Tree-interior radar (TIR) imaging using matching pursuit algorithm and comparison to other TIR focusing techniques

Gokkan, Serhat; Özdemir, Caner

doi:10.1016/j.dsp.2021.103218

Cited by 5 publications

(2 citation statements)

References 24 publications

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“…As a result; the detected envelopes of torso-free signatures include not only the characteristics of big limbs such as arms and legs; but also the ones for the smaller body parts such as knees, hands, and fingers. Therefore, further signal processing routines such as matching pursuit algorithm 21,22 can be consecutively used to detect-and-extract the signatures of these various micro-motions that were left for future research. One can easily judge that the model will fundamentally rely on the periodicity and motion behavior characteristics of each limb that are to be studied in future research.…”

Section: Discussionmentioning

confidence: 99%

“…(ii) The second step that is torso removal procedure cannot be applied since the resultant torso‐eliminated signatures will reside on top of each other which in turn will produce more complexity in detection. (iii) For the case of multiple persons’ signatures are not fully separated on the time‐Doppler image, model based‐detection algorithms such as Matching Pursuit 21,22 can be applied such that each person's signatures will be subtracted from the image consecutively for a more reliable detection. Also, range processing of the scene will; of course, effectively detect multi persons at different range distances so that the time‐Doppler processing can, then, be separately applied for each person as examined in some studies 23–26 …”

Section: Application Of Proposed Routines To the Measurement Datamentioning

confidence: 99%

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Signal preprocessing routines for the detection and classification of human micro‐Doppler radar signatures

Tekir

Yılmaz

Özdemir

2023

Micro & Optical Tech Letters

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In this study, we introduce a radar sensor that we have developed for the micro-Doppler signature measurements together with three consecutive signal preprocessing routines that are to be used for primary detection and classification of human gait and movements. The developed C-band radar sensor has been used in a series of human walking and running experiments that were conducted at Mersin University facilities. First, the raw data collected in time domain are transformed to two-dimensional (2D) timefrequency plane to investigate the Doppler characteristics of human movements. To detect and extract the main body signature of the human, a Kalman filter-based torso signature detection methodology is introduced as the first preprocessing tool. Then, to be able to better detect the other body parts' signatures, the torso's response is thoroughly extracted from the 2D time-Doppler image as the second preprocessing routine by applying a circular shift procedure along each time-bin. As the last procedure, envelopes of the micro-Doppler signatures on the 2D time-Doppler image plane are determined by another routine based on human Doppler threshold method. To assess the success and the validity of the proposed pre-signal processing tools, they are applied to the measured data collected via different experiments of human movements. After applying all these steps, the torso signature and envelopes of all other body parts' signatures are marked and determined with good fidelity.

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Section: Discussionmentioning

confidence: 99%

Section: Application Of Proposed Routines To the Measurement Datamentioning

confidence: 99%