PCLOS based fractional-order sliding mode stochastic path following control for underactuated marine vehicles with multiple disturbances and constraints.

This paper investigates the stochastic path following control of underactuated marine vehicles (UMVs) subject to multiple disturbances and constraints. Firstly, the complex marine environment in which UMVs navigate typically contains stochastic components, thus the multiple disturbances are categorized as slow-varying deterministic disturbances and stochastic disturbances. Secondly, a position-constrained line-of-sight (PCLOS) based fractional-order sliding mode stochastic (FSMS) control strategy is established to achieve path following control of UMVs. A PCLOS guidance law based on universal barrier Lyapunov function is proposed to ensure that the position errors remain within the constraint ranges, which is versatile for systems with symmetric constraints or without constraints. An FSMS controller based on fractional-order theory and sliding mode control is designed to improve the dynamic response speed of the system and effectively attenuate chattering phenomenon. A stochastic disturbance observer is developed to estimate the slow-varying deterministic disturbances in the stochastic system, and auxiliary dynamic compensators are used to mitigate the impact of input constraints. Lastly, theoretical analysis indicates that the closed-loop system is stable and the position constraint requirements are satisfied. Comparative simulations illustrate the effectiveness of the proposed control strategy.

Finite-Time Line-of-Sight Guidance-Based Path-Following Control for a Wire-Driven Robot Fish.

This paper presents an adaptive line-of-sight (LOS) guidance method, incorporating a finite-time sideslip angle observer to achieve precise planar path tracking of a bionic robotic fish driven by LOS. First, an adaptive LOS guidance method based on real-time cross-track error is presented. To mitigate the adverse effects of the sideslip angle on tracking performance, a finite-time observer (FTO) based on finite-time convergence theory is employed to observe the time-varying sideslip angle and correct the target yaw. Subsequently, classical proportional-integral-derivative (PID) controllers are utilized to achieve yaw tracking, followed by static and dynamic yaw angle experiments for evaluation. Finally, the yaw-tracking-based path-tracking control strategy is applied to the robotic fish, whose motion is generated by an improved central pattern generator (CPG) and equipped with a six-axis inertial measurement unit for real-time swimming direction. Quantitative comparisons in tank experiments validate the effectiveness of the proposed method.

Influence of cranium orientation on cervical sagittal alignment during radiographic examination: a radiographic analysis.

BACKGROUND CONTEXT: During the radiographic examination, the cranium orientation varies not only individually but also within the same subject, in different imaging sessions. Knowing how changes in the orientation of the cranium influences cervical sagittal alignment during the radiographic examination of the cervical spine can aid clinicians in the accurate evaluation for cervical sagittal alignment in clinical practice. PURPOSE: To radiographically examine the influence of cranium orientation on cervical sagittal alignment during radiographic examination in an asymptomatic cohort. STUDY DESIGN: A prospective radiographic study. PATIENT SAMPLE: Eighty asymptomatic volunteers (mean age, 40.4 years; 50.0% male) were enrolled. OUTCOME MEASURES: Cervical sagittal parameters including the regional slope (C1 slope, C2 slope, C5 slope, C7 slope, and T1 slope), Cobb angle (O-C1 angle, C1-C2 angle, C2-C5 angle, C5-C7 angle, and C7-T1 angle), and cranial/cervical offset (sella turcica tilt [ST tilt] and C2 tilt). METHODS: In all participants, standing lateral radiographs of the cervical spine were taken in 3 forward-gazing positions: anteverted-cranium (AC) position; neutral-cranium (NC) position; and retroverted-cranium (RC) position. Cervical sagittal parameters, including the regional slope, Cobb angle, and cranial/cervical offset, in these 3 positions were statistically compared. RESULTS: The C1 and C2 slopes were anteverted and retroverted in the AC and RC positions, respectively, compared to those in the NC position. The C5 slope, C7 slope, and T1 slope were constant among the 3 positions. In O-C2 and C2-C5, statistically significant differences in the regional Cobb angles were identified among the 3 positions; however, there were no significant differences in the C5-C7 or C7-T1 segments. Cranial and cervical offsets of ST tilt and C2 tilt increased and decreased when the cranium was anteverted and retroverted, respectively. CONCLUSIONS: The current study suggests that the adjustment of the cranium orientation when taking cervical spine radiographs is mainly controlled at the upper cervical spine of the O-C2 segment in an asymptomatic cohort. On radiograph, alignment in the upper cervical segment of O-C2 changes; accordingly, the middle cervical segment of C2-C5 can change during the adjustment of cranium orientation. However, alignment in the lower cervical segment of C5-C7 and the cervicothoracic junction of C7-T1 remains constant. Further, cranial/cervical offset increases and decreases when the cranium is anteverted and retroverted, respectively. Our results can help the accurate evaluation of cervical sagittal alignment on plain radiographs in clinical practice.

