Large Localization Errors Research Articles

Adaptive Pseudospectral Successive Convex Optimization for Six-Degree-of-Freedom Powered Descent Guidance

Guidance for six-degrees-of-freedom powered descent is more challenging due to its stronger nonlinearity compared to three-degrees-of-freedom. The standard convex programming algorithm has been difficult to effectively address this problem. To enhance the performance of successive convex programming in terms of both optimality and accuracy, an adaptive pseudospectral successive convex optimization algorithm is proposed in this paper. First, it transforms the nonlinear optimal control problem into a convex subproblem by integrating global pseudospectral discretization with local linearization. Second, a parameter-adaptive successive convexification algorithm is proposed, which adaptively adjusts the trust region size based on the update rate of the optimal trajectory. Last, the global error and local error are accurately calculated based on the pseudospectral method. To tackle the issue of excessively large local errors caused by nonlinearity, an adaptive grid method is proposed to refine the mesh grid. Simulation results demonstrate the efficacy of the proposed pseudospectral convex optimization method and adaptive mesh grid method in reducing local errors and improving solution accuracy. Furthermore, the proposed parameter-adaptive successive convex optimization algorithm exhibits adaptability and optimality across different initial conditions, while also improving solution accuracy.

Markerless tracking of tumor and tissues: A motion model approach.

Respiratory motion management is essential in order to achieve high-precision radiotherapy. Markerless motion tracking of tumor can provide a non-invasive way to manage respiratory motion, thereby enhancing treatment accuracy. However, the low contrast in real-time x-ray images for image guidance limits the application of markerless tracking. We present a novel approach based on a motion model to perform markerless tracking of tumor and surrounding tissues even when they have low contrast in real-time x-ray images. A proof-of-concept validation of the method has been performed using digital and physical phantoms at breathing conditions that are significantly different than the planning stage. A motion model is first constructed by performing principal component analysis (PCA) on the planning 4DCT. During treatment, the motion of a surrogate is tracked and used as the input of the motion model, which generates a 3D real-time volume estimation. Such 3D estimation is then projected to 2D to create digitally reconstructed radiographs (DRRs). The relationships between the real-time DRRs, reference DRRs, and reference x-ray images are first established to simulate 2D real-time images from the real-time volume. The registration between the simulated 2D real-time images and real-time x-ray images corrects the initial motion model estimation to ensure the estimated volume matches the real-time condition. In digital phantom, the Dice index of pancreas was improved from 0.74 to 0.78 after correction using real-time DRRs in fully inhaled phase. Validation on lung and pancreas is performed in physical phantom with two motion traces. The surrogate-tumor relationships were intentionally altered to generate large target localization errors due to the differences in body condition between treatment planning stage and during treatment. The real-time correction for the estimated 3D real-time volume was performed using a pair of 2D x-ray images. For the deep breathing motion trace, the tumor localization mean absolute error (MAE) throughout the tracking decreases from around 3mm to less than 1mm after correction. For the shallow breathing motion trace with a 1.7mm baseline shift, the tumor localization MAE throughout the tracking decreases from around 1.5mm to less than 1mm after correction. The method combines the detailed structural information from planning 4DCT and real-time information from real-time x-ray images through a motion model. The matching between the real-time model estimation and 2D real-time images is performed in the same modality so that it can be applied to regions with low contrast in the images. The real-time images successfully corrected the initial motion model estimations in our proof-of-concept validation. This suggests the potential to perform markerless tracking in low-contrast region using a motion model.

An Enhanced Hidden Markov Model for Map-Matching in Pedestrian Navigation

Map-matching is a core functionality of pedestrian navigation applications. The localization errors of the global positioning systems (GPSs) in smartphones are one of the most critical factors that limit the large-scale deployment of pedestrian navigation applications, especially in dense urban areas where multiple road segments exist within the range of GPS errors, which can be increased by tall buildings neighboring each other. In this paper, we address two issues of practical importance for map-matching based on the Hidden Markov Model (HMM) in pedestrian navigation systems: large localization error in the initial phase of map-matching and HMM breaks in open field traversals. A heuristic method to determine the probability of initial states of the HMM based on a small number of GPS data received during the short warm-up period is proposed to improve the accuracy of initial map-matching. A simple but highly practical method based on a heuristic evaluation of near-future locations is proposed to prevent the malfunction of the Viterbi algorithm within the area of open fields. The results of field experiments indicate that the enhanced HMM constructed via the proposed methods achieves significantly higher map-matching accuracy compared to that of state of the art.

