In this study, corner non-functional solder joints were designed as solder joint canaries, and their cycles to failure were compared under four-point bending and thermal cycling tests. A more efficient design and verification method for solder joint canaries was developed. By changing the PCB pad area of the corner solder joints, they were used as canaries for internal functional solder joints, allowing in-situ prediction of internal solder joint failures. To obtain the cycles to failure of solder joint canaries of different sizes, both thermal cycling tests and four-point bending cyclic tests were performed. The four-point bending test provides a more controllable and faster stress loading process. The results show that, compared with the four-point bending test, the thermal cycling test has a longer testing period and shows less distinct regulation effects among different-sized solder joint canaries. To verify the experimental results, a unified creep–plasticity constitutive model coupled with damage was proposed based on a previous model. Using the four-point bending simulation model, the damage evolution during solder joint fatigue was simulated, and the failure behaviors of different-sized solder joint canaries were validated. Further analysis shows that the initial R50 solder joints contain more low-angle grain boundaries and smaller orientation differences compared with the R100 solder joints, resulting in a longer fatigue life. These findings provide important theoretical and methodological support for the efficient design and verification of solder joint canaries.

With the rapid growth in demand for indoor positioning, Wi-Fi Fine Time Measurement (FTM)-based positioning technology has gained significant attention due to its low cost and wide applicability. However, the positioning accuracy is significantly affected by the ranging errors in Wi-Fi. This paper proposes a comprehensive Wi-Fi FTM positioning framework to address this issue. Based on a systematic analysis of Wi-Fi error propagation mechanisms, correction and optimization solutions were developed through determining initial biases and modeling systematic ranging errors, predicting multipath interference via regression, and determining weights assisted by quality classification. Static and kinematic positioning experiments were conducted in an underground garage to validate the model’s generalization performance and applicability across various observation conditions. Results demonstrate that the distance model alone improved the ranging accuracy by 24.68%, and the regression model further enhanced it to 64.00%. By integrating correction and classification strategies, the proposed solution achieved a positioning accuracy of 0.80 m in general static environments and 1.20 m in complex environments. In kinematic experiments, the technique maintained optimal positioning accuracy under complex occlusion conditions, highlighting its robustness and practicality.

This paper proposes a new Signal-to-interference-plus-noise ratio (SINR)-based Device selection, Power control, and Reconfigurable intelligent surface (RIS) configuration (SDPR) algorithm, which allows imperfect aggregation of wireless federated learning (FL) in RIS-assisted Non-Orthogonal Multiple Access (NOMA) systems. The SDPR algorithm selects the local models with SINRs within an acceptable range for global aggregations, benefiting FL from involving more local models with tolerable errors. The convergence of FL under the imperfect aggregation is analytically validated, where the influence of the local model quantization and modulation is captured through the translation of the SINR thresholds to the symbol error rates (SERs). Employing successive convex approximation and gradient descent, we jointly optimize the RIS configuration and the transmit powers of participating devices, thereby minimizing the convergence upper bound of FL under imperfect aggregation. Experimental results demonstrate that using SDPR, FL achieves superior convergence and accuracy by effectively utilizing model updates, even if they are received with errors. Moreover, more quantization bits do not necessarily offer better FL accuracy, and need to be tailored under specific SERs.

This article is dedicated to addressing the localization challenge in 5G environments, specifically utilizing time-of-arrival (TOA) measurements. The focus is on scenarios where the user equipment (UE) and base transceiver stations (BTSs) or first-generation NodeBs (gNBs) face challenges related to synchronization or inaccurate BTS or gNB positions. First, we present the range-weighted majorization minimization (RW-MM) algorithm, harnessing majorization minimization (MM) techniques for UE localization. We rigorously establish the algorithm’s monotonicity and demonstrate its convergence to a stationary point, providing a theoretical foundation for the RW-MM method. In addition, we present a novel UE localization method called robust range-weighted semidefinite relaxation (RRW-SDR). This method is specifically designed for situations involving bounded gNB position errors. The RRW-SDR algorithm optimizes the worst-case weighted least square function while uniquely addressing the individual error constraints associated with each gNB. Furthermore, we derive the Cramér-Rao lower bound (CRLB) for TOA-based UE localization. We additionally establish a more stringent lower bound on the determinant of the target estimation error covariance. This is particularly significant when dealing with scenarios that include independent measurement noise with varying variances. Moreover, we identify an optimal user-gNB geometrical configuration capable of achieving this lower bound. To validate the effectiveness and practical applicability of our contributions, we conduct a series of meticulous numerical simulations. These experiments affirm the robustness and utility of the developed methods.

