Статті в журналах: "Deep space tracking"

1

Genova, Antonio, and Flavio Petricca. "Deep-Space Navigation with Intersatellite Radio Tracking." Journal of Guidance, Control, and Dynamics 44, no. 5 (May 2021): 1068–79. http://dx.doi.org/10.2514/1.g005610.

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2

Davarian, Faramaz, and Luitjens Popken. "Technical Advances in Deep-Space Communications and Tracking." Proceedings of the IEEE 95, no. 11 (November 2007): 2108–10. http://dx.doi.org/10.1109/jproc.2007.906610.

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3

Bocanegra-Bahamón, T. M., G. Molera Calvés, L. I. Gurvits, D. A. Duev, S. V. Pogrebenko, G. Cimò, D. Dirkx, and P. Rosenblatt. "Planetary Radio Interferometry and Doppler Experiment (PRIDE) technique: A test case of the Mars Express Phobos Flyby." Astronomy & Astrophysics 609 (January 2018): A59. http://dx.doi.org/10.1051/0004-6361/201731524.

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Context. Closed-loop Doppler data obtained by deep space tracking networks, such as the NASA Deep Space Network (DSN) and the ESA tracking station network (Estrack), are routinely used for navigation and science applications. By shadow tracking the spacecraft signal, Earth-based radio telescopes involved in the Planetary Radio Interferometry and Doppler Experiment (PRIDE) can provide open-loop Doppler tracking data only when the dedicated deep space tracking facilities are operating in closed-loop mode. Aims. We explain the data processing pipeline in detail and discuss the capabilities of the technique and its potential applications in planetary science. Methods. We provide the formulation of the observed and computed values of the Doppler data in PRIDE tracking of spacecraft and demonstrate the quality of the results using an experiment with the ESA Mars Express spacecraft as a test case. Results. We find that the Doppler residuals and the corresponding noise budget of the open-loop Doppler detections obtained with the PRIDE stations compare to the closed-loop Doppler detections obtained with dedicated deep space tracking facilities.

4

Gawronski, W. "Predictive Controller and Estimator for NASA Deep Space Network Antennas." Journal of Dynamic Systems, Measurement, and Control 116, no. 2 (June 1, 1994): 241–48. http://dx.doi.org/10.1115/1.2899216.

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This paper presents a modified output prediction procedure, and a new controller design based on the predictive control law. Also, a predictive estimator is developed for implementing the controller. The predictive controller was designed and simulated for tracking control of the NASA Deep Space Network 70-m antenna. Simulation results show significant improvement in tracking performance compared to the linear quadratic controller and estimator presently in use.

5

Teitelbaum, Lawrence, Walid Majid, Manuel M. Franco, Daniel J. Hoppe, Shinji Horiuchi, and T. Joseph W. Lazio. "Precision Pulsar Timing with NASA's Deep Space Network." Proceedings of the International Astronomical Union 11, A29B (August 2015): 367–69. http://dx.doi.org/10.1017/s174392131600555x.

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AbstractMillisecond pulsars (MSPs) are a class of radio pulsars with extremely stable rotation. Their excellent timing stability can be used to study a wide variety of astrophysical phenomena. In particular, a large sample of these pulsars can be used to detect low-frequency gravitational waves. We have developed a precision pulsar timing backend for the NASA Deep Space Network (DSN), which will allow the use of short gaps in tracking schedules to time pulses from an ensemble of MSPs. The DSN operates clusters of large dish antennas (up to 70-m in diameter), located roughly equidistant around the Earth, for communication and tracking of deep-space spacecraft. The backend system will be capable of removing entirely the dispersive effects of propagation of radio waves through the interstellar medium in real-time. We will describe our development work, initial results, and prospects for future observations over the next few years.

6

Mukai, R., V. A. Vilnrotter, P. Arabshahi, and V. Jamnejad. "Adaptive acquisition and tracking for deep space array feed antennas." IEEE Transactions on Neural Networks 13, no. 5 (September 2002): 1149–62. http://dx.doi.org/10.1109/tnn.2002.1031946.

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7

Chen, Yijiang, Hamid Hemmati, and Gerry G. Ortiz. "Feasibility of infrared Earth tracking for deep-space optical communications." Optics Letters 37, no. 1 (December 24, 2011): 73. http://dx.doi.org/10.1364/ol.37.000073.

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8

Johnston, Mark D., Daniel Tran, Belinda Arroyo, Sugi Sorensen, Peter Tay, Butch Carruth, Adam Coffman, and Mike Wallace. "Automated Scheduling for NASA's Deep Space Network." AI Magazine 35, no. 4 (December 22, 2014): 7–25. http://dx.doi.org/10.1609/aimag.v35i4.2552.

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This article describes the DSN scheduling wngine (DSE) component of a new scheduling system being deployed for NASA's deep space network. The DSE provides core automation functionality for scheduling the network, including the interpretation of scheduling requirements expressed by users, their elaboration into tracking passes, and the resolution of conflicts and constraint violations. The DSE incorporates both systematic search and repair-based algorithms, used for different phases and purposes in the overall system. It has been integrated with a web application which provides DSE functionality to all DSN users through a standard web browser, as part of a peer-to-peer schedule negotiation process for the entire network. The system has been deployed operationally and is in routine use, and is in the process of being extended to support long-range planning and forecasting, and near-real-time scheduling.