Reliable urban vehicle localization under faulty satellite navigation signals

Reliable urban navigation using global navigation satellite system requires accurately estimating vehicle position despite measurement faults and monitoring the trustworthiness (or integrity) of the estimated location. However, reflected signals in urban areas introduce biases (or faults) in multiple measurements, while blocked signals reduce the number of available measurements, hindering robust localization and integrity monitoring. This paper presents a novel particle filter framework to address these challenges. First, a Bayesian fault-robust optimization task, formulated through a Gaussian mixture model (GMM) measurement likelihood, is integrated into the particle filter to mitigate faults in multiple measurement for enhanced positioning accuracy. Building on this, a novel test statistic leveraging the particle filter distribution and the GMM likelihood is devised to monitor the integrity of the localization by detecting errors exceeding a safe threshold. The performance of the proposed framework is demonstrated on real-world and simulated urban driving data. Our localization algorithm consistently achieves smaller positioning errors compared to existing filters under multiple faults. Furthermore, the proposed integrity monitor exhibits fewer missed and false alarms in detecting unsafe large localization errors.

Robust Positioning Estimation for Underwater Acoustics Targets with Use of Multi-Particle Swarm Optimization

With the extensive application of sensor technology in scientific ocean research, ocean resource exploration, underwater engineering construction, and other fields, underwater target positioning technology has become an important support for the ocean field. This paper proposes a robust positioning algorithm that combines the disadvantages of distributed estimation and particle swarm optimization, which can solve the large localization error problem caused by uncertainties in underwater acoustic communication and sampling processes. Considering the presence of ranging anomalies and sampling packet loss in underwater acoustic measurements, a weighted coupling filling method is used to correct the outliers in an underwater acoustic ranging signal. Based on the mapping model from the element array to the underwater acoustic responder, an unconstrained optimization algorithm for one-time localization estimation of underwater acoustic targets was established. Based on the one-time localization estimation results of underwater acoustic targets, an improved multi-particle swarm optimization estimation based on interactive search is proposed, which improves the accuracy of underwater target localization. The numerical results show that the positioning accuracy of the proposed algorithm can be effectively enhanced in cases of distance measurement errors and azimuth measurement errors. Compared with the positioning error before optimization, the positioning error can be reduced after optimization. Additionally, the experiment was carried out in the underwater environment of Hangzhou Qiandao Lake, which verified the positioning performance of the proposed algorithm.

A Note on Performance Assessment of Signal Energy–Based Acoustic Source Localization in a Carbon Fiber–Reinforced Polymer Plate

Abstract The effectiveness of the signal energy–based acoustic source localization approach in practical applications has yet to be established. This is addressed herein by conducting an experimental study on a 500 mm × 500 mm carbon fiber–reinforced polymer plate and generating artificial acoustic events in the plate. Upon acquiring the propagating wave signals at several well-scattered sensors, the signal energy–based approach is applied, and the accuracy of the source localization results is noted. Seven experiments are performed with varying source locations, sensor-plate bonding, and excitation types. This approach has performed well for five experiments with source localization errors below 15 mm. The remaining two experiments where the acoustic sources are relatively close to the plate edges compared to the other experiments have, however, produced large localization errors, indicating a scope of improvement in the approach to encompass all situations.

MOANS DV-Hop: An anchor node subset based localization algorithm for wireless sensor networks