Planar features serve as an important component in point cloud registration and reconstruction. However, extracting planes in the point clouds collected by a 3D LiDAR sensor is still a challenging task due to the sparse property. To obtain reliable plane segmentation results, it is very necessary to fully exploit the scanning pattern of the sensor. In this paper, we propose a novel plane extraction method for 3D LiDAR data in a framework of point-to-line-to-plane. In the point-to-line stage, a new flat-point detector is introduced to obtain line segments. In the line-to-plane stage, we present the line based Douglas-Peucker algorithm (LBDP) to find coplanar line segments. Unlike region growing, which is generally applied to grouping line segments, LBDP does not suffer from the poor geometry of the selected initial region. More importantly, since the collected point clouds are always noisy, we model the measurement noise via statistical analysis, and bridge the noise level and parameter uncertainty to provide reasonable thresholds throughout our method. In the experiments, we evaluate the proposed method on both simulated and real datasets in terms of true positive rate (TPR), positive predictive value (PPV), F1 score and five segmentation metrics. The results show that the proposed method can accurately extract planes in real time and outperforms the compared approaches.

Water vapor over oceans is integral to climate research, weather prediction, and various scientific disciplines. Owing to challenges in deploying in situ instruments, water vapor over oceans is predominantly measured using satellite-borne sensors such as the satellite-borne scanning microwave radiometer (SMWR) and satellite-borne optical imager (SOI). Nevertheless, evaluations of satellite-based remote sensing of atmospheric water vapor over oceans using alternative independent techniques remain limited. In this study, we examine the performance of satellite-borne sensors in measuring water vapor over oceans using shipborne global navigation satellite system (GNSS) precipitable water vapor (PWV) from 2014 to 2021. The comprehensive evaluation of satellite-based PWV over oceans reveals that SMWR PWV outperforms SOI PWV by approximately 2 mm in root-mean-square (rms). Among all satellite-borne SMWRs, Fengyun (FY)-3 C Microwave Radiation Imager-1 exhibits superior agreement with a mean value of 0.26 mm and an rms of 2.20 mm. Both SMWRs and SOIs exhibit diminished agreement in wetter areas, especially for SOIs, attributable to heightened sensitivity to cloudy and moist weather conditions. Temporally, most satellite-borne sensors demonstrate stable performance in PWV retrieval over oceans, with no apparent observation drift. In addition, MODIS exhibits slightly better stability performance compared to SMWRs. The inter-technique validations affirm the elevated accuracy and stability of satellite-based PWV over oceans. Nonetheless, long-term calibration and refinement of algorithms remain imperative, particularly in tropical regions.

The technology of autonomous vehicle (AV) is critical in nowadays Intelligent Transportation Systems. To achieve the fully automated operation for AVs, one important prerequisite is the accurate and reliable seamless localization covering complex outdoor-indoor scenarios. Although many solutions have been proposed to support AV localization, it is still challenging in achieving reliable drift-free positioning in seamless urban environments. With the current on-board sensors such as GNSS, IMU, LiDAR and cameras, it is difficult to achieve accurate drift-free indoor positioning due to the lack of GNSS indoors. Meanwhile, challenges remain in reliable navigation under obscured conditions. In this paper, we propose a tightly coupled integration algorithm of GNSS RTK, Ultra-Wide Band (UWB) and Visual Inertial Odometry (VIO) to enhance the accuracy and reliability for AVs seamless localization in challenging environments. The UWB technique is innovatively incorporated into the AVs navigation system to extend absolute positioning indoors. The stereo cameras are utilized to improve positioning continuity and enhance GNSS/UWB usability in outdoor-indoor obscured environments. The proposed algorithm is evaluated over real-world datasets in complex seamless environments. The results show that the proposed algorithm achieves 0.411m and 0.077m horizontal positioning accuracy in obscured outdoor and indoor environments, yielding 71.2% and 18.1% improvements compared with the traditional LC integration schemes, respectively.

