Position Root Mean Square Error Research Articles

Improved Fingerprint-Based Localization Based on Sequential Hybridization of Clustering Algorithms

The localization accuracy of a fingerprint-based localization system is dependent on several factors, one of which is the accuracy and efficiency at which the fingerprint database is clustered. Most highly efficient and accurate clustering algorithms have high time-dependent computational complexity (CC), which tends to limit their practical applicability. A technique that has yet to be explored is the sequential hybridization of multiple low-time CC clustering algorithms to produce a single moderate-time CC clustering algorithm with high localization accuracy. As a result, this paper proposes a clustering algorithm with a moderate time CC that is based on the sequential hybridization of the closest access point (CAP) and improved k-means clustering algorithms. The performance of the proposed sequential hybrid clustering algorithm is determined and compared to the modified affinity propagation clustering (m-APC), fuzzy c-mean (FCM), and 2-CAP algorithms presented in earlier research works using four experimentally generated and publicly available fingerprint databases. The performance metrics considered for the comparisons are the position root mean square error (RMSE) and clustering time based on big O notation. The simulation results show that the proposed sequential hybrid clustering algorithm has improved localization accuracy with position RMSEs of about 54%, 77%, and 52%, respectively, higher than those of the m-APC, FCM, and 2-CAP algorithms. In terms of clustering time, it is 99% and 79% faster than the m-APC and FCM algorithms, respectively, but 90% slower than the 2-CAP algorithm. The results have shown that it is possible to develop a clustering algorithm that has a moderate clustering time with very high localization accuracy through sequential hybridization of multiple clustering algorithms that have a low clustering time with poor localization accuracy. Doi: 10.28991/ESJ-2024-08-02-02 Full Text: PDF

Enhancing Real-Time Kinematic Relative Positioning for Unmanned Aerial Vehicles

Real-time kinematic (RTK) positioning of the global navigation satellite systems (GNSS) is used to provide centimeter-level positioning accuracy. There are several ways to implement RTK but a Kalman filter-based RTK is preferred because of its superior capability to resolve GNSS carrier phase integer ambiguities. However, the positioning performance of the Kalman filter-based RTK is often compromised by various factors when it comes to determining a precise relative position vector between moving unmanned aerial vehicles (UAVs) equipped with low-cost GNSS receivers and antennas, where the locations of both GNSS antennas are not accurately known and change over time. Some of the critical factors that lead to a high rate of incorrect resolutions of carrier phase integer ambiguities are measurement time differences between GNSS receivers, frequent cycle slips with high noise in code and carrier phase measurements, and an improper Kalman filter gain due to a newly risen satellite. In this paper, effective methods to deal with those factors to achieve a seamless Kalman filter-based RTK performance in moving UAVs are presented. Using our extensive 45 flight tests data sets, conducted over a duration of 3 to 12 min, the RTK positioning results showed that the root-mean-square position error (RMSE) decreased by up to 95.13%, with an average of 65.31%, and that the percentage of epochs that passed the ratio test, which is the most common method for validating double differenced carrier phase integer ambiguity resolution, increased by up to 130%, with an average of 23.54%.

Efficient Transmitter Selection Strategies for Improved Information Gathering of Aerial Vehicle Navigation in GNSS-Denied Environments

Aerial vehicle navigation in global navigation satellite system (GNSS)-denied environments by utilizing pseudorange measurements from <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">M</i> terrestrial signals of opportunity (SOPs) is considered. To this end, the aerial vehicle is tasked with choosing <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">K</i> < <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">M</i> most informative terrestrial SOPs. Two computationally efficient, but sub-optimal, transmitter selection strategies are proposed. These selection strategies, termed opportunistic greedy selection (OGS) and one-shot selection (OSS), exploit the additive, iterative properties of the Fisher information matrix (FIM), where OGS selects the most informative transmitters in finite iterations, while the OSS selects in one-iteration. Monte Carlo simulation results are presented comparing the OGS and OSS strategies versus the optimal (exhaustive search) selection strategy, where it is concluded that OGS performs closely to the optimal selection, while executing in a fraction of the optimal selection's time. Experimental results are presented of a U.S. Air Force high-altitude aircraft navigating without GNSS signals in (i) a rural region and (ii) a semi-urban region. The performance of the aircraft's navigation solution with the selected SOP transmitters via optimal, OGS, OSS are compared over a flight segment where the selection remained valid. The position root mean-squared error (RMSE) with the optimal, OGS, and OSS were 4.53 m, 6.28 m, 7.13 m in the rural region; and 5.83 m, 6.08 m, 6.70 m in the semi-urban region for an aircraft traversing a trajectory of 1.48 km and 1.22 km, respectively.

