Radar as a Key to Global Aeroecology

2022 ◽  
pp. 482-505
Author(s):  
Alexey Noskov
Keyword(s):  
Big Data ◽  
Pest Management ◽  
Target Detection ◽  
Technical Progress ◽  
Continuous Wave ◽  
Insect Species ◽  
Detection And Tracking ◽  
Insect Biodiversity ◽  
Wave Radar ◽  
History Of

Open, systematic, and global approaches are needed to address the challenges of aeroconservation and pest management. Recent technical progress enables deeper investigation and understanding of aeroecology. Radar plays a central role in flying species monitoring in the global scope. The technology provides various ways of target detection and tracking, working for multiple ranges and different visibility. The existing technology allows deploying global monitoring of avian and insect species. This work discusses the essentials of the technology and the history of its application for bird and insect detection. The author describes the development of the topic according to the main groups of radar approaches: pulsed sets, vertical-looking solutions, harmonic systems, and efficient frequency modulated continuous wave radar. Advances in big data processing, robotics, computation, and communications enable practitioners to combine the discussed radar solutions aiming at global avian and insect biodiversity monitoring and negative human impact systematic estimation.

Target detection and tracking with a high frequency ground wave radar

1994 ◽  
Vol 19 (4) ◽  
pp. 540-548 ◽  
Author(s):  
R. Khan ◽  
B. Gamberg ◽  
D. Power ◽  
J. Walsh ◽  
B. Dawe ◽  
...  
Keyword(s):  
Target Detection ◽  
High Frequency ◽  
Detection And Tracking ◽  
Wave Radar ◽  
Ground Wave

scholarly journals Robust Target Detection and Tracking Algorithm Based on Roadside Radar and Camera

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1116
Author(s):  
Jie Bai ◽  
Sen Li ◽  
Han Zhang ◽  
Libo Huang ◽  
Ping Wang
Keyword(s):  
Target Detection ◽  
Traffic Safety ◽  
Gaussian Mixture ◽  
Transportation Systems ◽  
Fusion Algorithm ◽  
Smart Cameras ◽  
Probability Hypothesis Density ◽  
Detection And Tracking ◽  
Wave Radar ◽  
Perception System

Intelligent transportation systems (ITSs) play an increasingly important role in traffic management and traffic safety. Smart cameras are the most widely used sensors in ITSs. However, cameras suffer from a reduction in detection and positioning accuracy due to target occlusion and external environmental interference, which has become a bottleneck restricting ITS development. This work designs a stable perception system based on a millimeter-wave radar and camera to address these problems. Radar has better ranging accuracy and weather robustness, which is a better complement to camera perception. Based on an improved Gaussian mixture probability hypothesis density (GM-PHD) filter, we also propose an optimal attribute fusion algorithm for target detection and tracking. The algorithm selects the sensors’ optimal measurement attributes to improve the localization accuracy while introducing an adaptive attenuation function and loss tags to ensure the continuity of the target trajectory. The verification experiments of the algorithm and the perception system demonstrate that our scheme can steadily output the classification and high-precision localization information of the target. The proposed framework could guide the design of safer and more efficient ITSs with low costs.

Short range continuous wave radar for target detection in various mediums

2014 ◽  
Vol 56 (11) ◽  
pp. 2484-2489
Author(s):  
Tauseef Tauqeer ◽  
Maira Islam ◽  
A. K. Aziz
Keyword(s):  
Target Detection ◽  
Short Range ◽  
Continuous Wave ◽  
Wave Radar

scholarly journals Foreign Object Debris Automatic Target Detection for Millimeter-Wave Surveillance Radar

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3853
Author(s):  
Fei Qin ◽  
Xiangxi Bu ◽  
Yunlong Liu ◽  
Xingdong Liang ◽  
Jihao Xin
Keyword(s):  
Long Range ◽  
Millimeter Wave ◽  
Target Detection ◽  
Continuous Wave ◽  
Detection Algorithm ◽  
Superior Performance ◽  
Foreign Object ◽  
Long Distance ◽  
Automatic Target Detection ◽  
Wave Radar

Foreign Object Debris (FOD) refers to any foreign material on the airfield that may injure and threaten the aircraft and airport system. Due to the complex background on the airfield pavement and weak target echoes in long-distance monitoring, it is not easy to detect objects of various types and sizes. The existing FOD radar system’s detection method has a short effective range, and the detectable objects’ radar cross-section intensity is no less than −20 dBsm. In this paper, we propose an integrated FOD automatic target detection algorithm for millimeter-wave (MMW) surveillance radar to improve small target detection under long-range conditions of over 660 m. The signal form of FOD and a clutter model of ground clutter received by millimeter-wave radar are primarily utilized and established theoretically. The runway edge detection means that it is employed based on the in-continuity features as the runway region of interest during the automatic extraction step. Following the clutter map constant false alarm detection algorithm, we utilize a time-domain algorithm that functions as the vital detection processor. Moreover, an explicit definition of the FOD detection performance is developed in a characteristic quantitative way. This criterion involves an absolute reference value for all FOD radar systems. The well-designed FOD frequency-modulated continuous-wave MMW surveillance radar is utilized, and actual experiments are carried out in a real airport in Beijing, China. The results validate the proposed method’s effectiveness and the superior performance of FOD target detection in long-range situations.

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