The emerging Internet of Things (IoT) applications, such as intelligent transportation based on vehicular-lane accurate positioning, have a growing demand for precise and reliable positioning with global navigation satellite systems (GNSSs). It is desirable to use GNSS modules in smartphones to achieve high-precision positioning. The GNSS modules in some brands of smartphones thus far are able to track the new L5 signals of GPS and QZSS, E5a signals of Galileo, and B2a signals of BeiDou-3. The L5/E5a/B2a signals have higher quality due to their signal structure, which provides an important potential for high-precision positioning in smartphones. In this article, we will study the quality of L5/E5a/B2a signals, and their superiorities in integer ambiguity resolution (IAR) and precise positioning with respect to the L1/E1/B1 signals from GPS, QZSS, Galileo, and Beidou-2/3 satellites. The signal quality is evaluated in terms of observation precision, multipath, double-differenced ambiguity fractions, and ambiguity dilution of precision (ADOP). In addition, we propose a new weighting model that takes into account the variation range of carrier-to-noise density ratio ($C / N $textsubscript 0). The results indicate that the Beidou-3 B2a signal has comparable quality to L5/E5a signals of other systems, and all of them are better than the L1/E1/B1 signals. However, the ambiguity fractions of B2a signals diverge abruptly in some periods, resulting in the unsuccessful ambiguity fixing. The L5/E5a/B2a signals can generally obtain higher IAR fix-rate and positioning accuracies than the L1/E1/B1 signals. The new weighting model can capture the smartphone noise characteristics better than the traditional weighting model, thus improving the positioning accuracy.

The ultrawideband (UWB) system generates the precise Time of Arrival (TOA) to realize precise indoor positioning for various Internet of Things (IoT) applications. However, less attention has been paid to mitigating systematic errors in the UWB positioning algorithm, which could be a significant factor that reduces the positioning accuracy. The systematic errors are introduced mainly from two aspects. First, the offsets between the antenna reference point (ARP) and the real incidence point are not carefully eliminated from the ranging observations. Second, the between-antenna time latencies used for cable-linked synchronization usually contain systematic errors at the calibration point. In this article, we present two techniques, including the antenna phase center offset (PCO) calibration and the multipoint time latency determination (MTLD), to reduce the impact of systematic errors on the UWB positioning accuracy. The experiments are carried out in an indoor parking lot. The results show that the established PCO calibration model can correct the PCO errors by 70%. The accuracy of time latency determination is improved by 10% with MTLD and further by 44% with both MTLD and PCO calibration. The static and kinematic positioning tests indicate that the horizontal accuracy is improved from 31 to 10 cm and 20 to 10 cm, respectively.

The observation model of multitemporal inteferometric synthetic aperture radar (MTInSAR) is an underdetermined system. To obtain a unique solution, the traditional techniques impose the temporal constraints by assuming the deformation pattern typically as, e.g., a linear or polynomial model. However, these temporal constraints are not necessarily compatible with the realistic deformation, especially for complex deformations of, e.g., landslides and permafrost. Such discrepancy will bias the retrieval of MTInSAR parameters, thus producing inaccurate deformation results. In this letter, we propose a method for directly solving the deformation sequence by imposing the spatial similarity constraints instead of temporal constraints. The underlying rationale is that the spatially closer points share more similar deformation patterns for most motion events. The capability of the proposed method for retrieving displacement is initially demonstrated by using both simulated and real data experiments.

The ill-posed models are widely encountered in various inversions of geodesy and remote sensing. The regularization approaches can significantly stabilize the solution to ill-posed models since the high-frequency noise is effectively suppressed. Although the famous Tikhonov regularization and truncated singular value decomposition (TSVD) regularization have been widely applied in various geodetic applications, there still remain theoretical drawbacks for either single regularization. For Tikhonov regularization, given a regularization parameter, the low-frequency terms are over regularized, and high-frequency terms are under regularized. For TSVD regularization, some medium-frequency terms will be mistaken for high-frequency terms to be truncated and the hidden signals will be lost. For this reason, we propose an adaptive regularized solution in spectral form, which adaptively divides the terms of different frequencies into three kinds: (i) the low-frequency terms are not regularized; (ii) the medium-frequency terms are regularized by the Tikhonov method; (iii) the high-frequency terms are regularized by TSVD method. The analytical conditions for determining the term sets are derived based on the criteria that the introduced biases should be smaller than the reduced errors, in other words, the mean squared error (MSE) should be reduced. The two examples are presented to demonstrate the performance of our adaptive regularization. The first numerical example is solving the Fredholm integral equation of the first kind, which is widely encountered in remote sensing inversions. The simulations clearly demonstrate that the adaptive regularized solution can improve the MSE of ordinary Tikhonov and TSVD regularized functions by 25.00% and 9.09%, respectively; In the second example, we apply the new method to investigate the mass variation of the Yangtze River Basin based on the Gravity Recovery and Climate Experiment (GRACE) time-variable gravity field model. The Tongji-Grace 2018 monthly gravity field solutions from April 2002 to December 2016 are used to construct the mascon observation equation. The results show that our method also outperforms the ordinary Tikhonov and TSVD regularized solutions, with mean MSE reductions of about 13.40% and 11.69%, respectively. Furthermore, the spatial resolution of secular trend derived by our method are improved and the signal-to-noise ratio (SNR) of mass variation series is higher than the other two regularizations.

