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At least 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Development and Field Test Results of Distributed Ground Sensor Fusion Based Object Tracking

Autonomous operations are a crucial aspect in the context of Advanced Air Mobility and other emerging aviation markets. In order to enable this autonomy, an accurate and detailed understanding of the positions of the various vehicles in the air is necessary. Full localization independent of on-board sensors makes the system suitable for noncooperative vehicles. This paper focuses on the object tracking part that relies on distributed ground-based RF and other sensor fusion, considering specific properties and limitations of different sensor types. Results show satisfactory performance in nominal scenarios with full coverage for some sensor types, but RF signals are challenging because of their nature. This paper includes the results from simulations as well as field tests to support the observations and conclusions.

sensor fusion↗
NASA NTRS2023

Distributed Ground Sensor Fusion Based Object Tracking for Autonomous Advanced Air Mobility Operations

Autonomous operations are a crucial aspect in the context of Advanced Air Mobility and other emerging aviation markets. In order to enable this autonomy, an accurate and detailed understanding of the positions of the various vehicles in the air is necessary. Full localization independent of on-board sensors makes the system suitable for noncooperative vehicles. This paper focuses on the object tracking part that relies on distributed ground-based sensor fusion, considering specific properties and limitations of different sensor types. Results show satisfactory performance in nominal scenarios with full coverage. Dropouts of individual sensors affect the accuracy of the tracking results, which agrees with expectations for partial coverage, when full localization is not achievable anymore. Finally, a study is performed to identify which parameters have the largest impact on the fit error.

Autonomous↗
DOE OSTI2022

Automated Vehicle Multi-Object Tracking at Scale with CAN

Millions of vehicles are on the road with RADAR sensors in use for adaptive cruise control (ACC), and RADAR sen- sors are not tracking all of the objects in the field of view. This work shows a work-in-progress tool to improve track- ing from RADAR and controller area network (CAN) which should be vitally useful for safety of transportation systems and automated vehicle development. The CAN data provides object detections, but there is a lingering data association problem. The contribution of this work in progress is the solution to the data association problem by posing the data association as a minimum cost network flow problem, and doing it at low cost with an eye toward scalable CPS research.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Descriptor: Infrastructure Perception and Control: Multi-Sensor Object Tracking Dataset (IPC-MSOT)

Traffic intersections are crucial and challenging nodes in transportation networks where multiple lanes of vehicles and pedestrians converge. Traffic accidents often occur at traffic intersections, including a large proportion of traffic fatalities and about one-half of all traffic injuries in the United States. Object detection data were collected in 2024 across three intersections in Colorado Springs, CO, USA, over the course of multiple days and various times to induce a heterogeneous mix of traffic conditions and behaviors. The purpose of the data collection exercises was to learn various attributes about infrastructure sensors and to build a repository of high-resolution, object-level data that can be used for research and development (e.g., to develop multisensor data fusion algorithms). The Infrastructure Perception and Control:Multi-Sensor Object tracking (IPC-MSOT) dataset was collected as part of the U.S. Department of Transportation's Strengthening Mobility and Revolutionizing Transportation (SMART) project, where the city of Colorado Springs, Colorado, and the National Renewable Energy Laboratory collaborated to collect object-level trajectory data from road users using multiple types of infrastructure sensors deployed at different intersections. This dataset allows for testing of late-stage sensor fusion algorithms and their ability to ingest multimodal sensor data, and it can be utilized by traffic engineers to design and evaluate trajectory-based signal control strategies.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRS1988

Laser agile illumination for object tracking and classification - Feasibility study

The 'agile illumination' concept for discrimination between ICBM warheads and decoys involves a two-aperture illumination with coherent light, diffraction of light by propagation, and a resulting interference pattern on the object surface. A scanning two-beam interference pattern illuminates one object at a time; depending on the shape, momentum, spinning, and tumbling characteristics of the interrogated object, different temporal signals will be obtained for different classes of objects.

Scholl, Marija S.↗
NASA NTRS1995

Three-dimensional object tracking system and method employing plural sensors and plural processors for performing parallel processing

A multiple sensor/multiple processor 3-D tracking system includes multiple sensor (camera) and DSP combinations and a controller connected so as to create a parallel computer from the multiple DSPs. The camera/DSP combinations and the controller are connected together to form a ring. For 3-D position measurement and tracking applications, each camera/DSP combination is programmed with an identical program to function as a node in a true parallel computer, and the transformation equations are solved in a distributed fashion using the entire parallel machine. The distributed processor processes the camera x and y point measurements in a distributed and parallel fashion to yield a solution to the coordinate transformation equations much more quickly than is possible with multiple camera/DSP combinations and a single processor.

