This paper investigates range-depth localization of an orthogonal frequency-division multiplexing (OFDM) source using a single hydrophone, which serves as a portable scheme suitable for small underwater platforms. We consider a typical shallow-water propagation model, where the rich position information embedded in multipath delays and amplitudes offers the potential to achieve the above purpose. Specifically, we exploit the frequency-domain input-output relationship of pilot subcarriers to construct a measurement model and incorporate joint processing of multiple OFDM symbols to enhance localization performance. To characterize random fluctuations in path gains caused by scattering from the rough surface and bottom, we introduce the Rician fading model. Under this assumption, each complex path amplitude comprises a deterministic component associated with the source position and a random perturbation. To fully leverage the parametric model, we adopt the stochastic maximum likelihood principle. The resulting high-dimensional optimization problem is iteratively solved by the developed expectation-maximization-type algorithm. In addition, a stable initialization scheme is designed to strive for convergence to the global optimum. To quantitatively evaluate the sensing capability of the integrated system, we derive the Cramér-Rao bound on the mean square error of the localization result and elucidate the performance gain from the joint processing. For completeness, we also present a tailored communication processing chain for symbol detection. Simulations and laboratory experiments demonstrate the superiority of the proposed algorithm over existing matched field processing-based positioning techniques.

International audience

This article introduces a radar perception learning framework guided by data collected from commonly equipped visual-inertial (VI) sensor suites on smart vehicles. Unlike existing approaches that rely on dense point clouds from 3D LiDARs, which are costly and not widely deployed, this method leverages the broader availability of VI data. However, visual images alone lack the ability to capture the three-dimensional motion of moving targets, which limits their effectiveness in supervising motion-related tasks. To overcome this limitation, the framework integrates multiple perception tasks such as odometry estimation, motion segmentation, and scene flow prediction into a unified learning process. The first component is an odometry estimation module that combines deterministic ego-motion models with data-driven learning results. This fusion helps accurately infer the scene flow of static background points while minimizing drift. The second component is a supervision signal extraction module that aligns optical and millimeter-wave radar measurements to guide the learning of radar scene flow and rigid transformations. This module improves the reliability of dynamic point supervision through joint constraints across sensing modalities. The third component introduces a feature-selection module designed for cross-modal learning. It enhances the accuracy of motion segmentation and enforces consistency between odometry and scene flow, resulting in more coherent radar perception outputs. Experimental evaluations show that this framework achieves superior performance in challenging conditions such as smoke-obscured environments. It surpasses state-of-the-art (SOTA) methods that depend on high-cost LiDAR systems. The implementation of VISC+ will be open-source athttps://github.com/weini-Eve/VISC

This work proposes a mmWave radar’s scene flow estimation framework supervised by data from a widespread visual-inertial (VI) sensor suite, allowing crowdsourced training data from smart vehicles. Current scene flow estimation methods for mmWave radar are typically supervised by dense point clouds from 3D LiDARs, which are expensive and not widely available in smart vehicles. While VI data are more accessible, visual images alone cannot capture the 3D motions of moving objects, making it difficult to supervise their scene flow. Moreover, the temporal drift of VI rigid transformation also degenerates the scene flow estimation of static points. To address these challenges, we propose a drift-free rigid transformation estimator that fuses kinematic model-based ego-motions with neural network-learned results. It provides strong supervision signals to radar-based rigid transformation and infers the scene flow of static points. Then, we develop an optical-mmWave supervision extraction module that extracts the supervision signals of radar rigid transformation and scene flow. It strengthens the supervision by learning the scene flow of dynamic points with the joint constraints of optical and mmWave radar measurements. Extensive experiments demonstrate that, in smoke-filled environments, our method even outperforms state-of-the-art (SOTA) approaches using costly LiDARs.

The multi-channel outputs of an acoustic vector sensor (AVS) provide diversity gain for communications, and developing an effective combining scheme becomes a critical issue. However, in underwater multipath channels, a single AVS struggles to estimate the spatial signatures of multipath signals due to its limited sensing capability, which compromises the design of optimal combining weights. To overcome this issue, we propose a location-aided maximal ratio combining (MRC) technique. Armed with a predictable end-to-end propagation model, we first develop a maximum-likelihood sensing framework with the help of the pilot subcarriers embedded in the OFDM signal. The required channel state information is inferred from the estimated propagation geometry. Then, the combining weight vector is determined according to the MRC principle. Simulations demonstrate that this integrated scheme enhances communication performance through comprehensive environmental sensing.