The Longyao ground fissure, located in Hebei province, China, undergoes active changes in recent years. The caused deformation has resulted in severe damages to the public property. In order to mitigate these impacts, it is necessary to monitor the deformation of the ground fissure and its surroundings. In this paper, time series InSAR technique is applied to detect the ground movement by using C-band Sentinel-1A images from Oct, 2018 to Dec, 2019. Based on multiple interferograms, we derive deformation characteristics of the interesting area and find discontinuous deformation feature across the Longyao ground fissure, i.e., the northern side is uplifting while the southern side is subsiding. The mean deformation velocity ranges from - 65mm/yr to 60mm/yr. These results will assist in reducing the potential threaten to the human settlement of the studying region.

Estimation of surface deformation using synthetic aperture radar interferometry (InSAR) technique requires a precise removal of the topographic phase. Under multitemporal InSAR (MTInSAR) framework, topographic residual raised by differential operation with external Digital Elevation Model (DEM) is usually parameterized and jointly estimated together with deformation model, while the estimation can be distorted by systematic errors (e.g., model bias, baseline error). This letter aims to offer practical guidelines to users of MTInSAR framework concerning these errors and estimation precision. Starting from the generalized model, we derived the error propagation formula to quantitatively indicate how and to what extent the systematic errors degrade the topographic parameter estimation. The formulas validated by simulated tests are expected to be useful for optimal selection of MTInSAR modeling strategies and development of innovative algorithms (e.g., non-parametric estimator) for retrieval of DEM residuals from MTInSAR measurements.

Received Signal Strength Indicator (RSSI) fingerprinting is known as the most concerned method for indoor localization as its high accuracy and low cost. Numerous RSSI based methods have shown their attractive performances, but the major drawback is the high dependency on the database. In this paper, we propose a multi-floor indoor localization method which includes floor detection and location estimation based on the radial basis function (RBF) network. To ensure the localization accuracy and stability, the network is constructed according to the probabilistic algorithm. Choosing Gaussian radial basis functions with appropriate widths, the network parameters can be initialized appropriately regardless of deficiency of RSSI data. By further conducting the supervised learning of RBF network, the network parameters will be effectively calibrated and updated, so as to achieve a better localization performance. In addition, a radio map quality evaluation criterion is proposed to conduct a comprehensive analysis and interpretation for the localization approach. Finally, experimental results of a publicly accessible dataset which includes multi-floors buildings verify that the performance of the proposed RBF network is superior to other commonly used methods.

Affine transformation that allows the axis-specific rotations and scalars to capture the more transformation details has been extensively applied in a variety of geospatial fields. In tradition, the computation of affine parameters and the transformation of non-common points are individually implemented, in which the coordinate errors only of the target system are taken into account although the coordinates in both target and source systems are inevitably contaminated by random errors. In this article, we propose the seamless affine error-in-variables (EIV) transformation model that computes the affine parameters and transforms the non-common points simultaneously, importantly taking into account the errors of all coordinates in both datum systems. Since the errors in coefficient matrix are involved, the seamless affine EIV model is nonlinear. We then derive its least squares iterative solution based on the Euler–Lagrange minimization method. As a case study, we apply the proposed seamless affine EIV model to the map rectification. The transformation accuracy is improved by up to 40%, compared with the traditional affine method. Naturally, the presented seamless affine EIV model can be applied to any application where the transformation estimation of points fields in the different systems is involved, for instance, the geodetic datum transformation, the remote sensing image matching, and the LiDAR point registration.

The variance and covariance component estimation (VCE) has been extensively investigated. However, in real application, the bottleneck problem is the huge computation burden, particularly when many variance and covariance components are involved for many heterogeneous observations. The objective of this paper is to develop a new method allowing the efficient estimation of variance and covariance components. The core of the new method is to construct an orthogonal complement matrix of the coefficient matrix in a Gauss-Markov model using only the coefficient matrix itself. Therefore, the constructed matrix and the computed discrepancies of measurements with each other, which are the essential inputs for the VCE, are invariant in the iterative procedure of computing the variance and covariance components. As a result, the computation efficiency is significantly improved. As a case study, we apply the new method to evaluate the GPS stochastic model with 15 variance and covariance components demonstrating its superior performance. Comparing with the traditional VCE method, the equivalent results are achievable, and the computation efficiency is improved by 34.2%. In the future, much more sensors will be available, and plentiful data can be acquired. Therefore, the new method will be very promising to efficiently estimate the variance and covariance components of the measurements from the different sensors and reasonably balance their contributions to the fused solution, benefiting the higher time-resolution solutions.