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An Improved Detecting Algorithm of Moving Targets for Airborne Maritime Surveillance Radar.
Sensors (Basel)
January 2025
Beijing Aerospace Automatic Control Institute, Beijing 100854, China.
The traditional method is capable of detecting and tracking stationary and slow-moving targets in a sea surface environment. However, the signal focusing capability of such a method could be greatly reduced especially for those variable-speed targets. To solve this problem, a novel tracking algorithm combining range envelope alignment and azimuth phase filtering is proposed.
View Article and Find Full Text PDFReinforcement Learning-Based Resource Allocation Scheme of NR-V2X Sidelink for Joint Communication and Sensing.
Sensors (Basel)
January 2025
College of Information Science and Technology, Donghua University, Shanghai 201620, China.
Joint communication and sensing (JCS) is becoming an important trend in 6G, owing to its efficient utilization of spectrums and hardware resources. Utilizing echoes of the same signal can achieve the object location sensing function, in addition to the V2X communication function. There is application potential for JCS systems in the fields of ADAS and unmanned autos.
View Article and Find Full Text PDFSignal Processing for Novel Noise Radar Based on de-chirp and Delay Matching.
Sensors (Basel)
November 2024
School of Electronic and Optical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China.
Modern radar technology requires high-quality signals and detection performance. However, traditional frequency-modulated continuous wave (FMCW) radar often has poor anti-jamming capabilities, and the high sampling rates associated with large time-bandwidth product signals can lead to increased system hardware costs and reduced data processing efficiency. This paper constructed a composite radar waveform based on noise frequency modulation (NFM) and linear frequency modulation (LFM) signals, enhancing the signal's complexity and anti-jamming capability.
View Article and Find Full Text PDFPoint-cloud clustering and tracking algorithm for radar interferometry.
Phys Rev E
October 2024
Department of Chemistry and Physics, Mount Royal University, Calgary, Alberta, Canada.
In data mining, density-based clustering, which entails classifying datapoints according to their distributions in some space, is an essential method to extract information from large datasets. With the advent of software-based radio, ionospheric radars are capable of producing unprecedentedly large datasets of plasma turbulence backscatter observations, and new automatic techniques are needed to sift through them. We present an algorithm to automatically identify and track clusters of radar echoes through time, using dbscan, a celebrated density-based clustering method for noisy point clouds.
View Article and Find Full Text PDFField validation of effects of species and flock size on echoes in avian radar surveys.
Sci Rep
September 2024
Faculty of Agriculture, Niigata University, 8050 Ikarashi 2-Nocho, Niigata, 950-2181, Japan.
Radar is a powerful technology for surveys of avian movements. Validating the accuracy of radar detection is essential when establishing quantitative criteria for tracking bird trajectories and counting bird flocks. This study clarifies the positional and biological factors influencing the probability of detection (POD) and echo size on X-band marine radar.
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