Radar target identification based on complex natural resonances

Chen, Wei Cher

doi:10.14264/uql.2015.513

Cited by 1 publication

(1 citation statement)

References 66 publications

(160 reference statements)

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“…By analyzing the different captured signals, we determined that the late time began seven samples after the first peak of the absolute value of the baseband signal. This value is obtained considering the time required for the incident pulse to pass through the target and return to the receiving antenna [41]. This method is appropriate at this stage because we assume that the target position and size are known, which is consistent with the application.…”

Section: B Experimental Measurementsmentioning

confidence: 99%

Robust Target Classification Using UWB Sensing^*

2023

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Contactless material characterization has received widespread attention in the radar and engineering domains. Specifically, impulsive Ultra Wideband (UWB) systems are a versatile technology for the nondestructive characterization of samples because the scattered field produced by the targets is highly dependent on their composition and shape. After the initial transient response to the transmitted pulse, the scattered signal can be decomposed as a sum of complex exponentials, called complex natural resonances (CNR), which are dependent only on the geometry and composition of the target. Using this result, a classification problem was formulated to discriminate among targets, and a processing strategy was proposed to solve it. In particular, by using spectral decomposition tools, the information obtained from the physical model can be exploited in combination with data-driven learning techniques. Consequently, a classification strategy that is robust to modeling uncertainties and experimental perturbations was designed. To assess the performance of the new scheme, it was tested using both synthetic and experimental data obtained from targets illuminated with a UWB radar. The results showed substantial gains compared to classification using time-domain signals. INDEX TERMSComplex natural resonances, pattern classification, radar signal processing, spectral analysis.

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Section: B Experimental Measurementsmentioning

confidence: 99%