Performance of Position, Force, and Impedance Controllers for a Pneumatic Cylinder Ankle Exoskeleton

Abstract The best exoskeleton control strategy depends on the task, motivating the development and analysis of an exoskeleton capable of position, force, and impedance control. In this paper, we implement and compare the three controller types via sliding mode control on a custom-built pneumatic cylinder ankle exoskeleton. To evaluate each controller's performance, the exoskeleton was tested on the benchtop and with human subject experiments. With the position controller, the exoskeleton achieved an root-mean-square error (RMSE) of under 5 deg for both the benchtop and human tests. It had a bandwidth of approximately 12 rad/s. The force controller tracked sinusoidal trajectories acceptably well at low frequencies (15% of force range at 6 rad/s), with a bandwidth of approximately 24 rad/s. The error was significantly higher (RMSE of 24% of force range) for the more biologically relevant trajectory. The impedance controller demonstrated the desired spring-like behavior, with position RMSE generally under 5 deg compared to expected position. All three controllers worked equally well for benchtop and human tests. Thus, a pneumatically powered ankle exoskeleton with sliding mode control is capable of accurate position and impedance control and is capable of marginally acceptable force control.

Cognitive Detection of Unknown Beacons of Terrestrial Signals of Opportunity for Localization

A cognitive approach is proposed to detect unknown beacons of terrestrial signals of opportunity (SOPs). Two scenarios are considered in the paper: (i) detection of unknown beacons with integer constraints (IC) and (ii) detection of unknown beacons with no integer constraint (NIC). An example of beacons with IC is the pseudo-noise (PN) sequences in cellular code division multiple access (CDMA) signals. On the other hand, the reference signals (RSs) in orthogonal frequency-division multiplexing (OFDM)-based systems can be considered as beacons signals with NIC. Matched subspace detectors are proposed for both scenarios, and it is shown experimentally that the proposed matched subspace detectors are capable of detecting cellular third-generation (3G) cdma2000 signals and fifth-generation (5G) OFDM signals. A low complexity method is derived to simplify the matched subspace detector with IC for <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">M</i> -ary phase shift keying ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">M</i> PSK) modulation. The effect of symbol errors in the estimated beacon signal on the carrier to noise ratio (CNR) is characterized analytically. Closed-form expressions for the asymptotic probability of detection and false alarm are derived. Experimental results are presented showing an application of the proposed cognitive approach by enabling an unmanned aerial vehicle (UAV) to detect and exploit terrestrial cellular signals for navigation purposes. In one experiment, the UAV achieved submeter-level accurate navigation over a trajectory of 1.72 km, by exploiting signals from four 3G cdma2000 transmitters. In another experiment, the UAV achieves a position root mean-squared error (RMSE) of 4.63 m over a trajectory of 416 m, by exploiting signals from two 5G transmitters.