9

Yamamoto, Zen-icji, Haruto Hirosawa, and Tamiya Nomura. "Dual Speed PN Ranging System for Tracking of Deep Space Probes." IEEE Transactions on Aerospace and Electronic Systems AES-23, no. 4 (July 1987): 519–27. http://dx.doi.org/10.1109/taes.1987.310885.

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10

Davarian, Faramaz, and Luitjens Popken. "Special Issue on Technical Advances in Deep-Space Communications and Tracking." Proceedings of the IEEE 95, no. 10 (October 2007): 1898–901. http://dx.doi.org/10.1109/jproc.2007.905981.

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11

Bokulic, R. S., and J. R. Jensen. "Experimental verification of noncoherent Doppler tracking at the Deep Space Network." IEEE Transactions on Aerospace and Electronic Systems 36, no. 4 (2000): 1401–6. http://dx.doi.org/10.1109/7.892689.

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12

Song, Qingping, and Rongke Liu. "Weighted adaptive filtering algorithm for carrier tracking of deep space signal." Chinese Journal of Aeronautics 28, no. 4 (August 2015): 1236–44. http://dx.doi.org/10.1016/j.cja.2015.05.001.

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13

Zhang, J. S., J. Cao, B. Mao, and D. Q. Shen. "EXTRACTING 3D SEMANTIC INFORMATION FROM VIDEO SURVEILLANCE SYSTEM USING DEEP LEARNING." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3 (April 30, 2018): 2257–61. http://dx.doi.org/10.5194/isprs-archives-xlii-3-2257-2018.

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At present, intelligent video analysis technology has been widely used in various fields. Object tracking is one of the important part of intelligent video surveillance, but the traditional target tracking technology based on the pixel coordinate system in images still exists some unavoidable problems. Target tracking based on pixel can’t reflect the real position information of targets, and it is difficult to track objects across scenes. Based on the analysis of Zhengyou Zhang's camera calibration method, this paper presents a method of target tracking based on the target's space coordinate system after converting the 2-D coordinate of the target into 3-D coordinate. It can be seen from the experimental results: Our method can restore the real position change information of targets well, and can also accurately get the trajectory of the target in space.

14

Zhai, Chengxing, Quanzhi Ye, Michael Shao, Russell Trahan, Navtej S. Saini, Janice Shen, Thomas A. Prince, et al. "Synthetic Tracking Using ZTF Deep Drilling Data Sets." Publications of the Astronomical Society of the Pacific 132, no. 1012 (April 21, 2020): 064502. http://dx.doi.org/10.1088/1538-3873/ab828b.

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15

Gawronski, W., J. J. Beech-Brandt, H. G. Ahlstrom, and E. Maneri. "Torque-bias profile for improved tracking of the Deep Space Network antennas." IEEE Antennas and Propagation Magazine 42, no. 6 (2000): 35–45. http://dx.doi.org/10.1109/74.894180.

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16

Buu, C. M., F. A. Jenet, J. W. Armstrong, S. W. Asmar, M. Beroiz, T. Cheng, and J. A. O'Dea. "A Prototype Radio Transient Survey Instrument for Piggyback Deep Space Network Tracking." Proceedings of the IEEE 99, no. 5 (May 2011): 889–94. http://dx.doi.org/10.1109/jproc.2010.2053830.

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17

Miller, James G. "Covariance analysis for deep-space satellites with radar and optical tracking data." Journal of the Astronautical Sciences 55, no. 2 (June 2007): 237–43. http://dx.doi.org/10.1007/bf03256522.

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18

Zhang, Rui, Zhaokui Wang, and Yulin Zhang. "Astronaut Visual Tracking of Flying Assistant Robot in Space Station Based on Deep Learning and Probabilistic Model." International Journal of Aerospace Engineering 2018 (July 12, 2018): 1–17. http://dx.doi.org/10.1155/2018/6357185.

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Real-time astronaut visual tracking is the most important prerequisite for flying assistant robot to follow and assist the served astronaut in the space station. In this paper, an astronaut visual tracking algorithm which is based on deep learning and probabilistic model is proposed. Fine-tuned with feature extraction layers’ parameters being initialized by ready-made model, an improved SSD (Single Shot Multibox Detector) network was proposed for robust astronaut detection in color image. Associating the detection results with synchronized depth image measured by RGB-D camera, a probabilistic model is presented to ensure accurate and consecutive tracking of the certain served astronaut. The algorithm runs 10 fps at Jetson TX2, and it was extensively validated by several datasets which contain most instances of astronaut activities. The experimental results indicate that our proposed algorithm achieves not only robust tracking of the specified person with diverse postures or dressings but also effective occlusion detection for avoiding mistaken tracking.

19

Subramanyam, A. V. G., D. Siva Reddy, V. K. Hariharan, V. V. Srinivasan, and Ajay Chakrabarty. "High Power Combline Filter for Deep Space Applications." International Journal of Microwave Science and Technology 2014 (September 14, 2014): 1–11. http://dx.doi.org/10.1155/2014/396494.

