Search Results (6)

Accurate Doppler frequency estimation for Low Earth Orbit (LEO)-based Signals of Opportunity (SoOP) positioning faces significant challenges from extreme dynamics (±40 kHz Doppler shift, 0.4 Hz/ms fluctuation) and severe SNR fluctuations induced by multi-beam switching. Empirical analysis reveals that phased-array beamforming generates three-tiered SNR fluctuation patterns during unpredictable beam handovers, rendering conventional single-algorithm solutions fundamentally inadequate. To address this limitation, we propose KDFE (KNN-Driven Fusion Estimator)—an adaptive framework integrating the Rife–Vincent algorithm and MLE via intelligent switching. Global FFT processing extracts real-time Doppler-SNR parameter pairs, while a KNN-based arbiter dynamically selects the optimal estimator by: (1) Projecting parameter pairs into historical performance space, (2) Identifying the accuracy-optimal algorithm for current beam conditions, and (3) Executing real-time switching to balance accuracy and robustness. This decision model overcomes the accuracy-robustness trade-off by matching algorithmic strengths to beam-specific dynamics, ensuring optimal performance during abrupt SNR transitions and high Doppler rates. Both simulations and field tests demonstrate KDFE’s dual superiority: Doppler estimation errors were reduced by 26.3% (vs. Rife–Vincent) and 67.9% (vs. MLE), and 3D positioning accuracy improved by 13.6% (vs. Rife–Vincent) and 49.7% (vs. MLE). The study establishes a pioneering framework for adaptive LEO-SoOP positioning, delivering a methodological breakthrough for LEO navigation. Full article

Previous lunar missions, such as Surveyor, Apollo, and the Lunar Atmosphere and Dust Environment Explorer (LADEE), have played a pivotal role in advancing our understanding of the lunar exosphere’s dynamics and its relationship with solar wind flux. The insights gained from these missions have laid a strong foundation for our current knowledge. However, due to insufficient near-surface observations, the scientific community has faced challenges in interpreting the phenomena of lunar dust lofting and levitation. This paper introduces the concept of signals of opportunity (SoOP), which utilizes radio occultation (RO) to retrieve the near-surface dust density profile on the Moon. Gravity Recovery and Interior Laboratory (GRAIL) radio science beacon (RSB) signals are used to demonstrate this method. By mapping the concentration of lunar near-surface dust using RO, we aim to enhance our understanding of how charged lunar dust interacts with surrounding plasma, thereby contributing to future research in this field and supporting human exploration of the Moon. Additionally, the introduced SoOP will be able to provide observational constraints to physical model development related to lunar surface particle sputtering and the reactions of near-surface dust in the presence of solar wind and electrostatically charged dust grains. Full article

This paper presents a thorough investigation into the EKF-based SoOP navigation algorithm’s sensitivity to spatial parameters and receiver- and transmitter-related properties. Utilizing the innovative SoOPNE simulation platform, our study unveils significant insights. For instance, at high latitudes, Iridium-Next, and Oneweb show a ten-fold accuracy improvement over Orbcomm. Additionally, discrepancies between predicted and actual satellite trajectories, with a nominal drift of approximately 250 m, result in navigation errors of around 400 m. Our findings underscore the critical importance of addressing these factors to optimize SoOP navigation performance. Full article

A multilayer module is incorporated into the Signals of Opportunity (SoOp) Coherent Bistatic Scattering model (SCoBi) for determining the reflections and propagation of electric fields within a series of multilayer dielectric slabs. This module can be used in conjunction with other SCoBi components to simulate complex, bistatic simulation schemes that include features such as surface roughness, vegetation, antenna effects, and multilayer soil moisture interactions on reflected signals. This paper introduces the physics underlying the multilayer module and utilizes it to perform a simulation study of the response of SoOp-R measurements with respect to subsurface soil moisture parameters. For a frequency range of 100–2400 MHz, it is seen that the SoOp-R response to a single dielectric slab is mostly frequency insensitive; however, the SoOp-R response to multilayer dielectric slabs will vary between frequencies. The relationship between SoOp-R reflectivity and the contributing depth is visualized, and the results show that SoOp-R measurements can display sensitivity to soil moisture below the penetration depth. By simulation of simple soil moisture profiles with different wetting and drying gradients, the dielectric contrast between layers is shown to be the greatest contributing factor to subsurface soil moisture sensitivity. Overall, it is observed that different frequencies can sense different areas of a soil moisture profile, and this behavior can enable subsurface soil moisture data products from SoOp-R observations. Full article

Soil moisture is an important factor affecting the global climate and environment, which can be monitored by microwave remote sensing all day and under all weather conditions. However, existing monostatic radars and microwave radiometers have their own limitations in monitoring soil moisture with shallower depths. The emerging remote sensing of signal of opportunity (SoOp) provides a new method for soil moisture monitoring, but only an experimental perspective was proposed at present, and its mechanism is not clear. In this paper, based on the traditional surface scattering models, we employed the polarization synthesis method, the coordinate transformation, and the Mueller matrix, to develop bistatic radar circular polarization models that are suitable for SoOP remote sensing. Using these models as a tool, the bistatic scattering versus the observation frequency, soil moisture, scattering zenith angle, and scattering azimuth at five different circular polarizations (LR, HR, VR, + 45° R, and −45° R) are simulated and analyzed. The results show that the developed models can determine the optimal observation combination of polarizations and observation angle. The systematic analysis of the scattering characteristics of random rough surfaces provides an important guiding significance for the design of space-borne payloads, the analysis of experimental data, and the development of backward inversion algorithms for more effective SoOP remote sensing. Full article

In this paper, we present the Software PARIS Interferometric Receiver (SPIR), a high-speed GNSS reflectometry recording receiver which has been designed and implemented with the primary goal of demonstrating the synoptic capabilities of the interferometric technique in GNSS Reflectrometry. Thanks to the use of large bandwidth GNSS signals, this technique is advantageous in comparison to the so-called clean-replica processing, when sea surface altimetric applications are pursued. The SPIR receiver down-converts, samples, and records the GNSS signals acquired by the sixteen elements of two antenna arrays. It can operate at any of the common GNSS L1, L2, or L5 bands. Digital beam-forming and signal processing is performed off-line by its dedicated signal processor, so that the GNSS reflectometry can be applied to different transmitting satellites using the same set of recorded signals. Alternatively, different processing techniques can be compared by applying them to exactly the same signals. This article focuses on the SPIR instrument hardware and software, as well as the remote sensing observables that can be obtained using this equipment. Full article