Propagation Map Reconstruction via Interpolation Assisted Matrix Completion

Sun, Hao; Chen, Junting

doi:10.1109/tsp.2022.3230332

Cited by 17 publications

(2 citation statements)

References 46 publications

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“…where F (u) is the kernel function. It allows the regression problem in equation ( 9) to consider only the interpolation of sample values within the radius b from c. A high weight is assigned if the baseline position s p corresponding to the visibility sample value is close to the interpolated position c; conversely, a low weight is assigned if the baseline position s p corresponding to the visibility sample value is far from the interpolated position c. The kernel function is chosen in this article as Epanechnikov kernel, 11 whose expression is:…”

Section: Interpolation Based On Local Polynomial Regression Modelmentioning

confidence: 99%

An interpolation-assisted matrix completion based method for RFI source localization in microwave interferometric radiometry

Chen,

Zhu,

2023

Microwave Remote Sensing: Data Processing and Applications II

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The Soil Moisture and Ocean Salinity (SMOS) mission led by the European Space Agency (ESA) is aimed at globally monitoring the Earth surface moisture and ocean salinity. As the single payload of the SMOS satellite, the Microwave Interferometric Radiometer with Aperture Synthesis (MIRAS) operates in the protected L-band. Nevertheless, the artificial sources emitting close to or/and entirely in this band are contaminating the collected remote sensing data and deteriorating the performance of the SMOS mission. In this article, we propose a method based on interpolation-assisted matrix completion (IMC) for the localization of RFI sources. The method firstly constructs the augmented covariance matrix (ACM). Secondly, it exploits the local property of visibility function sampling data in the spatial-frequency domain and adopts a local polynomial regression model to interpolate missing visibility data. Thirdly, it exploits the low-rank property of the ACM and recovers this ACM via the matrix completion technique. Finally, the subspace-based algorithm (i.e., MUSIC) is used to estimate source direction-of-arrivals (DOAs) for RFI localization. The proposed RFI localization method is termed as IMC-MUSIC. It is mainly devised for application scenarios where there exist data loss due to inherent array geometry constraint or potential hardware failure and compressed measurements are adopted for reducing system complexity. Validation results show that compared with traditional methods such as discrete Fourier transform (DFT) inversion and covariance-based DOA estimation, the proposed method shows better localization accuracy for different data loss or compression ratios.

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Section: Interpolation Based On Local Polynomial Regression Modelmentioning

confidence: 99%

An interpolation-assisted matrix completion based method for RFI source localization in microwave interferometric radiometry

Chen,

Zhu,

2023

Microwave Remote Sensing: Data Processing and Applications II

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“…Interpolation-based radio map construction techniques exploit real measurements taken at various locations of transmitters (TXs) and receivers (RXs) without explicitly exploiting the geometry structure of the environment. Some representative interpolation methods for radio map construction include k-nearest neighbor (KNN) interpolation [13], inverse distance weighted (IDW) interpolation [14], matrix completion [15], dictionary-based compressive sensing [16], and Kriging [17], etc. These methods are based on the spatial correlation of measurements, but they cannot differentiate the corresponding propagation conditions, such as LOS and non-line-of-sight (NLOS).…”

Section: Introductionmentioning

confidence: 99%

UAV-aided Joint Radio Map and 3D Environment Reconstruction using Deep Learning Approaches

Zeng

Chen

2022

ICC 2022 - IEEE International Conference on Communications

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Machine learning (ML) facilitates rapid channel modeling for 5G and beyond wireless communication systems. Many existing ML techniques utilize a city map to construct the radio map; however, an updated city map may not always be available. This paper proposes to employ the received signal strength (RSS) data to jointly construct the radio map and the virtual environment by exploiting the geometry structure of the environment. In contrast to many existing ML approaches that lack of an environment model, we develop a virtual obstacle model and characterize the geometry relation between the propagation paths and the virtual obstacles. A multiscreen knife-edge model is adopted to extract the key diffraction features, and these features are fed into a neural network (NN) for diffraction representation. To describe the scattering, as oppose to most existing methods that directly input an entire city map, our model focuses on the geometry structure from the local area surrounding the TX-RX pair and the spatial invariance of such local geometry structure is exploited. Numerical experiments demonstrate that, in addition to reconstructing a 3D virtual environment, the proposed model outperforms the state-of-theart methods in radio map construction with 10%−18% accuracy improvements. It can also reduce 20% data and 50% training epochs when transferred to a new environment.

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Radio Map Reconstruction With Adaptive Spatial Feature Learning

Yang,

Guo

2024

IET Signal Processing

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Radio map reconstruction is a fundamental problem of great relevance in numerous real‐world applications, such as network planning and fingerprint localization. Sampling the complete radio map is prohibitively costly in practice and difficult to achieve. Such methods for reconstructing radio maps from a subset of measurements are now gaining additional attention. In this paper, we first explore the spatial features of signals on the radio map and formulate the reconstruction problem as an optimization problem with feature penalties. Then, we propose an iteration algorithm with spatial feature learning to reconstruct signals on the radio map, which improves the reconstruction accuracy by using an adaptive feature dictionary. Numerical examples are given to demonstrate the viability and performance of our method at last.

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Propagation Map Reconstruction via Interpolation Assisted Matrix Completion

Cited by 17 publications

References 46 publications

An interpolation-assisted matrix completion based method for RFI source localization in microwave interferometric radiometry

An interpolation-assisted matrix completion based method for RFI source localization in microwave interferometric radiometry

UAV-aided Joint Radio Map and 3D Environment Reconstruction using Deep Learning Approaches

Radio Map Reconstruction With Adaptive Spatial Feature Learning

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