Simple estimation of late-time response for radar target identification

Boonpoonga, Akkarat; Chomdee, Pongsathorn; Burintramart, Santana; Akkaraekthalin, Prayoot

doi:10.1002/2016rs006237

Cited by 14 publications

(9 citation statements)

References 21 publications

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“…Here, the object under consideration is the layered soil. The effect of free space loss was not taken into account because poles are aspect-independent parameters [24]. The hc(t) denotes the impulse response of the mutual coupling between the transmitting and receiving antennas.…”

Section: T H T H T H T H T H T H Tmentioning

confidence: 99%

Mapping the Physical and Dielectric Properties of Layered Soil Using Short-Time Matrix Pencil Method-Based Ground-Penetrating Radar

et al. 2020

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This paper presents the mapping of the physical and dielectric properties of layered soil by using ground-penetrating radar (GPR). Poles extracted from the GPR signals by using the short-time matrix pencil method, along with preprocessing, including filtering and antenna calibration, were employed in order to map the physical and dielectric properties of the layered soil. With the proposed system, the conjugate gradient method was also introduced to solve the time-domain inverse problem faced in the antenna calibration. Experimentations were conducted on four different days in Nakhon Ratchasima province in Thailand, which is a potential area for the occurrence of hardpan. These were done based on the hypothesis that the soil properties, such as water content and dielectric constant, should change when the experimentation day changes in spite of the experimentations in the same area. In the experimentations, soil samples were collected using a core method, and their physical and dielectric properties were measured by using a standard laboratory method and a commercial dielectric probe kit. The measured soil properties and extracted poles for each experiment and each soil layer are shown and analyzed. The results indicate that the real part of the dielectric constants, strongly related to water content, can be mapped using extracted natural frequency. In order to map the dielectric properties, i.e. the water content and dielectric constants of the layered soil, the experimental results were fitted to 2 nd-order polynomial curve. The largest regression value of the fitted curve was 0.9994. The bulk density, which is a physical property of the soil, distinguishes the soil type here differentiated by different soil layers. According to the experimental results, the hardpan occurred at the second soil layer because its bulk density was higher than 1.8 g/cm 3. The bulk density was mapped by extracted poles, including damping factors and natural frequencies. INDEX TERMS dielectric property, permittivity estimation, layered soil, ground-penetrating radar, GPR, short-time matrix pencil method

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Section: T H T H T H T H T H T H Tmentioning

confidence: 99%

Mapping the Physical and Dielectric Properties of Layered Soil Using Short-Time Matrix Pencil Method-Based Ground-Penetrating Radar

et al. 2020

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“…Since the frequency of the scattered signal must be in the same range as that of the transmitted signal, there is a similarity between the transmitted pulse and the late‐time response as well. According to the approach recently proposed in Boonpoonga et al (), the commencement of the late‐time response can be estimated by using a simple cross correlation between the transmitted pulse x ( kT s ) and received signal

r_{n}^{'} ((), {italickT}_{s})

. The output of the cross correlation of the n th received signal was obtained by

C_{n} ([], τ) = \sum_{k = - \infty}^{\infty} x ([], k) r_{n}^{'} ([], k - τ)

where τ was the correlation lag.…”

Section: Proposed Techniquementioning

confidence: 99%

“…However, the minimum time step involves a high computation burden. More recently, we proposed a simple estimation method to distinguish the late‐time response from the entire scattering response (Boonpoonga et al, ). This method employs cross correlation between the transmitted monocycle pulse and scattering in order to find the similarity between them.…”

Section: Introductionmentioning

confidence: 99%

Automatic Detection and Classification of Buried Objects Using Ground‐Penetrating Radar for Counter‐Improvised Explosive Devices

et al. 2018

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In this paper, a technique that can automatically detect and classify objects buried under the ground is proposed. The technique employs a ground‐penetrating radar that transmits electromagnetic waves in order to strike the objects and then receives the backscattering electromagnetic wave to perform signal processing. This signal processing is divided into four main steps as follows. First, preprocessing is used to reduce the clutter due to the effect of the media layer interface. Second, the late time of the scattering signal is estimated using a simple cross correlation. Third, a few successive poles are extracted from the scattering response at the estimated late time by using the short‐time matrix pencil method. Finally, the extracted poles are fed for object classification with different constitutions and/or shapes using a support vector machine. Simulations according to the practical situation in three southern provinces of Thailand to counter the improvised explosive devices were set up. The performance of the proposed technique was evaluated. The simulation results showed that the proposed technique can efficiently detect and classify buried objects for counter‐improvised explosive device operations in the military.

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“…In this approach, the principle of the singularity expansion method (SEM) was applied to encode the tag ID into the tag's structure with the CNR, generally called the pole, and to retrieve the tag ID from the late-time portion of the tag response by using the matrix pencil method (MPM). The main drawback of the MPM is that is requires prior knowledge of the commencement of the late time, which relies upon the geometry and orientation of objects [12], [13]. However, this knowledge is unknown for the non-cooperative scenario.…”

Section: Introductionmentioning

confidence: 99%

Robust Chipless RFID Detection Using Complex Natural Frequency Along With the k-Nearest Neighbor Algorithm

2021

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This paper proposes a detection technique for the chipless RFID system. In the proposed technique, the experiments for measuring the scattering responses of all possible tags were conducted first in a free-space anechoic chamber in order to avoid the effect of unwanted signals due to the environment. The responses of all possible tags were exploited in order to extract poles, including natural frequencies and damping factors, by using the short-time matrix pencil method. All extracted poles successively chosen from the late-time portions were exploited to create the decision boundary by using the k-nearest neighbor algorithm. In order to validate the robustness of the proposed detection technique, experiments with the chipless RFID system with tags attached to containers, i.e. parcel and plastic boxes, were conducted. Poles extracted from the response of the tag attached to a container were fed to the decision boundary for ID detection. The poles that had fallen into the region labeled as logic "1" were detected as logic "1," and vice versa. The experiment results showed that the attachment of the tags to the containers caused a change in the poles. The conventional technique using only natural frequencies has exhibited poor performance regarding tag ID detection. On the other hand, the proposed detection technique achieved a 100% detection rate, although the extracted poles used to detect the bit logic were disturbed by the container. The experimental results confirm the superiority of the proposed system over conventional chipless RFID detection. Moreover, the proposed technique can be considered a robust detection technique.

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Simple estimation of late-time response for radar target identification

Cited by 14 publications

References 21 publications

Mapping the Physical and Dielectric Properties of Layered Soil Using Short-Time Matrix Pencil Method-Based Ground-Penetrating Radar

Mapping the Physical and Dielectric Properties of Layered Soil Using Short-Time Matrix Pencil Method-Based Ground-Penetrating Radar

Automatic Detection and Classification of Buried Objects Using Ground‐Penetrating Radar for Counter‐Improvised Explosive Devices

Robust Chipless RFID Detection Using Complex Natural Frequency Along With the k-Nearest Neighbor Algorithm

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