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An S-band, compact, high power filter, for use in the Mars Orbiter Mission (MOM) of Indian Space Research Organization (ISRO), has been designed and tested for multipaction. The telemetry, tracking, and commanding (TT&C) transponder of MOM is required to handle continuous RF power of 200 W in the telemetry path besides simultaneously maintaining an isolation of greater than 145 dBc to its sensitive telecommand path. This is accomplished with the help of a complex diplexer, requiring high power, high rejection transmit path filter, and a low power receive path filter. To reduce the complexity in the multipaction-free design and testing, the transmit path filter of the diplexer is split into a low rejection filter integral to the diplexer and an external high rejection filter. This paper highlights the design and space qualification phases of this high rejection filter. Multipaction test results with 6 dB margin are also presented. Major concerns of this filter design are isolation, insertion loss, and multipaction. Mission performance of the on-board filter is normal.

20

Chen, Can, Luca Zanotti Fragonara, and Antonios Tsourdos. "Relation3DMOT: Exploiting Deep Affinity for 3D Multi-Object Tracking from View Aggregation." Sensors 21, no. 6 (March 17, 2021): 2113. http://dx.doi.org/10.3390/s21062113.

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Autonomous systems need to localize and track surrounding objects in 3D space for safe motion planning. As a result, 3D multi-object tracking (MOT) plays a vital role in autonomous navigation. Most MOT methods use a tracking-by-detection pipeline, which includes both the object detection and data association tasks. However, many approaches detect objects in 2D RGB sequences for tracking, which lacks reliability when localizing objects in 3D space. Furthermore, it is still challenging to learn discriminative features for temporally consistent detection in different frames, and the affinity matrix is typically learned from independent object features without considering the feature interaction between detected objects in the different frames. To settle these problems, we first employ a joint feature extractor to fuse the appearance feature and the motion feature captured from 2D RGB images and 3D point clouds, and then we propose a novel convolutional operation, named RelationConv, to better exploit the correlation between each pair of objects in the adjacent frames and learn a deep affinity matrix for further data association. We finally provide extensive evaluation to reveal that our proposed model achieves state-of-the-art performance on the KITTI tracking benchmark.

21

Cardarilli, Gian Carlo, Luca Di Nunzio, Rocco Fazzolari, Daniele Giardino, Marco Matta, Marco Re, Luciano Iess та ін. "Hardware Prototyping and Validation of a W-ΔDOR Digital Signal Processor". Applied Sciences 9, № 14 (20 липня 2019): 2909. http://dx.doi.org/10.3390/app9142909.

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Microwave tracking, usually performed by on ground processing of the signals coming from a spacecraft, represents a crucial aspect in every deep-space mission. Various noise sources, including receiver noise, affect these signals, limiting the accuracy of the radiometric measurements obtained from the radio link. There are several methods used for spacecraft tracking, including the Delta-Differential One-Way Ranging ( Δ DOR) technique. In the past years, European Space Agency (ESA) missions relied on a narrowband Δ DOR system for navigation in the cruise phase. To limit the adverse effect of nonlinearities in the receiving chain, an innovative wideband approach to Δ DOR measurements has recently been proposed. This work presents the hardware implementation of a new version of the ESA X/Ka Deep Space Transponder based on the new tracking technique named Wideband Δ DOR (W- Δ DOR). The architecture of the new transponder guarantees backward compatibility with narrowband Δ DOR.

22

Denil, Misha, Loris Bazzani, Hugo Larochelle, and Nando de Freitas. "Learning Where to Attend with Deep Architectures for Image Tracking." Neural Computation 24, no. 8 (August 2012): 2151–84. http://dx.doi.org/10.1162/neco_a_00312.

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We discuss an attentional model for simultaneous object tracking and recognition that is driven by gaze data. Motivated by theories of perception, the model consists of two interacting pathways, identity and control, intended to mirror the what and where pathways in neuroscience models. The identity pathway models object appearance and performs classification using deep (factored)-restricted Boltzmann machines. At each point in time, the observations consist of foveated images, with decaying resolution toward the periphery of the gaze. The control pathway models the location, orientation, scale, and speed of the attended object. The posterior distribution of these states is estimated with particle filtering. Deeper in the control pathway, we encounter an attentional mechanism that learns to select gazes so as to minimize tracking uncertainty. Unlike in our previous work, we introduce gaze selection strategies that operate in the presence of partial information and on a continuous action space. We show that a straightforward extension of the existing approach to the partial information setting results in poor performance, and we propose an alternative method based on modeling the reward surface as a gaussian process. This approach gives good performance in the presence of partial information and allows us to expand the action space from a small, discrete set of fixation points to a continuous domain.

23

Yang, Guosheng, and Qisheng Wei. "Visual Object Multimodality Tracking Based on Correlation Filters for Edge Computing." Security and Communication Networks 2020 (December 10, 2020): 1–13. http://dx.doi.org/10.1155/2020/8891035.