A Combined UWB/IMU Localization Method with Improved CKF.

Aiming at the problem that ultra-wide band (UWB) cannot be accurately localized in environments with large noise variations and unknown statistical properties, a combinatorial localization method based on improved cubature (CKF) is proposed. First, in order to overcome the problem of inaccurate local approximation or even the inability to converge due to the initial value not being set near the optimal solution in the process of solving the UWB position by the least-squares method, the Levenberg-Marquardt algorithm (L-M) is adopted to optimally solve the UWB position. Secondly, because UWB and IMU information are centrally fused, an adaptive factor is introduced to update the measurement noise covariance matrix in real time to update the observation noise, and the fading factor is added to suppress the filtering divergence to achieve an improvement for the traditional CKF algorithm. Finally, the performance of the proposed combined localization method is verified by field experiments in line-of-sight (LOS) and non-line-of-sight (NLOS) scenarios, respectively. The results show that the proposed method can maintain high localization accuracy in both LOS and NLOS scenarios. Compared with the Extended Kalman filter (EKF), unbiased Kalman filter (UKF), and CKF algorithms, the localization accuracies of the proposed method in NLOS scenarios are improved by 25.2%, 18.3%, and 11.3%, respectively.

A Study on the Maximum Reliability of Multi-UAV Cooperation Relay Systems.

This paper studies the maximum reliability of multi-hop relay UAVs, in which UAVs provide wireless services for remote users as a coded cooperative relay without an end-to-end direct communication link. In this paper, the analytical expressions of the total power loss and total bit error rate are derived as reliability measures. First, based on the environmental statistical parameters, a LOS probability model is proposed. Then, the problem of minimizing the bit error rate of static and mobile UAVs is studied. The goal is to minimize the total bit error rate by jointly optimizing the height, elevation, power and path loss and introducing the maximum allowable path loss constraints, transmission power allocation constraints, and UAV height and elevation constraints. At the same time, the total path loss is minimized to achieve maximum ground communication coverage. However, the formulated joint optimization problem is nonconvex and generally difficult to solve. Therefore, we decomposed the problem into two subproblems and proposed an effective joint optimization iteration algorithm. Finally, the simulation results are given, and the analysis shows that the optimal height of different reliability measures is slightly different; thus, using the mobility of UAVs can improve the reliability of communication performance.

A Post-Processing Multipath/NLoS Bias Estimation Method Based on DBSCAN.

Positioning based on Global Navigation Satellite Systems (GNSSs) in urban environments always suffers from multipath and Non-Line-of-Sight (NLoS) effects. In such conditions, the GNSS pseudorange measurements can be affected by biases disrupting the GNSS-based applications. Many efforts have been devoted to detecting and mitigating the effects of multipath/NLoS, but the identification and classification of such events are still challenging. This research proposes a method for the post-processing estimation of pseudorange biases resulting from multipath/NLoS effects. Providing estimated pseudorange biases due to multipath/NLoS effects serves two main purposes. Firstly, machine learning-based techniques can leverage accurately estimated pseudorange biases as training data to detect and mitigate multipath/NLoS effects. Secondly, these accurately estimated pseudorange biases can serve as a benchmark for evaluating the effectiveness of the methods proposed to detect multipath/NLoS effects. The estimation is achieved by extracting the multipath/NLoS biases from pseudoranges using a clustering algorithm named Density-Based Spatial Clustering of Applications with Noise (DBSCAN). The performance is demonstrated using two real-world data collections in multipath/NLoS scenarios for both static and dynamic conditions. Since there is no ground truth for the pseudorange biases due to the multipath/NLoS scenarios, the proposed method is validated based on the positioning performance. Positioning solutions are computed by subtracting the estimated biases from the raw pseudoranges and comparing them to the ground truth.