Measurements have been made at P-band (0.707 GHz) to construct a model for the dielectric constant of sea water and extend the model for the dielectric constant to high salinity [$S \gt 50$practical salinity scale (pss)]. The measurements are part of research to develop a model for the dielectric constant suitable for future wide-bandwidth (BW) remote sensing of salinity and for application to water bodies, such as the Great Salt Lake with salinity significantly above that found in the open ocean. Measurements have been made at temperatures from 2 °C to 30 °C and salinity from 0 to 138 pss. The data have been fit to a Debye model for the dielectric constant with a single relaxation mode as has been employed at L-band (1.413 GHz), where remote sensing of salinity is currently done. Comparison with contemporary models developed from data at L-band indicates that the L-band model and new P-band model do well at both frequencies for salinity less than 50 pss, but at higher values of salinity, the L-band models diverge from the data. The data have also been used to test at high salinity the mathematical relationship between salinity and conductivity, which is the basis for the pss.

above article [1], the corrections are needed for signs in (3b) and (5a) and for the location of the decimal point in the first term in (8b). Equations (3b) and (5a) with the correct signs are \begin{align*} & \varepsilon _{s}\left ({{S, T}}\right) =\varepsilon _{s}\left ({{0, T}}\right)\left [{{1-\text {SR}\left ({{S, T}}\right)}}\right]\tag {3b}\\[8pt] & \tau \left ({{0, ~T }}\right) = \left [{{17.9539-0.6955 ~T+ 0.0177 ~T ^{2}-0.0002 ~T ^{3}}}\right]10^{-12.} \tag {5a}\end{align*}

A physics-based microwave emission model for layered vegetation (MEMLV) is developed to simulate the vegetation optical depth (VOD) and scattering albedo of coniferous forests from P- to Ka-band (0.4 GHz–37 GHz). This study aims to use physics-based forward modeling to guide and support multifrequency VOD retrieval. The MEMLV consists of three major components: 1) the single-layer discrete scatter model (SL-DSM) that calculates the VOD and single scattering albedo of a single layer; 2) a new Tree Structure Model (TSM) that represents forests by stratified multilayer media; and 3) the Two-Stream microwave emission model (2S-MEM) that combines SL-DSM and TSM to calculate the brightness temperature of forests and their corresponding effective VOD,$\tau _{\text {eff}}$, and effective scattering albedo,$\omega _{\text {eff}}$. Simulation of an exemplary coniferous forest shows that$\tau _{\text {eff}}$increases as frequency increases until reaching saturation at K-band. Meanwhile,$\omega _{\text {eff}}$increases rapidly at low frequencies until reaching a peak at S-band, then decreases until reaching X-band, and finally increases monotonically as frequency increases. Notably,$\omega _{\text {eff}}$is negligible at P-band for most cases. Sensitivity analyses demonstrate the saturation of$\tau _{\text {eff}}$when canopy height is substantial, and the decreasing trend of$\omega _{\text {eff}}$as canopy height or the areal fraction of canopy gap increases. The MEMLV has the potential to improve the parameterization of retrieval algorithms and to enhance the understanding of the retrieved VOD over a wide frequency range.