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In recent years, visual object tracking has become a very active research field which is mainly divided into the correlation filter-based tracking and deep learning (e.g., deep convolutional neural network and Siamese neural network) based tracking. For target tracking algorithms based on deep learning, a large amount of computation is required, usually deployed on expensive graphics cards. However, for the rich monitoring devices in the Internet of Things, it is difficult to capture all the moving targets in each device in real time, so it is necessary to perform hierarchical processing and use tracking based on correlation filtering in insensitive areas to alleviate the local computing pressure. In sensitive areas, upload the video stream to a cloud computing platform with a faster computing speed to perform an algorithm based on deep features. In this paper, we mainly focus on the correlation filter-based tracking. In the correlation filter-based tracking, the discriminative scale space tracker (DSST) is one of the most popular and typical ones which is successfully applied to many application fields. However, there are still some improvements that need to be further studied for DSST. One is that the algorithms do not consider the target rotation on purpose. The other is that it is a very heavy computational load to extract the histogram of oriented gradient (HOG) features from too many patches centered at the target position in order to ensure the scale estimation accuracy. To address these two problems, we introduce the alterable patch number for target scale tracking and the space searching for target rotation tracking into the standard DSST tracking method and propose a visual object multimodality tracker based on correlation filters (MTCF) to simultaneously cope with translation, scale, and rotation in plane for the tracked target and to obtain the target information of position, scale, and attitude angle at the same time. Finally, in Visual Tracker Benchmark data set, the experiments are performed on the proposed algorithms to show their effectiveness in multimodality tracking.

24

Luo, Cui Hua, Hai Feng Qi, and Cai Wen Ma. "Linearization of Coupled Pointing and Tracking Dynamical Equations." Advanced Materials Research 971-973 (June 2014): 1637–42. http://dx.doi.org/10.4028/www.scientific.net/amr.971-973.1637.

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Cooperative pointing and tracking is one important characteristic of deep space inter-satellite laser communication, so that the dynamic mathematical model is complicated nonlinearity and coupling. The contribution of this paper is to substitute the time-domain error transfer function of state equations and to linearize the state space for the sake of physical realization easily in terms of previous authors’ work. The method applied is state feedback and coordinate transformation in nonlinear field, the calculated result is demonstrated that differential equations modeled can be transferred into canonical controllable form and realized physically.

25

Gard, N. A., J. Chen, P. Tang, and A. Yilmaz. "DEEP LEARNING AND ANTHROPOMETRIC PLANE BASED WORKFLOW MONITORING BY DETECTING AND TRACKING WORKERS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-1 (September 26, 2018): 149–54. http://dx.doi.org/10.5194/isprs-archives-xlii-1-149-2018.

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<p><strong>Abstract.</strong> The worker productivity, a critical variable in project management, significantly affects the progress of a project. The key to measuring productivity is analysis of activities, which provides necessary information by identifying how workers spend their time at certain areas in the site. In this work, we propose a novel joint image-trajectory space for automatic detection and tracking of workers using a single fixed camera. A two-branch convolutional neural network detects workers and their body joints. Instead of tracking the body joints in the image space, we transform detected joints onto virtual parallel planes called “Anthropometric Planes”. The detected joints are, then, tracked using a Kalman Filter on these planes which are created based on anthropometric measures of an average American male. Finally, an uncertainty measure is introduced to reduce the number of identity changes and to handle missing joints. The experiments conducted on an image sequence captured in a nuclear plant shows promising detection and tracking results.</p>

26

Li, Shuang, Ruikun Lu, Liu Zhang, and Yuming Peng. "Image Processing Algorithms For Deep-Space Autonomous Optical Navigation." Journal of Navigation 66, no. 4 (April 22, 2013): 605–23. http://dx.doi.org/10.1017/s0373463313000131.

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As Earth-based radio tracking navigation is severely limited because of communications constraints and low relative navigation accuracy, autonomous optical navigation capabilities are essential for both robotic and manned deep-space exploration missions. Image processing is considered one of the key technologies for autonomous optical navigation to extract high-precision navigation observables from a raw image. New image processing algorithms for deep-space autonomous optical navigation are developed in this paper. First, multiple image pre-processing and the Canny edge detection algorithm are adopted to identify the edges of target celestial bodies and simultaneously remove the potential false edges. Secondly, two new limb profile fitting algorithms are proposed based on the Least Squares method and the Levenberg-Marquardt algorithm, respectively, with the assumption that the perspective projection of a target celestial body on the image plane will form an ellipse. Next, the line-of-sight (LOS) vector from the spacecraft to the centroid of the observed object is obtained. This is taken as the navigation measurement observable and input to the navigation filter algorithm. Finally, the image processing algorithms developed in this paper are validated using both synthetic simulated images and real flight images from theMESSENGERmission.

27

Hossain and Lee. "Deep Learning-Based Real-Time Multiple-Object Detection and Tracking from Aerial Imagery via a Flying Robot with GPU-Based Embedded Devices." Sensors 19, no. 15 (July 31, 2019): 3371. http://dx.doi.org/10.3390/s19153371.

