复杂度来确认场景类型,再根据场景类型选取对应的检测算法提取目标候选位置、灰度以及局部直方图等特征建立对应的量测模型与似然函数。在目标跟踪阶段,为了自适应地匹配广义标签多伯努利(Generalized Labeled Multi-Bernoulli,GLMB)滤波器的滤波参数,在GLMB的基础上提出一种适应视频图像的新生算法进行航迹起始;针对红外图像序列目标检测概率未知的情况,将未知检测概率的基数化概率假设密度(Cardinality Probability Hypothesis Density,CPHD)滤波器集成到GLMB中实时估计目标检测概率以提升跟踪精度。仿真结果表明,所提出算法能有效地排除量测漏检和虚警的干扰,跟踪不同红外复杂场景下的弱小目标。

利用Transformer的多模态目标跟踪算法

目前目标跟踪方法大多通过融合不同模态信息进行定位决策,存在信息提取不充分、融合方法简单、弱光场景无法准确跟踪目标的问题。为此,提出一种基于Transformer的多模态目标跟踪算法(Trans-RGBT):利用伪孪生网络对可见光图像和红外图像分别进行特征提取,并在特征层面充分融合;将首帧目标信息调制到待跟踪帧的特征向量中,得到一个专用跟踪器;应用Transformer的方法对视野中的目标进行编解码,通过空间位置预测分支预测目标在视野中的空间位置,并结合历史信息滤除干扰目标,得到目标的准确位置;使用矩形框回归网络预测目标的外接矩形框,从而实现目标准确跟踪。在最新的大规模数据集VTUAV、RGBT234上进行了实验,与孪生网络(Siambased)、滤波(filter-based)算法相比,Trans-RGBT精度更高、鲁棒性更好、速度接近实时,达22 FPS。

智能天地一体化网络的卫星跟踪测控技术综述

随着卫星互联网和我国航天测控技术的不断进步,航天测控网络朝着智能化、一体化的方向发展,在自主测控、资源分配等方面进展良好。因此,建立智能天地一体化的航天测控网是我国航天未来发展的重要目标。针对智能航天测控网中的跟踪测轨、遥测和遥控三个方面,分别介绍了相关原理与技术。同时,结合CCSDS提出的空间数据链路标准协议详细介绍了TM、TC、AOS、Proximity-1以及USLP标准,分析了不同标准所使用的技术与实际应用。本文从数据链路层和物理层的角度介绍了智能航天测控系统的工作原理及技术要求,为我国智能天地一体化卫星测控通信网的研究提供参考并予以展望。

基于双卫星导航接收机的测控装备精度验收方法研究

针对测控装备精度验收的问题,提出了采用双卫星导航接收机的测控装备精度验收方案,给出精度验收的一般流程,详细推导了一种不同于传统的真值计算新方法,并提供了此方法的具体算法和模型。在理论分析的基础上,采用算例进行计算和分析,结果表明基于双卫星导航接收机的测控装备精度验收方法,可以事先根据测控装备的精度指标,分析所需卫星导航接收机的指标,同时也可以为航路设计提供定量数据支撑,节省测控装备精度验收的人力与物力,可以在测控装备精度验收的相关领域推广应用。

基于核相关滤波的深空目标跟踪算法

随着天基红外探测器接近目标,目标在焦平面的成像会由小变大,在近距离时以面目标形式成像,随着距离的接近,目标的尺度变化会越来越剧烈。针对经典核相关滤波算法无法应对尺度变化及遮挡的问题,论文提出了一种融合尺度滤波的核相关滤波跟踪算法。在位置滤波器预测目标位置之后,由尺度滤波器计算尺度响应以解决目标尺度变化问题。针对目标跟踪过程中的遮挡问题,提出了一种遮挡判别依据,遮挡情况下停止更新模板以实现稳定、鲁棒的跟踪。通过对比实验表明,该算法可以有效地应对尺度变化及遮挡情况,且帧率可以达到120 FPS,满足实时要求。

一种航天测控冗余跟踪弧段处理方法

随着在轨航天器的日益增多,测控资源的需求量也日益增大,因此,其使用效率也倍受关注。当航天器重要测控弧段安排多个测控设备进行全力跟踪时,很容易出现测控弧段搭接或完全被覆盖的情况。为有效利用现有测控资源,针对弧段搭接的情况,提出了一种基于冗余度评价函数的跟踪弧段处理方法,该方法在不降低跟踪总时长情况下,通过合理有效地配置测控资源,剔除了冗余跟踪弧段,提高了现有测控资源的使用效率。

基于遥测姿态变换的外测数据跟踪部位修正方法

外测数据跟踪部位修正对于飞行器飞行试验的高精度轨迹参数获取有着重要意义.提出一种基于飞行器遥测姿态数据和粗略轨迹参数的跟踪部位修正新方法,利用遥测姿态数据确定惯导平台中心与跟踪部位轨迹参数的变换关系,进而由粗略轨迹参数推算惯导平台中心与跟踪部位测距、径向速度、方位角和俯仰角的差异值,实现跟踪部位修正量的准确计算.采用误差传播法对粗略轨迹误差影响下的跟踪部位修正精度进行理论分析,验证了该修正方法在实际应用中的有效性.进一步通过仿真实验和实装实验检验了修正精度.结果表明跟踪部位修正量的计算误差远小于外测系统的精度指标要求,有重要的实际应用价值.