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Locating, Classifying and Countering Agile Land Vehicles: With Applications to Command Architectures 1st ed. 2015 [Kõva köide]

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  • Formaat: Hardback, 290 pages, kõrgus x laius: 235x155 mm, kaal: 6348 g, 161 Illustrations, color; 4 Illustrations, black and white; XIV, 290 p. 165 illus., 161 illus. in color., 1 Hardback
  • Ilmumisaeg: 07-Aug-2015
  • Kirjastus: Springer International Publishing AG
  • ISBN-10: 3319194305
  • ISBN-13: 9783319194301
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Locating, Classifying and Countering Agile Land Vehicles: With Applications to Command Architectures 1st ed. 2015Suurem pilt
  • Formaat: Hardback, 290 pages, kõrgus x laius: 235x155 mm, kaal: 6348 g, 161 Illustrations, color; 4 Illustrations, black and white; XIV, 290 p. 165 illus., 161 illus. in color., 1 Hardback
  • Ilmumisaeg: 07-Aug-2015
  • Kirjastus: Springer International Publishing AG
  • ISBN-10: 3319194305
  • ISBN-13: 9783319194301
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9783319194301.html
Märksõnad:
This book examines real-time target tracking and identification algorithms with a focus on tracking an agile target. The authors look at several problems in which the tradeoff of accuracy and confidence must be made. These issues are explored within the context of specific tracking scenarios chosen to illustrate the tradeoffs in a simple and direct manner. The text covers the Gaussian wavelet estimator (GWE) which has a flexible architecture that is able to fuse uncommon sensor combinations with non-temporal structural constraints.

A Model for Tracking and Classification.- Target Location Using the Extended Kalman Filter.- Tracking an Agile Target.- Intercept and Target Prediction.- Classification and Target Tempo.- Assurance Regions on a Road Grid.- ActionWindows with Resource Limits.- Serendipitous Events in Tracking and Classification.- Deceiving a Classifier.
Symbols and Notational Conventions xi
1 A Model for Tracking and Classification
1(36)
1.1 Introduction to the Problem
1(7)
1.2 A Hybrid State Model of a Maneuvering Target
8(11)
1.2.1 The Modal State
8(5)
1.2.2 Kinematic State
13(6)
1.3 The Measurement Process
19(3)
1.3.1 Regime-State Measurement
19(2)
1.3.2 Kinematic-State Measurement
21(1)
1.4 State Estimates Using the Gaussian-Sum Distribution
22(3)
1.5 Assurance Regions
25(5)
1.6 Tracker Architecture
30(3)
1.6.1 The Conditional Distribution of the Target State
30(2)
1.6.2 The Projected Distribution
32(1)
1.7 Organization of the Book
33(1)
1.8 Conclusion
34(3)
2 Target Location Using the Extended Kalman Filter
37(30)
2.1 Introduction
37(3)
2.2 The Extended Kalman Filter
40(2)
2.3 Examples of the EKF-Tracker
42(5)
2.3.1 The Cooperative Target
42(3)
2.3.2 The Uncooperative Target
45(2)
2.4 Range-Only Tracking
47(10)
2.4.1 The EKF-Tracker on a Roadway
47(4)
2.4.2 GDOP and Tracker Accuracy
51(1)
2.4.3 The EKF-Tracker Including the North Node
52(2)
2.4.4 Map-Enhanced Tracking
54(3)
2.5 Tracking in the Presence of Spoofing
57(7)
2.5.1 Registration Errors
57(1)
2.5.2 Flagging Registration Errors Using Assurance Regions
58(5)
2.5.3 Map-Enhanced Flagging
63(1)
2.6 Conclusions
64(3)
3 Tracking an Agile Target