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In recent years, demand has been increasing for target detection and tracking from aerial imagery via drones using onboard powered sensors and devices. We propose a very effective method for this application based on a deep learning framework. A state-of-the-art embedded hardware system empowers small flying robots to carry out the real-time onboard computation necessary for object tracking. Two types of embedded modules were developed: one was designed using a Jetson TX or AGX Xavier, and the other was based on an Intel Neural Compute Stick. These are suitable for real-time onboard computing power on small flying drones with limited space. A comparative analysis of current state-of-the-art deep learning-based multi-object detection algorithms was carried out utilizing the designated GPU-based embedded computing modules to obtain detailed metric data about frame rates, as well as the computation power. We also introduce an effective target tracking approach for moving objects. The algorithm for tracking moving objects is based on the extension of simple online and real-time tracking. It was developed by integrating a deep learning-based association metric approach with simple online and real-time tracking (Deep SORT), which uses a hypothesis tracking methodology with Kalman filtering and a deep learning-based association metric. In addition, a guidance system that tracks the target position using a GPU-based algorithm is introduced. Finally, we demonstrate the effectiveness of the proposed algorithms by real-time experiments with a small multi-rotor drone.

28

Jiang, Haodi, Jiasheng Wang, Chang Liu, Ju Jing, Hao Liu, Jason T. L. Wang, and Haimin Wang. "Identifying and Tracking Solar Magnetic Flux Elements with Deep Learning." Astrophysical Journal Supplement Series 250, no. 1 (August 26, 2020): 5. http://dx.doi.org/10.3847/1538-4365/aba4aa.

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29

Lowe, S. T., and R. N. Treuhaft. "Applications of Few-Hundred Microarcsecond VLBI Astrometry: Planetary Relativistic Deflection, PPN Gamma Determination and Deep-Space Tracking." Symposium - International Astronomical Union 156 (1993): 145–49. http://dx.doi.org/10.1017/s0074180900173127.

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This paper presents several applications of a few hundred microarcsecond (μas) astrometric technique which has been developed and demonstrated using differential very long baseline interferometry (VLBI). A brief description of the technique along with several applications will be discussed below. This technique was developed for high-accuracy deep-space tracking, but the first application tracked an extragalactic radio source in a measurement of Jovian relativistic deflection. Current work includes making a state-of-the-art solar deflection measurement, and thus, an improved determination of the Parameterized Post Newtonian (PPN) gamma parameter. A number a future spacecraft tracking applications, described below, are also enabled by this technique.

30

Paidi, Vijay, Hasan Fleyeh, Johan Håkansson, and Roger G. Nyberg. "Tracking Vehicle Cruising in an Open Parking Lot Using Deep Learning and Kalman Filter." Journal of Advanced Transportation 2021 (August 23, 2021): 1–12. http://dx.doi.org/10.1155/2021/1812647.

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Due to the lack of wide availability of parking assisting applications, vehicles tend to cruise more than necessary to find an empty parking space. This problem is evident globally and the intensity of the problem varies based on the demand of parking spaces. It is a well-known hypothesis that the amount of cruising by a vehicle is dependent on the availability of parking spaces. However, the amount of cruising that takes place in search of parking spaces within a parking lot is not researched. This lack of research can be due to privacy and illumination concerns with suitable sensors like visual cameras. The use of thermal cameras offers an alternative to avoid privacy and illumination problems. Therefore, this paper aims to develop and demonstrate a methodology to detect and track the cruising patterns of multiple moving vehicles in an open parking lot. The vehicle is detected using Yolov3, modified Yolo, and custom Yolo deep learning architectures. The detected vehicles are tracked using Kalman filter and the trajectory of multiple vehicles is calculated on an image. The accuracy of modified Yolo achieved a positive detection rate of 91% while custom Yolo and Yolov3 achieved 83% and 75%, respectively. The performance of Kalman filter is dependent on the efficiency of the detector and the utilized Kalman filter facilitates maintaining data association during moving, stationary, and missed detection. Therefore, the use of deep learning algorithms and Kalman filter facilitates detecting and tracking multiple vehicles in an open parking lot.

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Mariotti, G., and P. Tortora. "Experimental validation of a dual uplink multifrequency dispersive noise calibration scheme for Deep Space tracking." Radio Science 48, no. 2 (March 2013): 111–17. http://dx.doi.org/10.1002/rds.20024.

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32

Li, Yidi, Hong Liu, Bing Yang, Runwei Ding, and Yang Chen. "Deep Metric Learning-Assisted 3D Audio-Visual Speaker Tracking via Two-Layer Particle Filter." Complexity 2020 (August 31, 2020): 1–8. http://dx.doi.org/10.1155/2020/3764309.

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For speaker tracking, integrating multimodal information from audio and video provides an effective and promising solution. The current challenges are focused on the construction of a stable observation model. To this end, we propose a 3D audio-visual speaker tracker assisted by deep metric learning on the two-layer particle filter framework. Firstly, the audio-guided motion model is applied to generate candidate samples in the hierarchical structure consisting of an audio layer and a visual layer. Then, a stable observation model is proposed with a designed Siamese network, which provides the similarity-based likelihood to calculate particle weights. The speaker position is estimated using an optimal particle set, which integrates the decisions from audio particles and visual particles. Finally, the long short-term mechanism-based template update strategy is adopted to prevent drift during tracking. Experimental results demonstrate that the proposed method outperforms the single-modal trackers and comparison methods. Efficient and robust tracking is achieved both in 3D space and on image plane.