Several applications have exploited wireless sensor networks (WSNs), and localization is a key WSN technology. The range-free localization technique is substantially less expensive than conventional range-based localization strategies since it does not require distance or angle measurements between the anchor and unknown nodes. The Distance Vector-Hop (DV-Hop) technique is a widespread localization solution for WSNs because of its straightforward theory and minimal cost. The coordinate estimating precision of the classic DV-Hop algorithm needs further improvement due to its large localization error. In this paper, a DV-Hop-based method utilizing modified optimum anchor node subset (MOANS DV-Hop) is proposed to improve the localization performance of the DV-Hop algorithm. A strategy for anchor node deployment is proposed. An objective function is formulated to minimize the error in estimating coordinates of unknown nodes. With the MOANS DV-Hop algorithm, each anchor node first uses other anchor nodes to locate itself, then uses the Scaled General Learning Equilibrium Optimizer (SGLEO) algorithm to create an optimal subset made up of anchor nodes other than itself. The anchor node then updates its average hop size using the anchor node subset and broadcasts both the updated hop size and the anchor node subset to the nearby unknown nodes. An unknown node then determines its location using the Equilibrium Optimizer (EO) algorithm, the anchor node subset, and the updated hop size received from the closest anchor node. Simulation findings show that MOANS DV-Hop algorithm has a greater level of localization accuracy in coordinate estimation than both the classical and other improved DV-Hop methods.

Sensor error calibration and optimal geometry analysis of calibrators

The target localization performance is not only affected by the measurement noise, but also related with the sensor error such as sensor position error and measurement biases. If not properly estimated and calibrated, sensor error may cause large localization error or ghost tracks. This paper investigates the sensor error calibration problem with imperfect calibrators whose positions are not accurately known when both the sensor position error and measurement biases exist. The performance loss without considering the sensor error is analyzed by evaluating the mean square error (MSE). A closed-form solution based on weighted least squares (WLS) is proposed to estimate the sensor measurement bias and update the sensor position by using the biased range and angle measurement about calibrators. Moreover, the optimal geometry for calibration objects is analyzed and the optimum criterions for updating sensor position are proposed by minimizing the trace of the CRLB. The optimal calibrator-sensor geometries in the presence of measurement bias are generally different from the optimal calibrator-sensor geometries in the absence of measurement bias. Finally the performance of the proposed solution is verified and the optimal calibrator-sensor geometry results for 2 or 3 sensors are solved efficiently by using Nelder–Mead simplex method in simulation.

Deep Learning-Based Geomagnetic Navigation Method Integrated with Dead Reckoning

Accurate location information has significant commercial and economic value as they are widely used in intelligent manufacturing, material localization and smart homes. Magnetic sequence-based approaches show great promise mainly due to their pervasiveness and stability. However, existing geomagnetic indoor localization methods are facing the problems of location ambiguity and feature extraction deficiency, which will lead to large localization errors. To address these issues, we propose a coarse-to-fine geomagnetic indoor localization method based on deep learning. First, a multidimensional geomagnetic feature extraction method is presented which can extract magnetic features from spatial and temporal aspects. Then, a hierarchical deep neural network model is devised to extract more accurate geomagnetic information and corresponding location clues for more accurate localization. Finally, localization is achieved through a particle filter combined with IMU localization. To evaluate the performance of the proposed methods, we carried out several experiments at three trial paths with two heterogeneous devices, Vivo X30 and Huawei Mate30. Experimental results demonstrate that the proposed algorithm can achieve more accurate localization performance than the state-of-the-art methods. Meanwhile, the proposed algorithm has low cost and good pervasiveness for different devices.

Kernel free boundary integral method for 3D incompressible flow and linear elasticity equations on irregular domains

A second-order accurate kernel-free boundary integral method is presented for Stokes flow and linear elasticity boundary value problems on three-dimensional irregular domains. It solves equations in the framework of boundary integral equations, whose corresponding discrete forms are well-conditioned and solved by the GMRES method. A notable feature of this approach is that the boundary or volume integrals encountered in BIEs are indirectly evaluated by a Cartesian grid-based method, which includes discretizing corresponding simple interface problems with a MAC scheme, correcting discrete linear systems to reduce large local truncation errors near the interface, solving the modified system by a CG method together with an FFT-based Poisson solver. No extra work or special quadratures are required to deal with singular or hyper-singular boundary integrals and the dependence on the analytical expressions of Green’s functions for the integral kernels is completely eliminated. Numerical results are given to demonstrate the efficiency and accuracy of the Cartesian grid-based method.