Ultra-Wideband Ranging Error Mitigation with Novel Channel Impulse Response Feature Parameters and Two-Step Non-Line-of-Sight Identification.

The effective identification and mitigation of non-line-of-sight (NLOS) ranging errors are essential for achieving high-precision positioning and navigation with ultra-wideband (UWB) technology in harsh indoor environments. In this paper, an efficient UWB ranging-error mitigation strategy that uses novel channel impulse response parameters based on the results of a two-step NLOS identification, composed of a decision tree and feedforward neural network, is proposed to realize indoor locations. NLOS ranging errors are classified into three types, and corresponding mitigation strategies and recall mechanisms are developed, which are also extended to partial line-of-sight (LOS) errors. Extensive experiments involving three obstacles (humans, walls, and glass) and two sites show an average NLOS identification accuracy of 95.05%, with LOS/NLOS recall rates of 95.72%/94.15%. The mitigated LOS errors are reduced by 50.4%, while the average improvement in the accuracy of the three types of NLOS ranging errors is 61.8%, reaching up to 76.84%. Overall, this method achieves a reduction in LOS and NLOS ranging errors of 25.19% and 69.85%, respectively, resulting in a 54.46% enhancement in positioning accuracy. This performance surpasses that of state-of-the-art techniques, such as the convolutional neural network (CNN), long short-term memory-extended Kalman filter (LSTM-EKF), least-squares-support vector machine (LS-SVM), and k-nearest neighbor (K-NN) algorithms.

An Improved Genetic Algorithm for the Recovery System of USVs Based on Stern Ramp Considering the Influence of Currents.

With the progression of marine exploration and exploitation, as well as the advancements in mechanical intelligence, the utilization of the unmanned surface vehicle (USV) and the design of their guidance system have become prominent areas of focus. However, the stern ramp recovery of the USV is still in its infancy due to its unique attitude requirements and automation design. Furthermore, few studies have addressed the impact of maritime disturbances, with most research limited to simulations. To enhance the efficiency and accuracy of stern ramp recovery, this paper presents the development and construction of a novel recovery system. By incorporating physical modeling of disturbance forces acting on USVs at sea, the practicality of the system is improved. Additionally, an optimized genetic algorithm is introduced in the navigation module to improve convergence rates and subsequently enhance recovery efficiency. A line-of-sight (LOS) algorithm based on average velocity is proposed in this paper to ensure the attainment of unique attitude requirements and to improve the effectiveness of stern chute recovery. This paper provides a detailed description of the independently designed USV hardware system. Moreover, simulations and practical experiments conducted using this experimental platform are presented, offering a new solution for the USV's stern ramp recovery.

Challenges of 3D Surface Reconstruction in Capsule Endoscopy.

Essential for improving the accuracy and reliability of bowel cancer screening, three-dimensional (3D) surface reconstruction using capsule endoscopy (CE) images remains challenging due to CE hardware and software limitations. This report generally focuses on challenges associated with 3D visualization and specifically investigates the impact of the indeterminate selection of the angle of the line-of-sight on 3D surfaces. Furthermore, it demonstrates that impact through 3D surfaces viewed at the same azimuth angles and different elevation angles of the line-of-sight. The report concludes that 3D printing of reconstructed 3D surfaces can potentially overcome line-of-sight indeterminate selection and 2D screen visual restriction-related errors.

Robust design of a machine learning-based GNSS NLOS detector with multi-frequency features.

The robust detection of GNSS non-line-of-sight (NLOS) signals is of vital importance for land- and close-to-land-based safe navigation applications. The usage of GNSS measurements affected by NLOS can lead to large unbounded positioning errors and loss of safety. Due to the complex signal conditions in urban environments, the use of machine learning or artificial intelligence techniques and algorithms has recently been identified as potential tools to classify GNSS LOS/NLOS signals. The design of machine learning algorithms with GNSS features is an emerging field of research that must, however, be tackled carefully to avoid biased estimation results and to guarantee algorithms that can be generalized for different scenarios, receivers, antennas, and their specific installations and configurations. This work first provides new options to guarantee a proper generalization of trained algorithms by means of a pre-normalization of features with models extracted in open-sky (nominal) scenarios. The second main contribution focuses on designing a branched (or parallel) machine learning process to handle the intermittent presence of GNSS features in certain frequencies. This allows to exploit measurements in all available frequencies as compared to current approaches in the literature based on only the single frequency. The detection by means of logistic regression not only provides a binary LOS/NLOS decision but also an associated probability which can be used in the future as a means to weight-specific measurements. The detection with the proposed branched logistic regression with pre-normalized multi-frequency features has shown better results than the state-of-the-art algorithms, reaching 90% detection accuracy in the validation scenarios evaluated.