Metal artifacts caused by the presence of metallic implants tremendously degrade the quality of reconstructed computed tomography (CT) images and therefore affect the clinical diagnosis or reduce the accuracy of organ delineation and dose calculation in radiotherapy. Although various deep learning methods have been proposed for metal artifact reduction (MAR), most of them aim to restore the corrupted sinogram within the metal trace, which removes beam hardening artifacts but ignores other components of metal artifacts. In this paper, based on the physical property of metal artifacts which is verified via Monte Carlo (MC) simulation, we propose a novel physics-inspired non-local dual-domain network (PND-Net) for MAR in CT imaging. Specifically, we design a novel non-local sinogram decomposition network (NSD-Net) to acquire the weighted artifact component and develop an image restoration network (IR-Net) to reduce the residual and secondary artifacts in the image domain. To facilitate the generalization and robustness of our method on clinical CT images, we employ a trainable fusion network (F-Net) in the artifact synthesis path to achieve unpaired learning. Furthermore, we design an internal consistency loss to ensure the data fidelity of anatomical structures in the image domain and introduce the linear interpolation sinogram as prior knowledge to guide sinogram decomposition. NSD-Net, IR-Net, and F-Net are jointly trained so that they can benefit from one another. Extensive experiments on simulation and clinical data demonstrate that our method outperforms state-of-the-art MAR methods.

This paper is concerned with the accurate measurement of the dielectric constant of seawater at P band. The measurement of seawater salinity at L band (1.414 GHz) by satellite becomes less accurate as the seawater temperature decreases. At lower frequencies such as 707 MHz, the sensitivity to salinity of the radiometer measurements increases. Designers of P band radiometers for ocean sensing will need to know how the dielectric constant of seawater varies with salinity (S) and temperature (T). To determine this relationship, a P band cavity, based on an L band design, has been constructed. Initial measurements are made at S=35 psu and T= 10°, 20°, 30°C. The results are compared with the values obtained from L band models evaluated at P band (f=707MHz.). The model predictions are close to the measurement results.

Accurate knowledge of the dielectric constant of sea water is important for remote sensing of surface parameters such as sea surface temperature (SST) and sea surface salinity (SSS). The advent of sensors in space, SMOS [1] , Aquarius [2] and SMAP [3] capable of measuring SSS motivated modern measurements [4] , [5] and modelling [5] , [6] of the dielectric constant at L-band (1.4 GHz). In the past, the range of salinity included in the data used to create these models has been restricted to values typically encountered in the open ocean (e.g., less than 40 psu). However, there are many smaller water bodies with much higher salinity. Notable examples are the Great Salt Lake in Utah with salinity on the order of 180 psu and Garabogazköl lagoon in Turkmenistan with even higher salinity. Unfortunately, existing models for the dielectric constant can’t necessarily just be extended to higher values of salinity. The problem is that the polynomials in salinity and temperature used to represent the unknown parameters in the models are not constrained outside the range of SSS and SST used to determine their coefficients. While the models for the dielectric constant may be very good within that range, outside that range they can lead to unrealistic behavior. Research is underway to develop a model that represents the dielectric constant well over the ocean and behaves well at high salinity. In preparation for possible wideband remote sensing of salinity [7] , [8] , [9] , the laboratory measurements made at 1.413 GHz [4] , [5] are being repeated at 0.707 GHz (P-band) and the plan is to include values of high salinity (50, 100, 150 psu).

This study focuses on modelling the scattering albedo of a vegetation canopy, which can be used in vegetation opacity depth (VOD) retrieval, over a wide frequency range (1-37 GHz). In this study, boreal tree canopy has been used as an example. A discrete scatter model has been implemented to calculate single scattering albedo and vegetation opacity depth (VOD) of a single-layer canopy consisting of a variety type of scatterers. For a more realistic parameterization, a novel tree structure model has been developed to quantify the vertical structure of a forest. For the first time, we combined the discrete scatter model with the multi-layer 2Stream model to calculate the brightness temperature of the tree canopy based on its vertical structure. This research provides a comprehensive forward modelling tool that can be parameterized with different vegetation types (e.g. tree, crops and grass) and parameters (e.g. height, density, soil condition and vertical water content distribution). The model can be used in retrieval algorithm to find effective scattering albedo and VOD over a wide range of frequencies.