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Mehmood, Atif, Inam ul Hasan Shaikh, and Ahsan Ali. "Application of Deep Reinforcement Learning for Tracking Control of 3WD Omnidirectional Mobile Robot." Information Technology and Control 50, no. 3 (September 24, 2021): 507–21. http://dx.doi.org/10.5755/j01.itc.50.3.25979.

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Deep reinforcement learning, the fastest growing technique, to solve real-world complex problems by creatinga simple mathematical framework. It includes an agent, action, environment, and a reward. An agent will interactwith the environment, takes an optimal action aiming to maximize the total reward. This paper proposesthe compelling technique of deep deterministic policy gradient for solving the complex continuous actionspace of 3-wheeled omnidirectional mobile robots. Three-wheeled Omnidirectional mobile robots tracking isa difficult task because of the orientation of the wheels which makes it rotate around its own axis rather tofollow the trajectory. A deep deterministic policy gradient (DDPG) algorithm has been designed to train in environmentswith continuous action space to follow the trajectory by training the neural networks defined forthe policy and value function to maximize the reward function defined for the tracking of the trajectory. DDPGagent environment is created in the Reinforcement learning toolbox in MATLAB 2019 while for Actor and criticnetwork design deep neural network designer is used. Results are shown to illustrate the effectiveness of thetechnique with a convergence of error approximately to zero.

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Chang, Oscar, Patricia Constante, Andrés Gordon, and Marco Singaña. "A Novel Deep Neural Network that Uses Space-Time Features for Tracking and Recognizing a Moving Object." Journal of Artificial Intelligence and Soft Computing Research 7, no. 2 (April 1, 2017): 125–36. http://dx.doi.org/10.1515/jaiscr-2017-0009.

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Abstract This work proposes a deep neural net (DNN) that accomplishes the reliable visual recognition of a chosen object captured with a webcam and moving in a 3D space. Autoencoding and substitutional reality are used to train a shallow net until it achieves zero tracking error in a discrete ambient. This trained individual is set to work in a real world closed loop system where images coming from a webcam produce displacement information for a moving region of interest (ROI) inside the own image. This loop gives rise to an emergent tracking behavior which creates a self-maintain flow of compressed space-time data. Next, short term memory elements are set to play a key role by creating new representations in terms of a space-time matrix. The obtained representations are delivery as input to a second shallow network which acts as “recognizer”. A noise balanced learning method is used to fast train the recognizer with real-world images, giving rise to a simple and yet powerful robotic eye, with a slender neural processor that vigorously tracks and recognizes the chosen object. The system has been tested with real images in real time.

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Davarian, Faramaz, Douglas Abraham, Matt Angert, John Baker, Jay Gao, Norman Lay, and Jeffrey Stuart. "Improving Small Satellite Communications and Tracking in Deep Space—A Review of the Existing Systems and Technologies With Recommendations for Improvement. Part III: The Deep Space Network." IEEE Aerospace and Electronic Systems Magazine 35, no. 8 (August 1, 2020): 4–13. http://dx.doi.org/10.1109/maes.2020.2992211.

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Cannon, W. H. "Quantum mechanical uncertainty limitations on deep space navigation by Doppler tracking and very long baseline interferometry." Radio Science 25, no. 2 (March 1990): 97–100. http://dx.doi.org/10.1029/rs025i002p00097.

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37

Lee, Eunji, Youngkwang Kim, Minsik Kim, and Sang-Young Park. "Development, Demonstration and Validation of the Deep Space Orbit Determination Software Using Lunar Prospector Tracking Data." Journal of Astronomy and Space Sciences 34, no. 3 (September 15, 2017): 213–23. http://dx.doi.org/10.5140/jass.2017.34.3.213.

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Lim, Seongmin, Jin-Hyung Kim, Won-Sub Choi, and Hae-Dong Kim. "A Study on the Deep Neural Network based Recognition Model for Space Debris Vision Tracking System." Journal of the Korean Society for Aeronautical & Space Sciences 45, no. 9 (September 30, 2017): 794–806. http://dx.doi.org/10.5139/jksas.2017.45.9.794.

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39

Lau, Kam Y., and George F. Lutes. "Ultra-stable RF-over-fiber transport enables NASA ground-based deep space tracking antenna arrays and space-borne earth mapping radar." IEEE Aerospace and Electronic Systems Magazine 29, no. 9 (September 2014): 34–41. http://dx.doi.org/10.1109/maes.2014.140080.

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Yang, Senlin, and Xin Chong. "Study on Portrait Tracking Technology of Deep Feature Learning in Monitoring Image Acquisition." Journal of Imaging Science and Technology 65, no. 4 (July 1, 2021): 40502–1. http://dx.doi.org/10.2352/j.imagingsci.technol.2021.65.4.040502.