An Improved UWB/IMU Tightly Coupled Positioning Algorithm Study.

The combination of ultra-wide band (UWB) and inertial measurement unit (IMU) positioning is subject to random errors and non-line-of-sight errors, and in this paper, an improved positioning strategy is proposed to address this problem. The Kalman filter (KF) is used to pre-process the original UWB measurements, suppressing the effect of range mutation values of UWB on combined positioning, and the extended Kalman filter (EKF) is used to fuse the UWB measurements with the IMU measurements, with the difference between the two measurements used as the measurement information. The non-line-of-sight (NLOS) measurement information is also used. The optimal estimate is obtained by adjusting the system measurement noise covariance matrix in real time, according to the judgment result, and suppressing the interference of non-line-of-sight factors. The optimal estimate of the current state is fed back to the UWB range value in the next state, and the range value is dynamically adjusted after one-dimensional filtering pre-processing. Compared with conventional tightly coupled positioning, the positioning accuracy of the method in this paper is improved by 46.15% in the field experimental positioning results.

TDOA-Based Target Tracking Filter While Reducing NLOS Errors in Cluttered Environments.

We consider tracking a moving target in a wireless communication system that is based on the radio signal. Considering a bounded workspace with many unknown obstacles, we handle tracking a non-cooperative transmitter using multiple signal receivers. Here, a non-cooperative transmitter is a transmitter whose signal emission time is not known in advance. We consider a time difference of arrival (TDOA) location problem, which locates the transmitter by processing the signal measurement time at multiple receivers. In tracking a non-cooperative transmitter, non-line-of-sight (NLOS) errors occur if obstacles block the LOS line connecting the receiver and the moving transmitter. Our article addresses how to track a moving transmitter while decreasing the NLOS error in TDOA-only measurements. We propose an algorithm to localize a transmitter while decreasing the NLOS error in TDOA measurements. For tracking a moving transmitter in real time, we integrate the proposed localization algorithm and the interacting multiple model Kalman filter (IMM KF). As far as we know, our article is novel in tracking a moving transmitter based on TDOA-only measurements in an unknown mixed LOS/NLOS workspace. We show that the proposed filter considerably decreases the NLOS errors in TDOA-only measurements while running fast. Therefore, the proposed tracking scheme is suitable for tracking a moving transmitter in real time. Through MATLAB simulations, we show that the proposed filter outperforms other state-of-the-art TDOA filters, considering both time efficiency and tracking accuracy.

Spatial Multi-Source Information Fusion Localization Algorithm in Non-Line-of-Sight Environments.

In this paper, a high-accuracy localization problem under a complex non-line-of-sight (NLOS) condition is addressed by a new method that utilizes multiple NLOS paths to improve localization accuracy, as opposed to the traditional method of suppressing them. The spatial multi-path array fusion localization model is presented and analyzed, followed by an angle-of-arrival (AOA) and time-of-arrival (TOA) algorithm based on spatial multi-information fusion that seeks to improve localization accuracy. Multi-path of spatial signals, measurement of the multi-element antenna, and geographic environment information are integrated into the proposed method for localization optimization. Simulation experiments were carried out, and the results revealed that the proposed algorithm is capable of making full use of spatial multi-location information for localization, thus improving the accuracy of localization in a the NLOS environment effectively and increasing the locatable probability of complex environment localization applications.

NN-LCS: Neural Network and Linear Coordinate Solver Fusion Method for UWB Localization in Car Keyless Entry System.