Fault location of flexible grounding distribution system based on multivariate modes and kurtosis calibration

Aiming at the problems of large fault location errors when the traditional traveling wave fault location is applied to flexible ground distribution systems. This paper proposed the fault location of flexible grounding distribution system based on multivariate modes and kurtosis calibration. Firstly, a disturbance signal is generated by putting a small resistor in parallel. This signal reaches the fault point and undergoes a sudden change, generating an aerial mode traveling wave signal. Secondly, it used the multivariate variational mode decomposition algorithm to decompose the aerial mode current and extracts the fault features. Then, the discrete kurtosis value is calculated for the highest frequency intrinsic mode function component. The moment of maximum kurtosis is the arrival time of the aerial mode current. Finally, it adopted the head-end detection device to calculate the fault distance. For the complex topology of the flexible ground distribution systems, the localization equation of the overhead line, cable line, and hybrid lines are studied, and the pseudo-fault points are excluded by adding auxiliary devices at the branch end. The simulation results demonstrate that the proposed method can accurately locate faults and is not affected by the fault's initial phase angle, transition resistance, and location.

Maximum likelihood-based estimation of diffusion coefficient is quick and reliable method for analyzing estradiol actions on surface receptor movements.

The rapid effects of estradiol on membrane receptors are in the focus of the estradiol research field, however, the molecular mechanisms of these non-classical estradiol actions are poorly understood. Since the lateral diffusion of membrane receptors is an important indicator of their function, a deeper understanding of the underlying mechanisms of non-classical estradiol actions can be achieved by investigating receptor dynamics. Diffusion coefficient is a crucial and widely used parameter to characterize the movement of receptors in the cell membrane. The aim of this study was to investigate the differences between maximum likelihood-based estimation (MLE) and mean square displacement (MSD) based calculation of diffusion coefficients. In this work we applied both MSD and MLE to calculate diffusion coefficients. Single particle trajectories were extracted from simulation as well as from α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) receptor tracking in live estradiol-treated differentiated PC12 (dPC12) cells. The comparison of the obtained diffusion coefficients revealed the superiority of MLE over the generally used MSD analysis. Our results suggest the use of the MLE of diffusion coefficients because as it has a better performance, especially for large localization errors or slow receptor movements.

Neural Networks-Based Wi-Fi/PDR Indoor Navigation Fusion Methods

Pedestrian dead-reckoning (PDR) technique has high accuracy in short-term positioning and is little affected by environments, but its cumulative error makes long-term positioning infeasible. Received signal strength (RSS) fingerprint positioning is immune to the cumulative error, but its poor radio signal propagation affected by indoor environments leads to a large positioning error. The extended Kalman filter (EKF) is widely applied to improve the two methods, but the approximation of nonlinearity still causes a large localization error. In this study, we propose a new approach based on neural networks for the Wi-Fi/PDR positioning fusion. The nonlinear mapping capability of back-propagation (BP) neural network is used to correct the EKF nonlinear approximation errors. Unlike the commonly used PDR algorithm, a series of pedestrian historical motion states are applied to train the long short-term memory (LSTM) and then make predictions. This method can significantly reduce cumulative errors in PDR. On-site experiments demonstrate that the proposed method achieves an average localization error of 1.18 m with 73% of the errors under 2 m, which outperforms EKF and unscented Kalman filter (UKF) by approximately 45% under the same test environment. Moreover, another fusion method using LSTM localization results as a priori information is described in this article. Different from the fusion at the resulting level of the previous method, this second method achieves the fusion at the algorithm level by limiting the Wi-Fi reference point (RP) search range through the LSTM localization results to reduce training and positioning time.

Tailored Hidden Markov Model: A Tailored Hidden Markov Model Optimized for Cellular-Based Map Matching

Although the GPS-based positioning is ubiquitous for its high precision, the high power consumption brought by the high sampling frequency and the poor GPS signal penetration limits its availability in locating low-power mobile devices (especially mobile phones). As a promising complement, the cellular-based positioning has attracted great attention since it consumes much less power as well as its higher availability. However, the sparsity of cellular-based data (due to lower sampling rate) and large localization errors make the measurement accuracy becomes the main challenge of the cellular-based positioning. hidden Markov model can well solve the problem of positioning error of GPS data, but it is less accurate when applied to map matching of cellular-base data. Therefore, to improve accuracy, in this article, we propose a novel algorithm called the tailored hidden Markov model (THMM) that is optimized for the cellular-based data. Specifically, the geometric, the topological, and the probabilistic characteristics have been considered and fully exploited in the THMM design. Our proposed schemes are evaluated using real-world motor vehicle movement trajectories collected in Tianjin and the experimental results are encouraging compared with the state-of-the-art algorithms.

