Airborne Tracking of Small Ground and Maritime Targets Under Realistic Conditions
Airborne Tracking of Small Ground and Maritime Targets Under Realistic Conditions
批准号:
535810-2018
负责人:
Kirubarajan, Thia
金额:
$26.23万
依托单位:
依托单位国家:
加拿大
项目类别:
Collaborative Research and Development Grants
财政年份:
2021
资助国家:
加拿大
项目状态:
已结题
起止时间:
2021-01-01 至 2022-12-31
中文摘要
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英文摘要
The monitoring of moving objects or persons on the ground using different types of sensors on one or more airborne platforms has many applications in safety & security, search & rescue, law enforcement, intelligent transportation, shipping and agriculture. The sensors on these platforms could be electro-optical/infrared (EO/IR), laser, radar or acoustic. The moving targets of interest could be vehicles, ships, boats, people or even submarines. The targets of interest could be evolving with different motion characteristics (e.g., constant velocity, maneuvering) under different environmental conditions (e.g., low light, rain, heavy traffic). The primary objectives in these airborne surveillance systems are to 1) detect different types of moving objects of interest, 2) track and classify the detected objects, and 3) analyze their behavior under different environmental conditions using the different sensors and limited computing resources onboard. The combination of different types of platforms, sensors, targets and environmental conditions, poses a challenging multiplatform-multisensor-multitarget tracking problem with many conflicting accuracy vs. speed vs. complexity requirements. The objective of this proposal is to address these conflicts in airborne surveillance systems to improve the state-of-the-art in airborne multiplatform-multisensor-multitarget tracking systems. Motivated by the extensive experience of the industry partner in airborne surveillance and their observations from fielded systems, we propose to develop accurate target tracking and fusion algorithms that factor in real-world challenges (e.g., small target sizes, environmental conditions, limited computational resources). While we aim to solve specific challenges in airborne surveillance systems, the results of this research can be applied to intelligent transportation, smart agriculture and law enforcement as well. In addition, the proposed research will train a number of highly qualified personnel in key areas of importance to Canada.
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项目类别:Discovery Grants Program - Individual
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资助金额:$4.23万
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项目类别:DND/NSERC Discovery Grant Supplement
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资助金额:$2.91万
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财政年份:2019
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Optimal Layered Resource Management and Data Processing for Threat Detection in Urban Environments
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批准号:538404-2018
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项目类别:Collaborative Research and Development Grants
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负责人:Kirubarajan, Thia
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资助金额:$5.97万
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依托单位:
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-
批准号:500634-2016
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项目类别:Department of National Defence / NSERC Research Partnership
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资助金额:$5.97万
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财政年份:2018
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负责人:Kirubarajan, Thia
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依托单位:
Robust State Estimation in Uncertain Environments Using Point Process Models
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批准号:DGDND-2017-00082
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项目类别:DND/NSERC Discovery Grant Supplement
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资助金额:$2.91万
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财政年份:2018
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负责人:Kirubarajan, Thia
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依托单位:
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依托单位:
Robust State Estimation in Uncertain Environments Using Point Process Models
-
批准号:507969-2017
-
项目类别:Discovery Grants Program - Accelerator Supplements
-
资助金额:$2.91万
-
财政年份:2018
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负责人:Kirubarajan, Thia
-
依托单位:
Multi-level adaptive systems and algorithms for agile and opportunistic sensing
-
批准号:501206-2016
-
项目类别:Department of National Defence / NSERC Research Partnership
-
资助金额:$13.11万
-
财政年份:2018
-
负责人:Kirubarajan, Thia
-
依托单位:
Robust State Estimation in Uncertain Environments Using Point Process Models
-
批准号:RGPIN-2017-05365
-
项目类别:Discovery Grants Program - Individual
-
资助金额:$4.23万
-
财政年份:2018
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负责人:Kirubarajan, Thia
-
依托单位:
Multi-level adaptive systems and algorithms for agile and opportunistic sensing
-
批准号:501206-2016
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项目类别:Department of National Defence / NSERC Research Partnership
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负责人:Kirubarajan, Thia
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依托单位:
国内基金
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