Nowadays, ultra-wideband (UWB) technology is becoming a new approach to localize keyfobs in the car keyless entry system (KES), because it provides precise localization and secure communication. However, for vehicles the distance ranging suffers from great errors because of none-line-of-sight (NLOS) which is raised by the car. Regarding the NLOS problem, efforts have been made to mitigate the point-to-point ranging error or to estimate the tag coordinate by neural networks. However, it still suffers from some problems such as low accuracy, overfitting, or a large number of parameters. In order to address these problems, we propose a fusion method of a neural network and linear coordinate solver (NN-LCS). We use two FC layers to extract the distance feature and received signal strength (RSS) feature, respectively, and a multi-layer perceptron (MLP) to estimate the distances with the fusion of these two features. We prove that the least square method which supports error loss backpropagation in the neural network is feasible for distance correcting learning. Therefore, our model is end-to-end and directly outputs the localization results. The results show that the proposed method is high-accuracy and with small model size which could be easily deployed on embedded devices with low computing ability.

Research on the Line of Sight Stabilization Control Technology of Optronic Mast under High Oceanic Condition and Big Swaying Movement of Platform.

To realize high-performance line of sight (LOS) stabilization control of the optronic mast under high oceanic conditions and big swaying movements of platforms, a composite control method based on an adaptive radial basis function neural network (RBFNN) and sliding mode control (SMC) is proposed. The adaptive RBFNN is used to approximate the nonlinear and parameter-varying ideal model of the optronic mast, so as to compensate for the uncertainties of the system and reduce the big-amplitude chattering phenomenon caused by excessive switching gain in SMC. The adaptive RBFNN is constructed and optimized online based on the state error information in the working process; therefore, no prior training data are required. At the same time, a saturation function is used to replace the sign function for the time-varying hydrodynamic disturbance torque and the friction disturbance torque, which further reduce the chattering phenomenon of the system. The asymptotic stability of the proposed control method has been proven by the Lyapunov stability theory. The applicability of the proposed control method is validated by a series of simulations and experiments.

Transceiver Optimization for mmWave Line-of-Sight MIMO Systems Using Hybrid Arrays.

The performance of millimeter wave (mmWave) line-of-sight multiple input multiple output (LOS MIMO) systems using hybrid arrays of planar subarrays was studied. We characterized the achievable maximum spatial multiplexing gain for such LOS MIMO systems by the measures of spectral efficiency and effective degree of freedom (EDoF). By proposing a joint plane-wave and spherical-wave-based general 3D channel model, we derived the optimal design parameters in the analog domain, i.e., the optimal subarray separation products, and analyzed their sensitivity on the system performance. We also gave analytical eigenvalue expressions of the equivalent LOS MIMO channel matrix, which are applicable to the case of a non-optimal design, as well as the upper and lower bounds of the EDoF for system performance evaluation. A piecewise uniform quantization codebook was further designed for quantizing phase shifter values in practical applications. The numerical and simulation results show that planar subarrays are superior to traditional arrays in terms of spectral efficiency and EDoF in Ricean fading channels because they are more robust to the change in the communication distance and the deviation from the optimal design. The use of hybrid arrays of planar subarrays effectively removes the limitation of mmWave LOS MIMO systems using traditional arrays, through which, the conventional Rayleigh distance criterion has to be satisfied to achieve the optimal performance.

Sensor Fusion with Asynchronous Decentralized Processing for 3D Target Tracking with a Wireless Camera Network.

We present a method to acquire 3D position measurements for decentralized target tracking with an asynchronous camera network. Cameras with known poses have fields of view with overlapping projections on the ground and 3D volumes above a reference ground plane. The purpose is to track targets in 3D space without constraining motion to a reference ground plane. Cameras exchange line-of-sight vectors and respective time tags asynchronously. From stereoscopy, we obtain the fused 3D measurement at the local frame capture instant. We use local decentralized Kalman information filtering and particle filtering for target state estimation to test our approach with only local estimation. Monte Carlo simulation includes communication losses due to frame processing delays. We measure performance with the average root mean square error of 3D position estimates projected on the image planes of the cameras. We then compare only local estimation to exchanging additional asynchronous communications using the Batch Asynchronous Filter and the Sequential Asynchronous Particle Filter for further fusion of information pairs' estimates and fused 3D position measurements, respectively. Similar performance occurs in spite of the additional communication load relative to our local estimation approach, which exchanges just line-of-sight vectors.

Machine-Learning-Based LOS Detection for 5G Signals with Applications in Airport Environments.

