An Efficient Maximum-Likelihood-Like Algorithm for Near-Field Coherent Source Localization

Cheng, Cheng; Liu, Songyong; Wu, Hongzhuang; Zhang, Ying

doi:10.1109/tap.2022.3161269

Cited by 11 publications

(6 citation statements)

References 24 publications

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“…Te ML method can be deployed to examine the behaviour of channel data parameters. Tis study employs the likelihood function [40][41][42] to determine the ML estimation parameters in the measured pathloss data. Specifcally, the likelihood function of the lognormal distribution for P i (i � 1, 2, 3, .…”

Section: Maximum Likelihood Estimatorsmentioning

confidence: 99%

Statistical Characterization and Modeling of Radio Frequency Signal Propagation in Mobile Broadband Cellular Next Generation Wireless Networks

Isabona

Ibitome

Imoize

et al. 2023

Computational Intelligence and Neuroscience

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An accurate assessment of the spatial and temporal radio frequency channel characteristics is essential for complex signal processing and cellular network optimization. Current research has employed numerous models to figure out how much signal propagation loss occurs along the propagation paths. However, there are issues in finding the right model for a particular terrain because these models are not universally applicable. By employing the lognormal function and the Maximum Likelihood model, a hybrid probabilistic statistical distribution model was evolved. Three LTE cell site locations in Port Harcourt, Nigeria, were used to create a hybrid model that describes the functional stochastic signal propagation loss in the area. The evaluated Maximum Likelihood model accurately estimates the relevant wireless channel properties based on observed field data. The minor square regression approach and the proposed hybrid parameter estimation methodology are compared. When it comes to estimating standard deviation errors as well as the root mean square errors, the ML-based approach consistently outperforms the least square regression model. Finally, the proposed hybrid probabilistic statistical distribution model would be useful for mobile broadband network planning in related wireless propagation conditions.

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Section: Maximum Likelihood Estimatorsmentioning

confidence: 99%

Statistical Characterization and Modeling of Radio Frequency Signal Propagation in Mobile Broadband Cellular Next Generation Wireless Networks

Isabona

Ibitome

Imoize

et al. 2023

Computational Intelligence and Neuroscience

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“…Many methods for near-field localization, such as those described in 2 , 11 – 13 , 23 – 29 , rely on a simplified version of the exact model. In the near-field, unlike the far-field, the distance between the source and each element in the receiver array cannot be neglected.…”

Section: Introductionmentioning

confidence: 99%

Electromagnetic informed data model considerations for near-field DOA and range estimates

Ebadi,

Molaei,

Abbasi

et al. 2024

Sci Rep

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Localizing sources in the near-field is one of the emerging challenges for array signal processing, which has received a great deal of attention in recent years. The development of accurate localization algorithms requires the definition of a reliable model of the received signal that takes into account all wavefront characteristics, such as angle, range, and polarization, as well as electromagnetic effects, such as mutual coupling between antennas and the amplitude and phase behaviour of electromagnetic wavefronts. A system model that considers the electromagnetic-informed wave behaviour effects, independent of the type of receiver antennas, array structure, degree of correlation of sources signals and other electromagnetic effects, is considered an “ exact model ” in the literature. However, due to the mathematical complexity of this modeling approach, simplifications using several approximations are conventionally used. For instance, the phase of the exact model is approximated using the Fresnel approximation, while the magnitude of the exact model is simplified by assuming equal distances between the source and all elements in the array. In this work, we evaluate the accuracy of a localization algorithm, the multiple signal classification (MUSIC), using the exact and approximated models in the near-field region. Through a series of simulations, we demonstrate that the localization algorithm designed based on the electromagnetic-informed exact model outperforms the one designed using the approximated model. We also show that considering electromagnetic factors in the system model through the exact model results in a 13% improvement in the direction of arrival (DOA) root mean square error (RMSE) and a 57.7% improvement in range RMSE at signal-to-noise ratio (SNR) of 15 dB.

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“…The above algorithms are based on simplified spatial models [ 18 ] that are approximated by second-order Taylor series expansion, so model errors are unavoidable. In addition, the elimination of parameters in the simplified model is usually conducted to avoid 2D search, which does not utilize all the data in the cumulant matrix and thus increases the estimation error.…”

Section: Introductionmentioning

confidence: 99%

A Mixed-Field Circular and Non-Circular Source Localization Algorithm Based on Exact Spatial Propagation Geometry

Lin

Cui

et al. 2023

Sensors

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In passive localization techniques, as the scale of the array of the sensors used increases, the source distribution may be a coexistence of near-field (NF) and far-field (FF) sources. Most of the existing algorithms dedicated to the localization of mixed-field sources are based on a simplified model, which has model errors and cannot make good use of non-circular properties when non-circular signals are present in the sources. In this paper, we present a mixed-field circular and non-circular source localization algorithm based on exact spatial propagation geometry. First, we make an initial estimate of the source parameters using exact spatial geometry relations. The MUSIC algorithm is then used in combination with the non-circular properties of the signal to achieve an accurate estimate. The algorithm does not lose performance due to model mismatch and is able to make good use of the non-circular properties of the sources to improve the estimation accuracy. The simulation results show that the proposed algorithm can effectively distinguish between sources and that the algorithm performs satisfactorily.

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An Efficient Maximum-Likelihood-Like Algorithm for Near-Field Coherent Source Localization

Cited by 11 publications

References 24 publications

Statistical Characterization and Modeling of Radio Frequency Signal Propagation in Mobile Broadband Cellular Next Generation Wireless Networks

Statistical Characterization and Modeling of Radio Frequency Signal Propagation in Mobile Broadband Cellular Next Generation Wireless Networks

Electromagnetic informed data model considerations for near-field DOA and range estimates

A Mixed-Field Circular and Non-Circular Source Localization Algorithm Based on Exact Spatial Propagation Geometry

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