Flight Demonstration of High Altitude Aircraft Navigation With Cellular Signals

This article presents the first demonstration of navigation with cellular signals of opportunity (SOPs) on a high-altitude aircraft. An extensive flight campaign was conducted by the Autonomous Systems Perception, Intelligence, and Navigation Laboratory in collaboration with the U.S. Air Force to sample ambient downlink cellular SOPs in different regions in Southern California, USA. Carrier phase measurements were produced from these signals, which were subsequently fused in an extended Kalman filter along with altimeter measurements to estimate the aircraft’s state (position, velocity, and time). Three flights are performed in three different regions: 1) rural, 2) semiurban, and 3) urban. A multitude of flight trajectories and altitudes above ground level (AGL) was exercised in the three flights: 1) a 51-km trajectory of grid maneuvers with banking and straight segments at about 5,000 ft AGL, 2) a 57-km trajectory of a teardrop descent from 7,000 ft AGL down to touchdown at the runway, and 3) a 55-km trajectory of a holding pattern at about 15,000 ft AGL. The estimated aircraft trajectory is computed for each flight and compared with the trajectory from the aircraft’s onboard navigation system, which utilized a GPS receiver coupled with an inertial navigation system and an altimeter. The cellular SOPs produced remarkable sustained navigation accuracy over the entire flight trajectories in all three flights, achieving a 3D position root mean-squared error of 10.53 m, 4.96 m, and 15.44 m, respectively.

Performance-Driven Design of Carrier Phase Differential Navigation Frameworks With Megaconstellation LEO Satellites

A navigation framework with carrier phase differential measurements from megaconstellation low Earth orbit (LEO) satellite signals is developed. The measurement errors due to ephemeris errors and ionospheric and tropospheric delays are derived and the statistics of the dilution of precision is characterized. Moreover, the joint probability density function of the megaconstellation LEO satellites' azimuth and elevation angles is derived to (i) enable performance characterization of navigation frameworks with LEO satellites in computationally efficient way and (ii) facilitate parameter design, namely, the differential baseline, to meet desired performance requirements. The Starlink constellation is used as a specific LEO megaconstellation example to demonstrate the developed carrier phase differential LEO (CD-LEO) navigation framework. Simulation results are presented demonstrating the efficacy of the proposed CD-LEO framework for an unmanned aerial vehicle (UAV) navigating for 15.1 km in 300 seconds, while using signals from 44 Starlink satellites, achieving a three-dimensional (3-D) position root mean squared error (RMSE) of 2.2 m and a 2-D RMSE of 32.4 cm. Experimental results are presented showing UAV navigating for 2.28 km in 2 minutes over Aliso Viejo, California, USA, using exclusively signals from only two Orbcomm LEO satellites, achieving an unprecedented position RMSE of 14.8 m.

Differential Framework for Submeter-Accurate Vehicular Navigation With Cellular Signals

A framework that could achieve submeter-level-accurate horizontal navigation with carrier phase differential measurements from cellular signals is developed. This framework, termed CD-cellular, is composed of a base and a rover in a cellular environment, both making carrier phase measurements to the same cellular base transceiver stations (BTSs). The base shares its carrier phase measurements with the mobile rover, which in turn employs an extended Kalman filter to obtain a coarse estimate of its states, followed by a batch weighted nonlinear least squares (B-WNLS) estimator to solve for the integer ambiguities, and finally a point-solution WNLS to estimate its own states. The framework is designed to guarantee that after some time, the rover's position error remains below a pre-defined threshold with a desired probability. This is achieved by leveraging models of the BTS positions from stochastic geometry. Experimental results on an unmanned aerial vehicle (UAV) in an open semi-urban environment with multipath-free, line-of-sight (LOS) conditions are presented, showing that the developed framework achieves a 70.48 cm position root mean-squared error (RMSE) over a trajectory of 2.24 km, measured with respect to the UAV's navigation solution from its onboard GPS-inertial navigation system (INS).

A 5-D Large-Workspace Magnetic Localization and Actuation System Based on an Eye-in-Hand Magnetic Sensor Array and Mobile Coils

