System Software Approach to Extend The Engagement Capability of the IIR Guided Missile System From Sea to the Occluded Land Targets
- 주제(키워드) IIR guided missile , Land target , Occlusion , Dynamic Feature , Seeker algorithm , system software
- 발행기관 아주대학교
- 지도교수 Euiwhan Kim
- 발행년도 2019
- 학위수여년월 2019. 8
- 학위명 석사
- 학과 및 전공 일반대학원 시스템공학과
- 실제URI http://www.dcollection.net/handler/ajou/000000029021
- 본문언어 영어
- 저작권 아주대학교 논문은 저작권에 의해 보호받습니다.
초록/요약
The usage of IIR (Imaging Infrared) missile guidance systems keeps increasing due to their operational usefulness and technical capability improvement. Some of those systems improvement comes from the software capability enhancement. In this study, the approach to extend the existing IIR Guided missile system developed for the sea targets to the land targets was conducted. The land target engagement usually demands to overcome the more adverse operational environment coming from the complex ground background and occlusion. This research is important to IIR missile developers who focus on overcoming operational environment problems, specifically to the persons who work for IIR seeker software or automatic target acquisition (ATA) algorithm enhancement. Through exploring IIR guided missile CONOPS, architecture and its operational environment, several environment problems were identified that could limits IIR seeker missile detection and tracking process against the land targets. SE process was used as the main qualitative approach to analyze the operational environment, system need and to derive the additional requirements. From this analysis the occlusion problem was selected as the most important technical issue to overcome the difficulty of land target engagement. In order to solve the occlusion problem, software enhancement approach was chosen to satisfy the additional requirements. The dynamic feature enhancement of the seeker is proposed as a solution to reduce the tracking failure from the occluded targets in nonhomogeneous environment. MATLAB tool was used as a quantitative approach for SW algorithms simulation and testing. Tracker simulation results and failure rate was used as the evaluation criteria. Several trackers results on several IR video sequences were compared. The enhanced trackers with Kalman filter always achieved the lowest tracking failure rate by up to 93% compared to all other trackers, and better tracking performance by cumulatively ˃ 4 % compared to original SRDCF Tracker. Enhancing seeker dynamic feature allowed IR seeker system to achieve significantly more accurate tracking performance and better performance in keeping the lock-on target within its FOV even when the occlusion occurred.
more목차
Acknowledgement: I
Abbreviation List II
Abstract III
Table of Contents V
List of Figures: VII
List of Tables: VIII
1. Introduction: 1
1.1. Thesis Background 1
1.2. Thesis Purpose and Objectives: 2
1.3. Thesis Problem Statement 2
1.4. Thesis Scope: 3
1.5. Thesis Composition 3
1.6. Methodology 4
1.7. Limitation of thesis 4
2. Review of Literature: 5
2.1. Thermal Infrared & Visual Object Tracking Challenge: 5
3. IIR Guided Missile against Sea Targets 7
3.1. IR/IIR Guided Missile Overview: 7
3.2. IIR Guided Missile Operational Capability: 8
3.2.1 Mission Analysis 8
3.2.2 Concepts of Operations (CONOPS) & Operational Sequence: 9
3.3. IIR Guided Missile Physical Hierarchy 11
4. Expansion To Land Threat: 13
4.1 Needs Identification: 13
4.2. Land Target CONOPS: 14
4.3. Threat Operational Environment Analysis: 15
4.3.1 Sea Vs. Land Target Environments: 15
4.3.2 Problem Identification: 17
5. Additional Requirements for Land Targets: 18
5.1. Overview: 18
5.2. IIR Requirements & Specification Derivation: 18
5.2.1 Stakeholders/Users Requirements: 19
5.2.2 Sea Vs. Land target IIR Seeker Specifications: 21
5.3. Software Algorithm Improvement as a Feasible Solution: 23
6. IIR Seeker Algorithms Dynamic Improvement: 25
6.1. Analysis of Existing Tracking Limitation: 25
6.1.1 Scenario-Driven Trackers Testing: 26
6.1.2 SRDCF Tracking-by-Detection: 31
6.2. Dynamic Feature Enhancement by KALMAN Filtering: 31
6.2.1 Intro to KALMAN Filter: 32
6.2.2 Upgrading SRDCF Tracker with KALMAN Filter: 34
A. MATLAB Algorithm flowchart: 34
B. Mathematical SW implementation: 35
C. MATLAB Algorithm Pseudo Code & Parameters: 36
7. Software Experimental Analysis: 38
7.1. Kalman filtering Tracking Improvement Results 38
7.2. Measure of Effectiveness & Evaluation Metric: 38
7.3. Trackers comparison results 39
8. Conclusion & Recommendations: 42
9. REFERENCES: 43