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Abstract In a network information society, there are many occasions where people’s behaviors need to be tracked, photographed, and recognized. Biometric recognition technologies are considered to be one of the most effective solutions. Traditional methods mostly use graph structure and deformed component model to design two-dimensional (2D) human body component detectors, and apply graph models to establish the connectivity of each component. The recognition design process is simple, but the accuracy of recognition and tracking effect applied in monitoring image acquisition is not high. The improved particle swarm optimization algorithm is used to determine the particle structure, and the binary bit string is used to represent the particle structure. The support vector machine (SVM) parameters of discrete particles are optimized, and the synchronous optimization design of feature selection and SVM parameters is carried out to realize the synchronous optimization of portrait feature subset and SVM parameters in discrete space. Through in-depth research, the extracted feature subsets can be effectively optimized and selected, and the parameters of SVM model can be optimized synchronously. The discrete particle structure is associated with the SVM parameters to achieve feature selection and SVM parameter synchronization and optimization. It is not only superior to traditional algorithms in terms of recognition rate, but also reduces the feature dimension and shortens the recognition time. The deep feature recognition built on the learning machine is not easy to diverge and can effectively adjust the particle speed to the global optimal, which is more effective than the particle swarm algorithm to search for the global optimal solution, and has better robustness. In the experiments, the research content of the article is compared with the traditional methods to test and analysis. The results show that the method optimizes the selection of feature subset and eliminates a large number of invalid features. The method not only reduces space complexity and shortens recognition time, but also improves recognition rate. The dimension of feature subset dimensions are superior to those extracted by other algorithms.

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BERTOLAMI, ORFEU, FREDERICO FRANCISCO, PAULO J. S. GIL, and JORGE PÁRAMOS. "TESTING THE FLYBY ANOMALY WITH THE GNSS CONSTELLATION." International Journal of Modern Physics D 21, no. 04 (April 2012): 1250035. http://dx.doi.org/10.1142/s0218271812500356.

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We propose the concept of a space mission to probe the so called flyby anomaly, an unexpected velocity change experienced by some deep-space probes using earth gravity assists. The key feature of this proposal is the use of GNSS systems to obtain an increased accuracy in the tracking of the approaching spacecraft, mainly near the perigee. Two low-cost options are also discussed to further test this anomaly: an add-on to an existing spacecraft and a dedicated mission.

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JOHANN, ULRICH A. "CONCEPT CONSIDERATIONS FOR A DEEP SPACE GRAVITY PROBE BASED ON LASER-CONTROLLED FREE-FLYING REFERENCE MASSES." International Journal of Modern Physics D 16, no. 12a (December 2007): 2297–307. http://dx.doi.org/10.1142/s0218271807011450.

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Concept considerations for a space mission with the objective of precisely testing the gravitational motion of a small test mass in the solar system environment are presented. In particular, the mission goal is an unambiguous experimental verification or falsification of the Pioneer anomaly effect. A promising concept is featuring a passive reference mass, shielded or well modeled with respect to nongravitational accelerations and formation flying with a rather standard deep space probe. The probe provides laser ranging and angular tracking to the reference mass, ranging to Earth via the radio-communication link and shielding from light pressure in the early parts of the mission. State-of-the-art ranging equipment can be used throughout, but requires in part optimization to meet the stringent physical budget constraints of a deep space mission. Mission operation aspects are briefly addressed.

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Xia, Chunlei, Longwen Fu, Zuoyi Liu, Hui Liu, Lingxin Chen, and Yuedan Liu. "Aquatic Toxic Analysis by Monitoring Fish Behavior Using Computer Vision: A Recent Progress." Journal of Toxicology 2018 (April 3, 2018): 1–11. http://dx.doi.org/10.1155/2018/2591924.

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Video tracking based biological early warning system achieved a great progress with advanced computer vision and machine learning methods. Ability of video tracking of multiple biological organisms has been largely improved in recent years. Video based behavioral monitoring has become a common tool for acquiring quantified behavioral data for aquatic risk assessment. Investigation of behavioral responses under chemical and environmental stress has been boosted by rapidly developed machine learning and artificial intelligence. In this paper, we introduce the fundamental of video tracking and present the pioneer works in precise tracking of a group of individuals in 2D and 3D space. Technical and practical issues suffered in video tracking are explained. Subsequently, the toxic analysis based on fish behavioral data is summarized. Frequently used computational methods and machine learning are explained with their applications in aquatic toxicity detection and abnormal pattern analysis. Finally, advantages of recent developed deep learning approach in toxic prediction are presented.

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Zhu, Kun, Xiaodong Zhang, Guanzhou Chen, Xiaoliang Tan, Puyun Liao, Hongyu Wu, Xiujuan Cui, Yinan Zuo, and Zhiyong Lv. "Single Object Tracking in Satellite Videos: Deep Siamese Network Incorporating an Interframe Difference Centroid Inertia Motion Model." Remote Sensing 13, no. 7 (March 29, 2021): 1298. http://dx.doi.org/10.3390/rs13071298.

