Multipath Detection and Mitigation of Random Noise Signals Propagated through Naturally Lossy Dispersive Media for Radar Applications

Vazquez Alejos, Ana; Dawood, Muhammad

doi:10.3390/s23239447

Cited by 3 publications

(2 citation statements)

References 54 publications

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“…These data are the payload transmitted between the subscribers. The sequence identification process consists of mapping input data with a copy of the preamble, stored on the receiving end [16][17][18].…”

Section: Modified Barker Codesmentioning

confidence: 99%

Modified Nested Barker Codes for Ultra-Wideband Signal–Code Constructions

Nenashev,

Bestugin,

Rabin

et al. 2023

Sensors

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Currently, various applications of ultra-wideband signal–code constructions are among the most vibrant technologies, being implemented in very different fields. The purpose of this research consists of analyzing Barker codes and searching for the optimal nested representations of them. We also aim to synthesize signal–code constructions based on the tenets of nesting of alternative modified Barker codes, which employ an asymmetric alphabet. The scientific merit of the paper is as follows: on the basis of new analytic expressions, modified nested codes and signal–code constructions were obtained, applicable for the establishment of the unambiguous association of the component values of the nested codes with any lobes of the normalized autocorrelation function. With these analytical expressions, we are, hence, able to determine the values of the binary asymmetrical components of the nested codes related to the side lobes of the normalized autocorrelation function. In this way, we clearly obtain better (low) levels for these lobes than by using the autocorrelation function, as established by the equivalent conventional Barker codes, including the nested constructions. Practical application of these modulated ultra-wideband signals ensures improved correlational features, high-fidelity probabilistic detection, and more precise positional detection of physical bodies depending on the range coordinate.

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Section: Modified Barker Codesmentioning

confidence: 99%

Modified Nested Barker Codes for Ultra-Wideband Signal–Code Constructions

Nenashev,

Bestugin,

Rabin

et al. 2023

Sensors

View full text Add to dashboard Cite

show abstract

“…To reduce the workload, semi-automatic labeling is performed. To begin with, a lightweight model is trained on a subset of already labeled data, which facilitates the automatic labeling of the remaining unlabeled data [33]. Subsequently, the automatically generated labels are manually corrected to rectify any mislabeling or omissions.…”

Section: Dataset Labelingmentioning

confidence: 99%

Intelligent Space Object Detection Driven by Data from Space Objects

Tang,

Li,

Xie

et al. 2023

Applied Sciences

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With the rapid development of space programs in various countries, the number of satellites in space is rising continuously, which makes the space environment increasingly complex. In this context, it is essential to improve space object identification technology. Herein, it is proposed to perform intelligent detection of space objects by means of deep learning. To be specific, 49 authentic 3D satellite models with 16 scenarios involved are applied to generate a dataset comprising 17,942 images, including over 500 actual satellite Palatino images. Then, the five components are labeled for each satellite. Additionally, a substantial amount of annotated data is collected through semi-automatic labeling, which reduces the labor cost significantly. Finally, a total of 39,000 labels are obtained. On this dataset, RepPoint is employed to replace the 3 × 3 convolution of the ElAN backbone in YOLOv7, which leads to YOLOv7-R. According to the experimental results, the accuracy reaches 0.983 at a maximum. Compared to other algorithms, the precision of the proposed method is at least 1.9% higher. This provides an effective solution to intelligent recognition for spatial target components.

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Intelligent Space Object Detect Driven by Space Object Data

Tang,

Li,

Xie

et al. 2023

Preprint

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With the rapid development of space programs in various countries, the number of satellites in space is increasing, resulting in an increasingly complex space environment. Therefore, improving space object identification technology has become highly important. We proposes a method of applying deep learning to intelligent detection of space object. We utilize 49 authentic 3D satellite models including 16 scenarios to generate a dataset comprising 17,942 images, which contains over 500 actual satellite photos. Additionally, we acquired a substantial amount of annotated data using a semi-automatic labeling method, which resulted in significant labor cost savings, and obtained a total of 39,000 labels. We validate the feasibility of the dataset using YOLOv3 and YOLOv7 models. What's more, we optimize the YOLOv7 model by integrating deformable convolution RepPoint into the YOLOv7 backbone to obtain the YOLOv7-R model. Through training with these two models, experimental results show that YOLOv3 achieves an accuracy of 0.927, YOLOv7 reaches an accuracy of 0.964, and YOLOv7-R achieves the highest accuracy at 0.983. This provides an effective solution for intelligent space object detection.

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Multipath Detection and Mitigation of Random Noise Signals Propagated through Naturally Lossy Dispersive Media for Radar Applications

Cited by 3 publications

References 54 publications

Modified Nested Barker Codes for Ultra-Wideband Signal–Code Constructions

Modified Nested Barker Codes for Ultra-Wideband Signal–Code Constructions

Intelligent Space Object Detection Driven by Data from Space Objects

Intelligent Space Object Detect Driven by Space Object Data

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