Algorithm design for detection and tracking of multiple targets using FMCW radar

Hong, Dan-Bee; Yang, Chan-Su

doi:10.1109/oceans-yeosu.2012.6263605

Cited by 3 publications

(1 citation statement)

References 2 publications

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“…The setting of their algorithm and the evaluation study are not published. Another approach is used in [19], where other authors use another type of the algorithm for the processing of the velocity measurement. The second group of authors are using target route tracking for velocity estimation of the targets in this research, but they did not describe their algorithm exactly.…”

Section: The Algorithm Test On the Training Sequence Data And Discussionmentioning

confidence: 99%

Neural Networks Application for Processing of the Data from the FMICW Radars

et al. 2019

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In this paper the results of the Neural Networks and machine learning applications for radar signal processing are presented. The radar output from the primary radar signal processing is represented as a 2D image composed from echoes of the targets and noise background. The Frequency Modulated Interrupted Continuous Wave (FMICW) radar PCDR35 (Portable Cloud Doppler Radar at the frequency 35.4 GHz) was used. Presently, the processing is realized via a National Instruments industrial computer. The neural network of the proposed system is using four or five (optional for the user) signal processing steps. These steps are 2D spectrum filtration, thresholding, unification of the target, target area transforming to the rectangular shape (optional step), and target board line detection. The proposed neural network was tested with sets of four cases (100 tests for every case). This neural network provides image processing of the 2D spectrum. The results obtained from this new system are much better than the results of our previous algorithm.

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Section: The Algorithm Test On the Training Sequence Data And Discussionmentioning

confidence: 99%