Welch, Sharon S.↗
NASA NTRS2005

Methods and apparatus for extraction and tracking of objects from multi-dimensional sequence data

An object tracking technique is provided which, given: (i) a potentially large data set; (ii) a set of dimensions along which the data has been ordered; and (iii) a set of functions for measuring the similarity between data elements, a set of objects are produced. Each of these objects is defined by a list of data elements. Each of the data elements on this list contains the probability that the data element is part of the object. The method produces these lists via an adaptive, knowledge-based search function which directs the search for high-probability data elements. This serves to reduce the number of data element combinations evaluated while preserving the most flexibility in defining the associations of data elements which comprise an object.

Hill, Matthew L.↗
NASA NTRS2008

Methods and apparatus for extraction and tracking of objects from multi-dimensional sequence data

An object tracking technique is provided which, given: (i) a potentially large data set; (ii) a set of dimensions along which the data has been ordered; and (iii) a set of functions for measuring the similarity between data elements, a set of objects are produced. Each of these objects is defined by a list of data elements. Each of the data elements on this list contains the probability that the data element is part of the object. The method produces these lists via an adaptive, knowledge-based search function which directs the search for high-probability data elements. This serves to reduce the number of data element combinations evaluated while preserving the most flexibility in defining the associations of data elements which comprise an object.

Hill, Matthew L.↗
NASA NTRSDate not supplied

The Viability of See and Avoid for Urban Air Mobility Operations

Urban Air Mobility (UAM) is an emerging aviation concept that could supplement today’s ground and air transportation systems. For UAM, it is generally assumed that the private sector will manage separation and not rely on the Federal Aviation Administration air traffic control system. To date, discussions of initial operations focus on using the visual abilities of the pilot to “see and avoid” (SAA) other aircraft. Decades of research on SAA has demonstrated that it is inadequate for reliable detection of aircraft that might pose a collision risk. The literature on multi-object tracking is also reviewed for findings on how well humans can visually track objects. The research shows that humans have limited resources for tracking and that this may be affected by object characteristics and cognitive skills. The conclusion is that SAA is a risky method for avoiding midair collisions. It is recommended that flight deck displays and automated collision avoidance systems be implemented for all UAM aircraft at the outset of their introduction.

urban air mobility↗
NASA NTRSDate not supplied

The Viability of See-and-Avoid for Midair Collision Avoidance for UAM

Urban Air Mobility (UAM) is an emerging aviation concept that could supplement today’s ground and air transportation systems. For UAM, it is generally assumed that the private sector will manage separation and not rely on the Federal Aviation Administration air traffic control system. To date, discussions of initial operations focus on using the visual abilities of the pilot to “see and avoid” (SAA) other aircraft. Decades of research on SAA has demonstrated that it is inadequate for reliable detection of aircraft that might pose a collision risk. The literature on multi-object tracking is also reviewed for findings on how well humans can visually track objects. The research shows that humans have limited resources for tracking and that this may be affected by object characteristics and cognitive skills. The conclusion is that SAA is a risky method for avoiding midair collisions. It is recommended that flight deck displays and automated collision avoidance systems be implemented for all UAM aircraft at the outset of their introduction.

urban air mobility↗
NASA NTRSDate not supplied

The Viability of See-and-Avoid for Midair Collision Avoidance for Urban Air Mobility (UAM)

Urban Air Mobility (UAM) is an emerging aviation concept that could supplement today’s ground and air transportation systems. For UAM, it is generally assumed that the private sector will manage separation and not rely on the Federal Aviation Administration air traffic control system. To date, discussions of initial operations focus on using the visual abilities of the pilot to “see and avoid” (SAA) other aircraft. Decades of research on SAA has demonstrated that it is inadequate for reliable detection of aircraft that might pose a collision risk. The literature on multi-object tracking is also reviewed for findings on how well humans can visually track objects. The research shows that humans have limited resources for tracking and that this may be affected by object characteristics and cognitive skills. The conclusion is that SAA is a risky method for avoiding midair collisions. It is recommended that flight deck displays and automated collision avoidance systems be implemented for all UAM aircraft at the outset of their introduction.

urban air mobility↗
NASA NTRS2023

The Viability of See-and-Avoid for Urban Air Mobility Operations

Urban Air Mobility (UAM) is an emerging aviation concept that could supplement today’s ground and air transportation systems. For UAM, it is generally assumed that the private sector will manage separation and not rely on the Federal Aviation Administration air traffic control system. To date, discussions of initial operations focus on using the visual abilities of the pilot to “see and avoid” (SAA) other aircraft. Decades of research on SAA has demonstrated that it is inadequate for reliable detection of aircraft that might pose a collision risk. The literature on multi-object tracking is also reviewed for findings on how well humans can visually track objects. The research shows that humans have limited resources for tracking and that this may be affected by object characteristics and cognitive skills. The conclusion is that SAA is a risky method for avoiding midair collisions. It is recommended that flight deck displays and automated collision avoidance systems be implemented for all UAM aircraft at the outset of their introduction.

urban air mobility↗
NASA NTRS2010

Visual object recognition and tracking

This invention describes a method for identifying and tracking an object from two-dimensional data pictorially representing said object by an object-tracking system through processing said two-dimensional data using at least one tracker-identifier belonging to the object-tracking system for providing an output signal containing: a) a type of the object, and/or b) a position or an orientation of the object in three-dimensions, and/or c) an articulation or a shape change of said object in said three dimensions.