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Satellite video single object tracking has attracted wide attention. The development of remote sensing platforms for earth observation technologies makes it increasingly convenient to acquire high-resolution satellite videos, which greatly accelerates ground target tracking. However, overlarge images with small object size, high similarity among multiple moving targets, and poor distinguishability between the objects and the background make this task most challenging. To solve these problems, a deep Siamese network (DSN) incorporating an interframe difference centroid inertia motion (ID-CIM) model is proposed in this paper. In object tracking tasks, the DSN inherently includes a template branch and a search branch; it extracts the features from these two branches and employs a Siamese region proposal network to obtain the position of the target in the search branch. The ID-CIM mechanism was proposed to alleviate model drift. These two modules build the ID-DSN framework and mutually reinforce the final tracking results. In addition, we also adopted existing object detection datasets for remotely sensed images to generate training datasets suitable for satellite video single object tracking. Ablation experiments were performed on six high-resolution satellite videos acquired from the International Space Station and “Jilin-1” satellites. We compared the proposed ID-DSN results with other 11 state-of-the-art trackers, including different networks and backbones. The comparison results show that our ID-DSN obtained a precision criterion of 0.927 and a success criterion of 0.694 with a frames per second (FPS) value of 32.117 implemented on a single NVIDIA GTX1070Ti GPU.

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Tang Cong, 唐. 聪., 凌永顺 Ling Yongshun, 杨. 华. Yang Hua, 杨. 星. Yang Xing, and 郑. 超. Zheng Chao. "A visual tracking method via object detection based on deep learning." Infrared and Laser Engineering 47, no. 5 (2018): 526001. http://dx.doi.org/10.3788/irla201847.0526001.

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Hashmi, Ali J., Ali A. Eftekhar, Ali Adibi, and Farid Amoozegar. "Statistical analysis and performance evaluation of optical array receivers for deep-space optical communications under random tracking errors." Physical Communication 31 (December 2018): 230–38. http://dx.doi.org/10.1016/j.phycom.2018.03.010.

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47

Guo Qiang, 郭. 强., 芦晓红 Lu Xiaohong, 谢英红 Xie Yinghong, and 孙. 鹏. Sun Peng. "Efficient visual target tracking algorithm based on deep spectral convolutional neural networks." Infrared and Laser Engineering 47, no. 6 (2018): 626005. http://dx.doi.org/10.3788/irla201847.0626005.

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48

Welch, Bryan W. "Regionalized Lunar South Pole Surface Navigation System Analysis." International Journal of Navigation and Observation 2008 (April 9, 2008): 1–7. http://dx.doi.org/10.1155/2008/435961.

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Apollo missions utilized Earth-based assets for navigation, since the landings took place at lunar locations in constant view from the Earth. The new exploration campaign to the lunar South Pole region will have limited Earth visibility, but the extent to which a navigation system comprised solely of Earth-based tracking stations will provide adequate navigation solutions in this region is unknown. This article presents a dilution-of-precision-(DoP-) based stationary surface navigation analysis of the performance of multiple lunar satellite constellations, Earth-based deep space network assets, and combinations thereof. Results show that kinematic and integrated solutions cannot be provided by the Earth-based deep space network stations. Also, the surface stationary navigation system needs to be operated as a two-way navigation system, or as a one-way navigation system with local terrain information, while integrating the position solution over a short duration of time with navigation signals being provided by a lunar satellite constellation.

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Gochoo, Munkhjargal, Syeda Amna Rizwan, Yazeed Yasin Ghadi, Ahmad Jalal, and Kibum Kim. "A Systematic Deep Learning Based Overhead Tracking and Counting System Using RGB-D Remote Cameras." Applied Sciences 11, no. 12 (June 14, 2021): 5503. http://dx.doi.org/10.3390/app11125503.

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Automatic head tracking and counting using depth imagery has various practical applications in security, logistics, queue management, space utilization and visitor counting. However, no currently available system can clearly distinguish between a human head and other objects in order to track and count people accurately. For this reason, we propose a novel system that can track people by monitoring their heads and shoulders in complex environments and also count the number of people entering and exiting the scene. Our system is split into six phases; at first, preprocessing is done by converting videos of a scene into frames and removing the background from the video frames. Second, heads are detected using Hough Circular Gradient Transform, and shoulders are detected by HOG based symmetry methods. Third, three robust features, namely, fused joint HOG-LBP, Energy based Point clouds and Fused intra-inter trajectories are extracted. Fourth, the Apriori-Association is implemented to select the best features. Fifth, deep learning is used for accurate people tracking. Finally, heads are counted using Cross-line judgment. The system was tested on three benchmark datasets: the PCDS dataset, the MICC people counting dataset and the GOTPD dataset and counting accuracy of 98.40%, 98%, and 99% respectively was achieved. Our system obtained remarkable results.

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Niell, A. E. "Geocentric Terrestrial Reference Frame Accuracy: DSN Spacecraft Tracking and VLBI/Lunar Laser Ranging." Symposium - International Astronomical Union 128 (1988): 115–20. http://dx.doi.org/10.1017/s0074180900119370.

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From a combination of 1) the location of McDonald Observatory from Lunar Laser Ranging, 2) relative station locations obtained from Very Long Baseline Interferometry (VLBI) measurements, and 3) a short tie by traditional geodesy, the geocentric coordinates of the 64 m antennas of the NASA/JPL Deep Space Network are obtained with an orientation which is related to the planetary ephemerides and to the celestial radio reference frame. Comparison with the geocentric positions of the same antennas obtained from tracking of interplanetary spacecraft shows that the two methods agree to 20 cm in distance off the spin axis and in relative longitude. The orientation difference of a 1 meter rotation about the spin axis is consistent with the error introduced into the tracking station locations due to an error in the ephemeris of Jupiter.

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