High-Accuracy Wave Direction Estimation Using Kalman Fusion of Interferometric Measurements and Energy Field Reconstruction

Microwave wireless power transfer (MWPT) for space solar power stations (SSPS) requires high-precision beam pointing in order to maintain effective aperture coupling and transmission efficiency under platform motion and disturbances. This paper proposes a dual-link beam pointing estimation framework that integrates guidance-link interferometric angle-of-arrival (AoA) measurements with power-link energy-field reconstruction. The interferometric chain provides high-rate azimuth and elevation observations for dynamic tracking, while the energy-field reconstruction estimates the energy-centroid displacement from the received-aperture power distribution to correct steady-state pointing bias. A Kalman filter (KF) is developed to fuse the asynchronous multi-rate measurements, yielding continuous and robust pointing estimates for closed-loop beam control. Simulation results show that the proposed fusion method achieves azimuth and elevation RMSEs of 0.0069° and 0.006° with interferometric and energy-centroid error levels of approximately 0.05° and 0.02°, respectively, significantly reducing high-frequency fluctuations. In addition, a sensitivity model is established to quantify the impact of angular errors on capture efficiency. The expected efficiency improves from approximately 0.988 and 0.998 for the individual methods to nearly unity for the fusion output. Quantitative accuracy thresholds corresponding to different efficiency requirements are further derived, providing practical guidelines for SSPS MWPT system design.

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Publication Details

Journal
Sensors
Published
2026-03-15
DOI
https://doi.org/10.3390/s26061852
Primary Topic
Energy Harvesting in Wireless Networks
Type
article
Field-Weighted Citation Impact
0.00

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article

High-Accuracy Wave Direction Estimation Using Kalman Fusion of Interferometric Measurements and Energy Field Reconstruction

Caicheng Wang, Linshan Xue, Xue Li
Sensors
Energy Harvesting in Wireless Networks
article

High-Accuracy Wave Direction Estimation Using Kalman Fusion of Interferometric Measurements and Energy Field Reconstruction

Caicheng Wang, Linshan Xue, Xue Li
article en

Abstract

Microwave wireless power transfer (MWPT) for space solar power stations (SSPS) requires high-precision beam pointing in order to maintain effective aperture coupling and transmission efficiency under platform motion and disturbances. This paper proposes a dual-link beam pointing estimation framework that integrates guidance-link interferometric angle-of-arrival (AoA) measurements with power-link energy-field reconstruction. The interferometric chain provides high-rate azimuth and elevation observations for dynamic tracking, while the energy-field reconstruction estimates the energy-centroid displacement from the received-aperture power distribution to correct steady-state pointing bias. A Kalman filter (KF) is developed to fuse the asynchronous multi-rate measurements, yielding continuous and robust pointing estimates for closed-loop beam control. Simulation results show that the proposed fusion method achieves azimuth and elevation RMSEs of 0.0069° and 0.006° with interferometric and energy-centroid error levels of approximately 0.05° and 0.02°, respectively, significantly reducing high-frequency fluctuations. In addition, a sensitivity model is established to quantify the impact of angular errors on capture efficiency. The expected efficiency improves from approximately 0.988 and 0.998 for the individual methods to nearly unity for the fusion output. Quantitative accuracy thresholds corresponding to different efficiency requirements are further derived, providing practical guidelines for SSPS MWPT system design.

SensorsVol. 26(6)
Chongqing University (CN), China Academy of Space Technology (CN), Chongqing Electromechanical Holdings (China) (CN)
Chongqing University, China Academy of Space Technology, Centre Scientifique et Technique du Bâtiment
Affordable and clean energy
Openalex Percentile: Top 20%
Energy Harvesting in Wireless Networks
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