Chang, Chu-Yin↗
NASA NTRS2004

Growth in the Number of SSN Tracked Orbital Objects

The number of objects in earth orbit tracked by the US Space Surveillance Network (SSN) has experienced unprecedented growth since March, 2003. Approximately 2000 orbiting objects have been added to the "Analyst list" of tracked objects. This growth is primarily due to the resumption of full power/full time operation of the AN/FPS-108 Cobra Dane radar located on Shemya Island, AK. Cobra Dane is an L-band (23-cm wavelength) phased array radar which first became operational in 1977. Cobra Dane was a "Collateral Sensor" in the SSN until 1994 when its communication link with the Space Control Center (SCC) was closed. NASA and the Air Force conducted tests in 1999 using Cobra Dane to detect and track small debris. These tests confirmed that the radar was capable of detecting and maintaining orbits on objects as small as 5-cm diameter. Subsequently, Cobra Dane was reconnected to the SSN and resumed full power/full time space surveillance operations on March 4, 2003. This paper will examine the new data and its implications to the understanding of the orbital debris environment and orbital safety.

Stansbery, Eugene G.↗
NASA NTRS2018

USA Space Debris Environment, Operations, and Research Updates

Space Missions in 2017 Earth Satellite Population Collision Avoidance Maneuvers Post mission Disposal of U.S.A. Spacecraft Space Situational Awareness (SSA) and the Space Debris Sensor (SDS) A total of 86 space launches placed more than 400 spacecraft into Earth orbits during 2017, following the trend of increase over the past decade NASA has established conjunction assessment processes for its human spaceflight and uncrewed spacecraft to avoid accidental collisions with objects tracked by the U.S. Space Surveillance Network - NASA also assists other U.S. government spacecraft owners with conjunction assessments and subsequent maneuvers The ISS has conducted 25 debris collision avoidance maneuvers since 1999 - None in 2016-2017, but an ISS visiting vehicle had one collision avoidance maneuver in 2017 During 2017 NASA executed or assisted in the execution of 21 collision avoidance maneuvers by uncrewed spacecraft - Four maneuvers were conducted to avoid debris from Fengyun-1C - Two maneuvers were conducted to avoid debris from the collision of Cosmos 2251 and Iridium 33 - One maneuver was conducted to avoid the ISS NASA has established conjunction assessment processes for its human spaceflight and uncrewed spacecraft to avoid accidental collisions with objects tracked by the U.S. Space Surveillance Network - NASA also assists other U.S. government spacecraft owners with conjunction assessments and subsequent maneuvers The ISS has conducted 25 debris collision avoidance maneuvers since 1999 - None in 2016-2017, but an ISS visiting vehicle had one collision avoidance maneuver in 2017 During 2017 NASA executed or assisted in the execution of 21 collision avoidance maneuvers by uncrewed spacecraft - Four maneuvers were conducted to avoid debris from Fengyun-1C - Two maneuvers were conducted to avoid debris from the collision of Cosmos 2251 and Iridium 33 The 2014-15 NASA Engineering and Safety Center (NESC) study on the micrometeoroid and orbital debris (MMOD) assessment for the Joint Polar Satellite System (JPSS) provided the following findings - Millimeter-sized orbital debris pose the highest penetration risk to most operational spacecraft in LEO - The most effective means to collect direct measurement data on millimetersized debris above 600 km altitude is to conduct in situ measurements - There is currently no in situ data on such small debris above 600 km altitude Since the orbital debris population follows a power-law size distribution, there are many more millimeter-sized debris than the large tracked objects - Current conjunction assessments and collision avoidance maneuvers against the tracked objects (which are typically 10 cm and larger) only address a small fraction (<1%) of the mission-ending risk from orbital debris To address the millimeter-sized debris data gap above 600 km, NASA has recently developed an innovative in situ measurement instrument - the Space Debris Sensor (SDS) - One maneuver was conducted to avoid the ISS

Liou, J.-C.↗
NASA NTRS1988

Optical Recognition And Tracking Of Objects

Separate objects moving independently tracked simultaneously. System uses coherent optical techniques to obtain correlation between each object and reference image. Moving objects monitored by charge-coupled-device television camera, output fed to liquid-crystal television (LCTV) display. Acting as spatial light modulator, LCTV impresses images of moving objects on collimated laser beam. Beam spatially low-pass filtered to remove high-spatial-frequency television grid pattern.

Chao, Tien-Hsin↗