The accuracy of the Sea Surface Salinity (SSS) retrieved from L-Band radiometer measurements is strongly dependent on the reliability of the dielectric constant model. Two new parametrizations were recently developed based on one hand on the Soil Moisture and Ocean Salinity (SMOS) satellite multi-angular brightness temperature measurements by Boutin et al. (2021) (BV), and on the other hand on new George Washington University laboratory measurements by Zhou et al. (2021) (GW2020). These two approaches are fully independent. For most SSS and Sea Surface Temperature (SST) conditions commonly observed over the open ocean, the relative variations of brightness temperatures Tb simulated through the BV and GW2020 parametrizations agree particularly well, and better than with earlier parametrizations previously used in the SMOS, Soil Moisture Active Passive (SMAP) and Aquarius SSS retrievals. Nevertheless, uncertainty remains, especially below 10°C where a ~0.1K relative difference between the two models is observed. This motivates the development of a revised parameterization, BVZ, based on a methodology similar to that used to derive BV but using GW2020 instead of SMOS measurements. Compared to the GW2020 parameterization, BVZ is derived with a reduced number of degrees of freedom, it relies on TEOS10 PSS78 conductivity-salinity relationship and on previously derived static permittivity of fresh water. One month per season of SMOS data have been reprocessed in 2018 using BV, GW2020 and BVZ. We find the best overall agreement between SMOS SSS and Argo SSS with BVZ parametrization, with noticeable improvement in the 5°C-15°C SST range.

This paper describes a new system for measuring the dielectric constant of seawater near 700 MHz. The purpose of this research is to obtain data at P-band for the dielectric constant of seawater that can be combined with previous L-band data for developing a model function valid from 500 MHz to 2000 MHz. A transmission-type cylindrical cavity has been constructed with the$\text{TM}_{010}$mode resonance occurring near 700 MHz. The seawater is introduced via a capillary quartz tube. The dimensions of the cavity have been determined by electromagnetic modelling. The model takes the cavity wall loss into account and can provide accurate estimation of resonance condition for both the empty cavity and cavity with seawater. The errors between the theory and measurement are about 0.07% for the resonant frequency and 0.4% for the quality factor,$Q$. The cavity design, experimental setup and measurement schedule will be presented in the paper.

This manuscript reports an improvement in the model for the dielectric constant of seawater used to fit laboratory measurements of the dielectric constant at L-band. The new model (dielectric constant as a function of salinity, temperature and frequency) is based on the response of a polar molecule proposed by Debye and fits the same measurement as reported in earlier work, but uses a functional form for conductivity, σ(S,T), that is given by the definition of salinity. The new version of this model fits the data well and has the advantages that the relaxation time constant is allowed to be a function of temperature and salinity and is well behaved when extrapolated to high salinities.

A decomposition and reconstruction theorem (DRT) is introduced to advance computation and provide physical understanding for the scattering from fractal surfaces (FPs). The profile of FP is decomposed into test profiles (TPs), and the scattering of FP is reconstructed using the scattering of TPs to enhance the computational efficiency. A new method that applies DRT on the extended boundary condition method combined with the truncated singular value decomposition technique (TEBCM) is presented and referred to as TEBCM-DRT. The method of TEBCM-DRT is employed to solve the scattering from realistic soil surfaces, and the results are validated against other scattering models. Moreover, the efficiency of TEBCM-DRT and its validity range are investigated. The result shows that TEBCM-DRT improves computational efficiency to$1.95\times 10^{5}$and$7.35\times 10^{2}$times, respectively, compared to TEBCM and the conventional method of moments for a wide range of roughness. In addition, TEBCM-DRT indicates that the amplitude and direction of propagation of scattering modes are dependent on deterministic TPs. This relationship benefits for obtaining the accurate height of an arbitrary point on the profile from bistatic scattering coefficients.

This paper proposes a closed-form asymptotic solution for the bistatic coherent scattering of a multilayered rough surface structure based upon its effective dielectric constant (EDC) in specular direction. The EDC is modeled by establishing an equivalence of the coherent scattering between the multilayered rough surface structure and a half-space homogeneous medium. The scattering solution is then solved using the scalar Kirchhoff approximation (SKA) method. This new method is referred to as the SKA-EDC method, and it is applied to analyze the sensitivity of the EDC and coherent reflectivity to bare soils with realistic parameters at P- and L- bands. The result indicates that EDC can give different responses to the soil moisture variations with respect to the coherent reflectivity, enabling the potentials of root-zone soil moisture retrieval. At incidence angle smaller than 35°, EDC gives the same value for both polarizations, and the coherent reflectivity can show a significant response to soil at depths < 15-50 cm at 0.80 GHz and <5-15 cm at 1.57 GHz, depending on soil moisture. The simulation also demonstrates that subsurface roughness has a trivial effect on the EDC and coherent reflectivity for three-rough-surfaces separated by continuously dielectric profiles. Subsurface can thus be assumed to be flat for reducing uncertainty in soil moisture inversion algorithms when the subsurface roughness is unknown.