The operational costs of the advanced Air Traffic Management (ATM) solutions are often prohibitive in low- and medium-sized airports. Therefore, new and complementary solutions are currently under research in order to take advantage of existing infrastructure and offer low-cost alternatives. The 5G signals are particularly attractive in an ATM context due to their promising potential in wireless positioning and sensing via Time-of-Arrival (ToA) and Angle-of-Arrival (AoA) algorithms. However, ToA and AoA methods are known to be highly sensitive to the presence of multipath and Non-Line-of-Sight (NLOS) scenarios. Yet, LOS detection in the context of 5G signals has been poorly addressed in the literature so far, to the best of the Authors' knowledge. This paper focuses on LOS/NLOS detection methods for 5G signals by using both statistical/model-driven and data-driven/machine learning (ML) approaches and three challenging channel model classes widely used in 5G: namely Tapped Delay Line (TDL), Clustered Delay Line (CDL) and Winner II channel models. We show that, with simulated data, the ML-based detection can reach between 80% and 98% detection accuracy for TDL, CDL and Winner II channel models and that TDL is the most challenging in terms of LOS detection capabilities, as its richness of features is the lowest compared to CDL and Winner II channels. We also validate the findings through in-lab measurements with 5G signals and Yagi and 3D-vector antenna and show that measurement-based detection probabilities can reach 99-100% with a sufficient amount of training data and XGBoost or Random Forest classifiers.

Change in Line of Sight after Corrective Surgery of Adult Spinal Deformity Patients: A 2-Year Follow-up.

STUDY DESIGN: Cohort study. PURPOSE: There is currently no published study that focuses on the spinal corrective surgery effects with cranial parameters in adult spinal deformity (ASD) patients. It is an important factor to measure since it plays a critical role in maintaining the line of sight. The objective is to determine the change in cranial parameters using the slope of McGregor's line (McGS) after ASD surgery after 2 years of follow-up. OVERVIEW OF LITERATURE: A study concluded that cervical spine alignment (C2-C7 lordosis) is strongly affected by thoracic kyphosis (TK). Another study showed that patients with ascending gaze had significantly more thoracolumbar malalignment. METHODS: This retrospective study includes 295 corrective surgery patients with ASD. Subjects were divided into two groups after propensity age matching analysis: cranial malalignment (McGS <-8 or >13) and normal cranial alignment (-8≤ McGS ≤13). Lumbar lordosis (LL), pelvic tilt (PT), TK, cervical lordosis (CL), and sagittal vertical axis (SVA) were evaluated between the two groups. RESULTS: SVA (95-56 mm) and PT (34°-25°) decreased and LL (19°-41°) increased 2 years after surgery (p <0.05), but McGS (-1.1° to -0.5°) and CL (21°-19°) did not change. Conversely, in the group with cranial malalignment, SVA (120-64 mm), PT (35°-26°), and LL (12°-41°) showed similar results to the normal cranial parameter group 2 years after surgery, but in contrast, McGS (-13° to -2°) and CL (24°-18°) improved significantly. CONCLUSIONS: Severe ASD adversely affects to maintain horizontal gaze but can be improved by spinal corrective surgery.

An Improved TDOA Method for Land-Based Long-Range HF Skywave Source Geolocation and Experimental Results.

The real-time information of the unknown ionospheric environments is difficult to obtain, plaguing the timely and accurate geolocation of high frequency (HF) sources. In this paper, we propose an improved HF skywave source geolocation method based on the time-difference-of-arrival (TDOA) with the semidefinite programming (SDP), and model HF signal propagation paths as paths with significant non-line-of-sight (NLOS) biases. With this method, no priori information about the ionosphere, especially the priori ionospheric virtual heights of reflection, is necessary while timely and accurately geolocating the HF sources. Furthermore, we use the ray tracing technique and build a 3D ionospheric electron density gridded matrix model to simulate realistic HF signal propagation paths. In the simulations, the proposed method is compared with existing methods, and detailed geolocation error distribution maps are given. In the experiments, HF I/Q data captured from different types of HF transmitters are located by six receivers with time synchronization. Simulated and experimental results show that the proposed method improves the positioning accuracy by about 50% compared with existing methods under the same conditions, and the average relative positioning error is less than 2.7%.