67(24)
3.1 Introduction
67(2)
3.2 Multi-regime Engagement Model
69(4)
3.2.1 Kinematic Model
69(1)
3.2.2 Modal Model
69(2)
3.2.3 Kinematic Measurement
71(1)
3.2.4 Regime Measurement
71(2)
3.3 The Uni-Model Tracker
73(5)
3.3.1 The Engagement
73(1)
3.3.2 The EKF-Tracker
74(4)
3.4 The Multi-model Tracker
78(10)
3.4.1 The Engagement
78(2)
3.4.2 The GWE Algorithm
80(2)
3.4.3 The GWE-Tracker
82(6)
3.5 Conclusion
88(3)
4 Intercept and Target Prediction
91(22)
4.1 Introduction
91(2)
4.2 The EKF
93(5)
4.2.1 EKF-Tracker
93(1)
4.2.2 EKF-Predictor
94(4)
4.3 The y[ k]-GWE
98(6)
4.3.1 y[ k]-GWE-Tracker
98(2)
4.3.2 y[ k]-GWE-Predictor
100(4)
4.4 The g[ k]-GWE
104(5)
4.4.1 g[ k]-GWE-Tracker
104(3)
4.4.2 g[ k]-GWE-Predictor
107(2)
4.5 Conclusion
109(4)
5 Classification and Target Tempo
113(28)
5.1 Introduction
113(5)
5.2 The Engagement
118(2)
5.3 g[ k]-Tracking: Markov or Gamma
120(4)
5.4 Target Classification
124(12)
5.4.1 Friendly or Hostile
124(2)
5.4.2 Common Lifetimes and Regimes
126(8)
5.4.3 Distinctive Kinematics, Range-Bearing Sensors
134(2)
5.5 Conclusion
136(5)
6 Assurance Regions on a Road Grid
141(36)
6.1 Introduction
141(2)
6.2 The Engagement
143(5)
6.2.1 Kinematic Model
143(3)
6.2.2 Measurements
146(2)
6.3 The EKF
148(9)
6.3.1 Nominal EKF
148(2)
6.3.2 Pseudo-Noise Augmentation
150(2)
6.3.3 Tracker Contrasts
152(5)
6.4 The Map-Enhanced GWE
157(16)
6.4.1 Algorithm Overview
157(6)
6.4.2 The GWE-Tracker
163(10)
6.5 Conclusions
173(4)
7 Action Windows with Resource Limits
177(24)
7.1 Introduction
177(3)
7.2 Action Windows with a Grid Map
180(10)
7.2.1 The Engagement
180(2)
7.2.2 EKF-Illuminator
182(3)
7.2.3 GWE-Illuminator
185(5)
7.3 Ballistic Fire Control
190(8)
7.3.1 The Engagement
190(1)
7.3.2 EKF Fire Control
191(2)
7.3.3 GWE Fire Control
193(5)
7.4 Conclusions
198(3)
8 Serendipitous Events in Tracking and Classification
201(34)
8.1 System Specifications
201(2)
8.2 The Visible Target
203(5)
8.2.1 The Engagement Model
203(1)
8.2.2 Omnipresent EKF
204(4)
8.3 The Veiled Target
208(11)
8.3.1 The Engagement
208(1)
8.3.2 Blinded EKF
209(2)
8.3.3 GWE
211(6)
8.3.4 Algorithm Contrasts
217(2)
8.4 The Veiled Target with More Ambiguous Para-Measurements
219(14)
8.4.1 The Engagement
219(1)
8.4.2 Blinded EKF
220(2)
8.4.3 GWE
222(11)
8.5 Conclusions
233(2)
9 Deceiving a Classifier
235(18)
9.1 Introduction
235(2)
9.2 The Engagement
237(1)
9.3 An EKF Classifier
238(4)
9.3.1 No Spoofing
238(2)
9.3.2 Spoofing
240(1)
9.3.3 A Beacon Assist
241(1)
9.4 A Hybrid Classifier
242(7)
9.4.1 Spoofing
242(5)
9.4.2 A Beacon Assist
247(2)
9.5 Countermeasure Effectiveness
249(2)
9.6 Conclusions
251(2)
10 The Gaussian Wavelet Estimator
253(36)
10.1 GWE Details
253(13)
10.1.1 Engagement Structure: Kinematics
253(2)
10.1.2 Engagement Structure: Sensor Architecture
255(1)
10.1.3 Hybrid Estimation
255(11)
10.2 A Single-Step Example of the GWE
266(11)
10.2.1 Introduction
266(1)
10.2.2 Kinematic Extrapolation
267(1)
10.2.3 The Measurements
268(1)
10.2.4 The GWE Update
269(4)
10.2.5 The Location Estimate
273(3)
10.2.6 Reinitialization
276(1)
10.3 The Map-Enhanced GWE
277(12)
10.3.1 The (i44)-Local Estimator
278(11)
References 289
Dave Sworder is a Professor of ECE at UCSD John Boyd is the Chief Scientist, Information Systems for Cubic Defense Applications.