Stepped-frequency radar signal processing

Seyfried, Daniel; Schoebel, Joerg

doi:10.1016/j.jappgeo.2014.11.003

Cited by 26 publications

(9 citation statements)

References 6 publications

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“…For example, when detecting pipes by means of measurements subsequently performed perpendicular to the pipes' axes, one obtains hyperbolic reflection shapes, [6]. More on SFR signal processing can be found in [9].…”

Section: Radar Receiver Descriptionmentioning

confidence: 99%

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Investigations on the sensitivity of a stepped-frequency radar utilizing a vector network analyzer for Ground Penetrating Radar

Seyfried

Schubert

Schoebel

2014

Journal of Applied Geophysics

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Section: Radar Receiver Descriptionmentioning

confidence: 99%

“…From (9) it is clear that low-noise amplification of the signal before it is fed to the VNA's receiving port reduces the overall system's noise figure. This in turn makes the radar more sensitive (hence, weaker amplitudes can be detected), which, however, comes at the cost of the receiver dynamic range.…”

Section: Sensitivitymentioning

confidence: 99%

Investigations on the sensitivity of a stepped-frequency radar utilizing a vector network analyzer for Ground Penetrating Radar

Seyfried

Schubert

Schoebel

2014

Journal of Applied Geophysics

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“…However, the penetration depth of ground-penetrating radar is limited, and the effective detection depth generally does not exceed 10m [10][11], it can only detect shallow urban facilities and has no detection capability for underground targets below 30m. The existing shallow surface wave method [12][13][14] has difficulty meeting the detection requirements of urban geophysical exploration owing to its weak anti-jamming capability. Reference [15][16] proposed a load differential radiation pulse on a transient electromagnetic high-performance radiation source for pulse scanning detection to solve the problems of urban electromagnetic interference and insufficient harmonic components emitted by radiation sources.…”

Section: Introductionmentioning

confidence: 99%

High Accuracy Remote Data Detection Method for Underground Space Information Based on Fractional-order Differential Algorithm

Zuo¹,

Chen²,

Fang³

2023

Preprint

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The quality of underground space information has become a major problem that endangers the safety of underground spaces. Currently, the main methods for the high-precision and long-distance transmission of detection information are radar and optical methods. However, in practical applications, we found that the radar method has the shortcomings of large energy loss and poor anti-jamming ability, which limit the accuracy of information data transmission and distance. The optical method has the shortcomings that the weather has a great impact on its accuracy and can only be applied to static objects above ground; therefore, it has the limitation of application objects and use environment. More importantly, the current high-precision information remote detection methods are limited to the detection of overground space objects and are not applicable to the detection of various information data in underground space. In this study, we analyze the spectral properties of the fractional differential operator and find that it is suitable for studying non-linear, non-causal, and non-stationary signals. The theory of fractional calculus is applied to the field of data processing, and a mathematical model of remote transmission and high-precision detection of information based on fractional difference is established, which realizes the functions of high-precision and remote detection of information. By fusing the information data to detect the mathematical model over a long distance and with high accuracy, a mathematical model for stratum data processing used to provide long-distance and high-accuracy data was established. Through application in engineering practice, the effectiveness of this method for underground space information data detection was verified.

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“…With the stepped frequency waveform and a coherent radar system, a high resolution range profile is achieved by taking the inverse discrete Fourier transform (IDFT) of a set of M pulses [8]. See Section II-B.…”

Section: Introductionmentioning

confidence: 99%

Stepped Frequency Pulse Compression With Noncoherent Radar Using Deep Learning

Karlsson

Jansson

Holter

2021

IEEE Trans. Aerosp. Electron. Syst.

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A deep neural network (DNN) is used for achieving subpulse resolution in non-coherent stepped frequency waveform radar. The trade-off between high resolution and long range in radar systems is often addressed using pulse compression, allowing both long pulses and high resolution by increasing the pulse bandwidth. This typically requires a coherent radar. In this study we present a deep learning based solution for achieving subpulse resolution with a non-coherent radar. Our results for such a system are comparable to an equivalent coherent system for SNRs greater than 10 dB. All results are based on simulated data.

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Stepped-frequency radar signal processing

Cited by 26 publications

References 6 publications

Investigations on the sensitivity of a stepped-frequency radar utilizing a vector network analyzer for Ground Penetrating Radar

Investigations on the sensitivity of a stepped-frequency radar utilizing a vector network analyzer for Ground Penetrating Radar

High Accuracy Remote Data Detection Method for Underground Space Information Based on Fractional-order Differential Algorithm

Stepped Frequency Pulse Compression With Noncoherent Radar Using Deep Learning

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