Automatic Analysis of the Signals from the FMICW Radars

Rejfek, Lubos; Fišer, Ondřej; Chmelar, Pavel; Pitas, Karel; Bezoušek, Pavel; Tran, Phuong T.; Chau, Dong Si Thien

doi:10.1109/radioelek.2019.8733449

Cited by 2 publications

(4 citation statements)

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“…We can see, that the results are unacceptable. For the test, the same data were used, which we used for the validation of our previous algorithm, which is published in [1]. The algorithm results are in Table 3.…”

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

confidence: 99%

“…An example of this was described in [5]. The distance in FMICW radars is calculated by Equation (1).…”

Section: Fmicw Radar Descriptionmentioning

confidence: 99%

“…We are describing the algorithm for the automatic evaluation of the signal from this radar by using neural networks. After comparison with our previous algorithm [1], The benefit of this algorithm is its simple implementation on the Field Programmable Gate Array (FPGA) system and thanks to this, we can analyze signals much faster.…”

Section: Introductionmentioning

confidence: 99%

“…Results of the proposed neural network without unification of the targets. Validation of the previous algorithm published in[1].…”

mentioning

confidence: 99%

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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%

“…An example of this was described in [5]. The distance in FMICW radars is calculated by Equation (1).…”

Section: Fmicw Radar Descriptionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…Results of the proposed neural network without unification of the targets. Validation of the previous algorithm published in[1].…”

mentioning

confidence: 99%

See 2 more Smart Citations