The model expressing the dielectric constant of sea water at microwave frequencies as a function of salinity and temperature is an important element in remote sensing of sea surface salinity. It is also important independently as a description of the physical properties of salt water. A major milestone was the development in the late 1970’s by Klein and Swift of a model based on laboratory measurements at L- and S-band and a functional form supported by theory for polar molecules and previous work on freshwater. Much of the subsequent work has focused on measurements at higher frequency and determining model parameters tuned to apply for applications such as remote sensing of sea surface temperature. Interest in the dielectric constant at 1.4 GHz (L-band) increased again with the development of SMOS and Aquarius to measure salinity from space. But there have been few new measurements at L-band and often confusion regarding the applicability of new models at 1.4 GHz. The objective of this manuscript is to compare available models in the context of how well they represent the dielectric constant of sea water at 1.4 GHz. Among the criteria applied will be the recent measurements at the George Washington University of the dielectric constant at 1.4 GHz.

The accuracy of the Sea Surface Salinity (SSS) retrieved from L-Band radiometer measurements is strongly dependent on the accuracy of the modelling of the dielectric constant (ε). Two new ε parametrizations have recently been developed based on one hand on the Soil Moisture and Ocean Salinity (SMOS) satellite multi-angular brightness temperature measurements and on the other hand on new laboratory measurements. These two approaches are fully independent. These new ε parametrizations are compared with each other and with the ε models previously in use in the SMOS, Soil Moisture Active Passive (SMAP) and Aquarius SSS retrievals. The two new ε parametrizations are found to be in closer agreement than with earlier parametrizations for most common ocean conditions. We will further study to which extent the recent SMOS CCI+SSS v3 reprocessing confirms the above results and could help resolve remaining inconsistencies.

A model function of seawater, which specifies the dielectric constant of seawater as a function of salinity, temperature, and frequency, is important for the retrieval of sea surface salinity using satellite data. In 2017, a model function has been developed based on measurement data at 1.4134 GHz using a third-order polynomial expression in salinity ($S$) and temperature ($T$). Although the model showed improvements in salinity retrieval, it had an inconsistent behavior between partitioned salinities. To improve the stability of the model, new dielectric measurements of seawater have been made recently over a broad range of salinities and temperatures to expand the data set used for developing the model function. The structure of the model function has been changed from a polynomial expansion in$S$and$T$to a physics-based model consisting of a Debye molecular resonance term plus a conductivity term. Each unknown parameter is expressed in$S$and$T$based on the expanded measurement data set. Physical arguments have been used to limit the number of unknown coefficients in these expressions to improve the stability of the model function. The new model function has been employed in the retrieval algorithm of the Aquarius satellite mission to obtain a global salinity map. The retrieved salinity using a different model function is compared within situdata collected by Argo floats to evaluate the impact and the performance of model functions. The results indicate that the new model function has significant improvements in salinity retrieval compared with other existing models.

New seawater dielectric measurements have recently been made over a broad range of salinities and temperatures at the George Washington University (GW). These measurements have been used to refine the existing dielectric model function of seawater for salinity retrieval. The mathematical structure of the new model function has been investigated to: (a) look for a physical basis for the mathematical form; and (b) to optimize the accuracy of data fitting. The Debye model has been chosen to represent the dielectric constant of seawater as a function of salinity (S), temperature (T) and frequency (f). The retrieved salinity using the Debye model has good agreement with the in-situ data collected by Argo floats. The global differences in retrieved salinity and in-situ data will be presented at the meeting.

