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Über dieses Buch

This book describes grouping detection and initiation; group initiation algorithm based on geometry center; data association and track continuity; as well as separate-detection and situation cognition for group-target. It specifies the tracking of the target in different quantities and densities. At the same time, it integrates cognition into the application.

Group-target Tracking is designed as a book for advanced-level students and researchers in the area of radar systems, information fusion of multi-sensors and electronic countermeasures. It is also a valuable reference resource for professionals working in this field.

Inhaltsverzeichnis

Frontmatter

Chapter 1. Introduction

Target tracking is an essential issue in the real world both for the survival of animals and for the life of human beings. For instance, to capture a prey, a lion must fix its eyes all long on the target. In military and civil fields such as ballistic missile defense, air defense early-warning, sea and ground battlefield surveillance and air traffic control, tracking targets reliably and precisely is pursued all the time by sensor tracking systems like radar.
Wen-dong Geng, Yuan-qin Wang, Zheng-hong Dong

Chapter 2. Preliminaries

Modern radar systems include radar signal processors, data collectors, and radar data processors, besides traditional antennas, transmitters, receivers, and monitors. Signal processors are used mainly for target signal detection and for restraining the irrelevant signals of noise wave disturbances from earth or ocean, multi-path effects, atmospheric environment, universal noises, and man-made disturbances. The video output signals after signal processing are compared with certain detection threshold. If the signals are beyond the detection threshold, we “discover” targets.
Wen-dong Geng, Yuan-qin Wang, Zheng-hong Dong

Chapter 3. Grouping Detection and Group Initiation of Group-Target

Multi-targets tracking data association can generate three areas: correctly correlated area, unstable correlated area, and mistaken correlated area. Any radar cannot choose its tracking environment and target distance, but can enhance scan frequency and improve data association algorithm, etc. to decrease unstable correlated area and mistaken correlated area.
Wen-dong Geng, Yuan-qin Wang, Zheng-hong Dong

Chapter 4. Single-Group-Target Data Association and Track Maintenance

As we know, the nearest-neighboring algorithm and probability association algorithm in Bayesian algorithm are very successful two data association algorithms. The nearest-neighboring method, as one of the comparatively effective data association methods, regards the statistically “nearest” observation data that fall inside association gates as the correlated observation data of the target under tracking. The probability data association algorithm is an all-neighboring method. It comprehensively considers all the measurements inside tracking gate, computes the equivalent measurement according to the probabilistic weighted coefficients of each measurement, and updates target state using equivalent measurement.
Wen-dong Geng, Yuan-qin Wang, Zheng-hong Dong

Chapter 5. Multi-Group-Target Data Association and Track Maintenance

Multi-target tracking data association is a difficulty and also a key problem to be solved firstly in multi-target tracking. When tracking single target under situation of returns in density, the continuous occurrence of the measurement from near targets in the tracking gates causes continuous disturbance.
Wen-dong Geng, Yuan-qin Wang, Zheng-hong Dong

Chapter 6. Detection of Group-Target Combination and Splitting and Situation Cognition

Purely from the perspective of target tracking, we have completed the pretreatment of group-target measurement, group-target track initiation, single group-target data correlating and track maintenance, and multi-group-target data correlating and track maintenance.
Wen-dong Geng, Yuan-qin Wang, Zheng-hong Dong

Chapter 7. Simulations of Group-Target Tracking Algorithms

We discussed the background, meaning, present situation, content, and idea of group-target tracking algorithms in Chap. 1, some preliminaries in Chap. 2, group splitting detection and group initiation in Chap. 3, single group-target data association and track maintenance in Chap. 4, multi-group-target data association and track maintenance in Chap. 5, and group-target combination and splitting detection and group termination in Chap. 6, respectively. Although the abovementioned algorithm went through strict mathematical reasoning, their correctness and validity still need to be proved by simulations. Simulation validation is parallel to the study of the algorithms.
Wen-dong Geng, Yuan-qin Wang, Zheng-hong Dong

Backmatter

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