Three-Dimensional Node Localization Algorithm Based on Communication Radius Refinement and Improved Particle Swarm Optimization

Background: With the development of the Internet of things, WSN node positioning is particularly important as its core technology. One of the most widely used algorithms, the DV-hop algorithm, has many advantages, such as convenient operation, use of no additional equipment, etc. At the same time, it also has some disadvantages, like large location error and insufficient robustness. Particle swarm optimization algorithm is advantageous in dealing with nonlinear optimization problems. Therefore, the improved particle swarm optimization algorithm is introduced to solve the problem of inaccurate positioning. Objective: Aiming at the problem of large positioning error in the three-dimensional node localization algorithm, the paper proposes an intelligent node localization algorithm based on hop distance adjustment. The algorithm is used to optimize the hop number of nodes and make the distance calculation more accurate. At the same time, particle swarm optimization is used to intelligently solve the problem of choosing the most valuable node position. Methods: Firstly, this paper analyzed the errors caused by the 3D DV-hop localization algorithms. Then, a new method of distance estimation and coordinate calculation is provided. At the same time, mutation factors and learning factor based on the particle swarm optimization algorithm are introduced. Then, a three-dimensional node localization algorithm based on ranging error correction and particle swarm optimization algorithm is proposed. Finally, the improved algorithm is simulated and compared with similar algorithms. The simulation results show that the proposed algorithm has good convergence and improves the positioning accuracy without additional hardware conditions, and effectively solves the problem of inaccurate node positioning. : The simulation results show that the proposed algorithm has good convergence and improves the positioning accuracy without additional hardware conditions, and effectively solves the problem of inaccurate node positioning. Results: The proposed algorithm creatively combines the hop number correction and particle swarm optimization algorithm to improve the accuracy of node positioning and robustness. However，the amount of computation is increased. Conclusion: Overall, it is within acceptable limits. It is worthwhile to improve the performance with a little increase in the amount of computation. The algorithm is worth popularizing.

Assessment of the data assimilation framework for the Rapid Refresh Forecast System v0.1 and impacts on forecasts of a convective storm case study

Abstract. The Rapid Refresh Forecast System (RRFS) is currently under development and aims to replace the National Centers for Environmental Prediction (NCEP) operational suite of regional- and convective-scale modeling systems in the next upgrade. In order to achieve skillful forecasts comparable to the current operational suite, each component of the RRFS needs to be configured through exhaustive testing and evaluation. The current data assimilation component uses the hybrid three-dimensional ensemble–variational data assimilation (3DEnVar) algorithm in the Gridpoint Statistical Interpolation (GSI) system. In this study, various data assimilation algorithms and configurations in GSI are assessed for their impacts on RRFS analyses and forecasts of a squall line over Oklahoma on 4 May 2020. A domain of 3 km horizontal grid spacing is configured, and hourly update cycles are performed using initial and lateral boundary conditions from the 3 km grid High-Resolution Rapid Refresh (HRRR). Results show that a baseline RRFS run is able to represent the observed convection, although with stronger cells and large location errors. With data assimilation, these errors are reduced, especially in the 4 and 6 h forecasts using 75 % of the ensemble background error covariance (BEC) and 25 % of the static BEC with the supersaturation removal function activated in GSI. Decreasing the vertical ensemble localization radius from 3 layers to 1 layer in the first 10 layers of the hybrid analysis results in overall less skillful forecasts. Convection is greatly improved when using planetary boundary layer pseudo-observations, especially at 4 h forecast, and the bias of the 2 h forecast of temperature is reduced below 800 hPa. Lighter hourly accumulated precipitation is predicted better when using 100 % ensemble BEC in the first 4 h forecast, but heavier hourly accumulated precipitation is better predicted with 75 % ensemble BEC. Our results provide insight into the current capabilities of the RRFS data assimilation system and identify configurations that should be considered as candidates for the first version of RRFS.