The dielectric constant of seawater at L-band is determined by a resonant cavity technique. Based on the measurement data, an accurate dielectric model function has been developed and employed to retrieve the ocean surface salinity from the satellite data. The retrieved salinities indicate that the accuracy of the dielectric model needs to be improved to resolve the bias correlated with sea surface temperature. This paper reports the new measurements that have been recently made for the development of a more accurate model function. These new measurements are made using 20, 34 and 36 psu seawater samples from 10°-30° C with a 5° C interval. An improved model function is developed based on the new measurements and on the previous measurements. The salinity retrieval results using the new model function will be presented at the meeting.

The Information-Centric Network possessing the content-centric features, is the innovative architecture of the next generation of network. Collaborating with fog computing characterized by its strong edge power, ICN will become the development trend of the future network. The emergence of Information-Centric Multimedia Network (ICMN) can meet the increasing demand for transmission of multimedia streams in the current Internet environment. The data transmission has become more delay-constrained and convenient because of the distributed storage, the separation between the location of information and terminals, and the strong cacheability of each node in ICN. However, at the same time, the security of the multimedia streams in the delivery process still requires further protection against wiretapping, interception or attacking. In this paper, we propose the delay-constrained green security communications for ICMN based on chaotic encryption and fog computing so as to transmit multimedia streams in a more secure and time-saving way. We adapt a chaotic cryptographic method to ICMN, implementing the encryption and decryption of multimedia streams. Meanwhile, the network edge capability to process the encryption and decryption is enhanced. Thanks to the fog computing, the strengthened transmission speed of the multimedia streams can fulfill the need for short latency. The work in the paper is of great significance to improve the green security communications of multimedia streams in ICMN.

Permanently frozen and seasonally frozen soils occur over a large portion of the Earth's land surface. Changes in the freeze/thaw state of the land surface reflects major changes in thermal and hydraulic properties as well as acting as a “switch” for many ecological processes. In short, soil freeze/thaw state is a fundamental land surface variable in the water and energy cycles, and it connects to the carbon cycle. Surface freeze/thaw state is observable by passive and active microwave sensors. For example, NASA's Soil Moisture Active Passive (SMAP) mission includes a freeze/thaw data product. Such satellite sensing offers routine all-season and all-weather global observations of soil freeze/thaw state with the application of suitable algorithms. We describe early finding from the SLAPex Freeze/Thaw campaign, believed to be the first airborne campaign of its type, focusing on soil freeze/thaw.

Recently, the dielectric constant of seawater at L-band was determined by employing a resonant cavity technique. A dielectric model function has been developed based on the measurement data and the model function has been used for retrieving the ocean salinity. The results indicate that additional accuracy is still needed to resolve the bias correlated with sea surface temperature. This paper reports the improvements that have been made recently for the development of a more accurate seawater dielectric model function. The additional measurements for the open ocean will be addressed in the paper.

Existing model functions for the dielectric constant of sea water produce viable retrievals of ocean salinity but with differences that are important for eventual optimization of the science product. Issues exist in cold water and the dependence on sea surface temperature. Additional accuracy is needed to resolve the behavior in cold water and a residual bias correlated with sea surface temperature. The model function developed from direct measurements of the dielectric constant highlights the issues and points to regimes where new measurements and accuracy are needed.

This paper describes a new model of the seawater dielectric constant as a function of salinity and temperature at L-band. The model function is developed by fitting the accurate measurement data made at 1.413 GHz to a third-order polynomial. The purpose of this study is to provide an accurate model for earth-observing satellites to retrieve seawater salinities from remote sensing data. In this paper, the development of the model function is introduced along with an analysis of the goodness of fit. The model function is then compared with the model functions of Klein-Swift and Meissner-Wentz. Finally, the comparison is made between the retrieved salinity from the satellite data with the in situ data measured by Argo floats.

Seawater salinity measurements are currently being made at L-band (1.413 Ghz) by NASA's Aquarius instrument (on the Aquarius/SAC-D observatory). The goal of Aquarius mission is to measure the salinity of seawater to an accuracy on the order of 0.2 psu; this requires a model function of seawater permittivity with a high accuracy. Since 2011, the George Washington University (GW) has employed a cavity technique to determine the complex permittivity of seawater at 1.413 GHz. In this paper, a new seawater dielectric model function is introduced including the latest permittivity data for seawater with salinity 33 psu. Finally, the validation of the end-effect, measurement variance and data fitting will be discussed.