Air Surveillance

2013 ◽  
pp. 78-192 ◽  
Author(s):  
Wills ◽  
Barton ◽  
Griffiths
Keyword(s):  
Air Surveillance

scholarly journals Air surveillance by passive multisensor systems

2004 ◽  
Vol 52 (3-4) ◽  
pp. 331-343
Author(s):  
Miljko Eric ◽  
Aleksandar Kostic
Keyword(s):  
Multisensor Systems ◽  
Air Surveillance

A multi-frequency hybrid passive radar concept for medium range air surveillance

2012 ◽  
Vol 27 (10) ◽  
pp. 6-15 ◽  
Author(s):  
D. W. O'Hagan ◽  
H. Kuschel ◽  
M. Ummenhofer ◽  
J. Heckenbach ◽  
J. Schell
Keyword(s):  
Passive Radar ◽  
Medium Range ◽  
Air Surveillance

Air Surveillance Planning Approach for Large Areas

2019 ◽  
Author(s):  
Wassim Khiati ◽  
Younes Moumen ◽  
Ilham Zerrouk ◽  
Jamal Berrich ◽  
Toumi Bouchentouf
Keyword(s):  
Planning Approach ◽  
Air Surveillance

scholarly journals Weighted Kernel Filter Based Anti-Air Object Tracking for Thermal Infrared Systems

Sensors ◽  
2020 ◽  
Vol 20 (15) ◽  
pp. 4081
Author(s):  
Chuljoong Kim ◽  
Hanseok Ko
Keyword(s):  
Object Tracking ◽  
High Performance ◽  
Structural Information ◽  
Thermal Infrared ◽  
Visual Object ◽  
Surveillance Systems ◽  
Visual Object Tracking ◽  
Real Time Processing ◽  
Air Surveillance ◽  
Weighted Kernel

Visual object tracking is an important component of surveillance systems and many high-performance methods have been developed. However, these tracking methods tend to be optimized for the Red/Green/Blue (RGB) domain and are thus not suitable for use with the infrared (IR) domain. To overcome this disadvantage, many researchers have constructed datasets for IR analysis, including those developed for The Thermal Infrared Visual Object Tracking (VOT-TIR) challenges. As a consequence, many state-of-the-art trackers for the IR domain have been proposed, but there remains a need for reliable IR-based trackers for anti-air surveillance systems, including the construction of a new IR dataset for this purpose. In this paper, we collect various anti-air thermal-wave IR (TIR) images from an electro-optical surveillance system to create a new dataset. We also present a framework based on an end-to-end convolutional neural network that learns object tracking in the IR domain for anti-air targets such as unmanned aerial vehicles (UAVs) and drones. More specifically, we adopt a Siamese network for feature extraction and three region proposal networks for the classification and regression branches. In the inference phase, the proposed network is formulated as a detection-by-tracking method, and kernel filters for the template branch that are continuously updated for every frame are introduced. The proposed network is able to learn robust structural information for the targets during offline training, and the kernel filters can robustly track the targets, demonstrating enhanced performance. Experimental results from the new IR dataset reveal that the proposed method achieves outstanding performance, with a real-time processing speed of 40 frames per second.

scholarly journals A Grey Wolf Optimization-based Track-Before-Detect Method for Maneuvering Extended Target Detection and Tracking

Sensors ◽  
2019 ◽  
Vol 19 (7) ◽  
pp. 1577 ◽  
Author(s):  
Bo Yan ◽  
Xu Yang Zhao ◽  
Na Xu ◽  
Yu Chen ◽  
Wen Bo Zhao
Keyword(s):  
Target Detection ◽  
Synthetic Data ◽  
Optimal Solution ◽  
Real Data ◽  
Grey Wolf ◽  
Grey Wolf Optimization ◽  
Detection And Tracking ◽  
Extended Target ◽  
Air Surveillance ◽  
Track Before Detect

A grey wolf optimization-based track-before-detect (GWO-TBD) method is developed for extended target detection and tracking. The aim of the GWO-TBD is tracking weak and maneuvering extended targets in a cluttered environment using the measurement points of an air surveillance radar. The optimal solution is the trajectory constituted by the points of an extended target. At the beginning of the GWO-TBD, the measurements of each scan are clustered into alternative sets. Secondly, closely sets are associated for tracklets. Each tracklet equals a candidate solution. Thirdly, the tracklets are further associated iteratively to find a better solution. An improved GWO algorithm is developed in the iteration for removal of unappreciated solution and acceleration of convergence. After the iteration of several generations, the optimal solution can be achieved, i.e. trajectory of an extended target. Both the real data and synthetic data are performed with the GWO-TBD and several existing algorithms in this work. Result infers that the GWO-TBD is superior to the others in detecting and tracking maneuvering targets. Meanwhile, much less prior information is necessary in the GWO-TBD. It makes the approach is engineering friendly.

Sign in / Sign up

Export Citation Format

Share Document