An Efficient UAV Localization Technique Based on Particle Swarm Optimization

Unmanned aerial vehicles (UAVs) have recently attracted tremendous attentions in both industries and academic communities. Thanks to the high mobility and flexibility, UAVs can be deployed in many scenarios to provide various types of services. In these scenarios, the position of the UAVs must be timely and accurately acquired to avoid UAV collisions and realize millimeter-wave beamforming. Particle swarm optimization (PSO) is a potential approach to fulfill localization under GPS-denied environment. However, it has the drawbacks of high complexity and relative large localization error. In this article, we consider the UAV localization problem based on improved PSO, which aims at reducing complexity and localization error. We firstly analyze the performance metrics and performance bounds of conventional PSO in the considered UAV localization scenario. Then, the particle initialization process is reconsidered, where a particle and search space reduction method is introduced as the hierarchical PSO (HPSO). Next, the particle updating schemes are redesigned based on the particle number, where the reference best particle is introduced to deal with the limitations in conventional PSO, this is called reference PSO (RPSO). Lastly, the proposed HPSO and RPSO are validated in simulation results. It is shown that the proposed PSO method has both reduced complexity and localization error compared with conventional PSO and other reference methods.

Node topology location method of SDN social network based on ant colony algorithm

Due to the convergence defect of the existing SDN social network node topology location methods, there is a large node topology location error problem. In order to solve the above problems, this paper proposes a topology location method of SDN social network nodes based on ant colony algorithm. Firstly, the ant colony algorithm is introduced, and then the biological principle of ant colony algorithm is analyzed to determine the basic steps of ant colony algorithm. According to the number of mobile beacon launch positions and specific launch coordinates in ROI, ant colony algorithm is introduced into the mobile beacon path acquisition program to realize SDN node topology location based on ant colony algorithm. The experimental results show that under the background of node communication radius of 2.0 and 5.0, the node topology positioning errors of this method are small, and the minimum errors are 9.10% and 5.15% respectively. It is fully proved that this method has good node topology positioning effect.

Three-Dimensional DV-Hop Localization Algorithm Based on Hop Size Correction and Improved Sparrow Search

Acquiring precise localization information of sensor nodes is very important in wireless sensor networks. The 3DDV-hop localization algorithm suffers from large localization errors and high energy consumption. In order to improve positioning accuracy and reduce energy consumption, a 3DDV-hop node localization algorithm (3D-HCSSA) based on hop size correction and improved sparrow search optimization is proposed. The algorithm redefines the amendment factor and reduces the cumulative error caused by the hop counts in the traditional algorithm. A maximum distance similar link method based on a similar path search is proposed to find the most similar known node path pair from the target node to the noncoplanar known node link and correct the hop size between multihop counts. The sparrow search algorithm is improved by using the k -means clustering and sine cosine search strategy, which solves the problem that the traditional sparrow algorithm is easy to fall into the local optimum, accelerates the convergence speed, corrects the position deviation of the target node, and improves the positioning accuracy. Experiments demonstrate that the 3D-HCSSA algorithm can improve positioning accuracy and reduce energy consumption. Compared with the 3DDV-hop algorithm, 3D-GAIDV-hop algorithm, and HCLSO-3D algorithm, the 3D-HCSSA positioning accuracy is significantly improved.

Proximity-Distance Mapping and Jaya Optimization Algorithm Based on Localization for Wireless Sensor Network

Aiming at the problem of large location errors of traditional ranging-free algorithms in Wireless Sensor Network (WSN), a novel node location algorithm based on proximity-distance mapping (PDM) and Jaya optimization was proposed. In this algorithm, proximity and Euclidean distance are extracted from the relationship of anchor nodes to construct a mapping matrix by using the idea of PDM. It is calculated by using the mapping matrix that the estimated distance from the unknown node to the anchor node can be used for the subsequent calculations. After the estimated distance is obtained, the Jaya optimization algorithm is imported to calculate the location of the unknown one. To accelerate the convergence and enhance the accuracy of the algorithm, the idea of a boundary box is used to limit the initial feasible region of unknown nodes. The experiment results show that the PDM–Jaya algorithm has better positioning accuracy than the original PDM in the same condition.