Simultaneous magnetic localization and actuation in a human-scale workspace are challenging but essential for medical applications such as whole gastrointestinal (GI) tract monitoring and blood clot clearance in the leg. In this article, we propose a 5-D magnetic localization and actuation system that can track and actuate magnetic robots in a cylindrical workspace with a diameter of 500 mm. Based on the eye-in-hand configuration of the sensor array and the flexibility of the mobile electromagnetic manipulation system (mEMS), we realize a high localization accuracy in a large workspace with only 25 sensors. Moreover, we design an accurate magnetic field modeling method that can attenuate the electromagnets’ hysteresis and provide a precise actuation field estimation. Taking advantage of the mobile sensor array, we developed a signal-quality-based tracking strategy (STS) that could improve the localization performance by adjusting the movement of the sensor array. The effectiveness and advantages of the proposed methods are verified by simulations and experiments. Results show that the STS could decrease 25% position root-mean-square error (RMSE). The resulting position RMSE and orientation RMSE are 2.02 mm and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${\mathrm {8.24}}^{\circ} $ </tex-math></inline-formula> , respectively. We experimentally prove the viability of the proposed strategy by propelling a magnetic helical robot through a 400-mm long 3-D twisted silicone phantom.

I Am Not Afraid of the GPS Jammer: Resilient Navigation Via Signals of Opportunity in GPS-Denied Environments

I am not afraid of the GPS jammer, as long as there are ambient signals of opportunity (SOPs) to exploit in the environment. In environments where GPS signals are challenged (e.g., indoors and deep urban canyons) or denied (e.g., under jamming and spoofing attacks), SOPs could serve as an alternative positioning, navigation, and timing (PNT) source to GPS, and more generally, to global navigation satellite systems (GNSS). This article presents a radio simultaneous localization and mapping (radio SLAM) approach that enables the exploitation of SOPs for resilient and accurate PNT. Radio SLAM estimates the states of the navigator-mounted receiver simultaneously with the SOPs’ states. Radio SLAM could produce an SOP-derived navigation solution in a standalone fashion or by fusing SOPs with sensors [e.g., inertial measurement unit (IMU), lidar, etc.], digital maps, and/or other signals (e.g., GNSS). This article presents the first published experimental results evaluating the efficacy of radio SLAM in a real GPS-denied environment. These experiments took place at Edwards Air Force Base, California, USA, during which GPS was intentionally jammed with jamming-to-signal ratio as high as 90 dB. This article evaluates the timing of two cellular long-term evolution (LTE) SOPs located in the jammed environment, showing timing stability over 95 min of GPS jamming. Moreover, the article presents navigation results showcasing a ground vehicle traversing a trajectory of about 5 km in 180 s in the GPS-jammed environment. The vehicle's GPS-IMU system drifted from the vehicle's ground truth trajectory, resulting in a position root mean-squared error (RMSE) of 238 m. In contrast, the radio SLAM approach with a single cellular LTE SOP whose position was poorly known (an initial uncertainty on the order of several kilometers) achieved a position RMSE of 32 m.

A Pedestrian Indoor Navigation System Using Deep-Learning-Aided Cellular Signals and ZUPT-Aided Foot-Mounted IMUs

A signal-sensor-based indoor pedestrian navigation system is developed. The proposed system: 1) utilizes a foot-mounted inertial navigation system (INS), in which the accumulated errors are mitigated via a zero velocity update (ZUPT) approach; 2) exploits opportunistically cellular long-term evolution (LTE) signals in a deep neural network (DNN)-based synthetic aperture navigation (SAN) framework, in which the pedestrian’s motion is utilized to suppress multipath-induced errors. The proposed DNN-SAN-LTE-ZUPT-INS (DUALS) indoor pedestrian navigation system utilizes the complementary the desirable characteristics of both subsystems, coupled via two architectures: (a) loosely-coupled and (b) tightly-coupled. This paper designs and assesses both architectures experimentally in an indoor environment. The experimental study demonstrates a pedestrian traversing a trajectory of 600 m in 14 minutes, including a stationary period, straight segments, up/down the stairs, and riding in an elevator, while receiving signals from 4 LTE base stations (also known as evolved node Bs (eNodeBs)). The proposed tightly-coupled DUALS system exhibited a three-dimensional (3-D) position root mean-squared error (RMSE) of 1.34 m, outperforming the loosely-coupled DUALS, ZUPT-aided INS, and LTE-DNN-SAN, which achieved a position RMSE of 1.38, 1.49, and 1.97 m, respectively.

Distributed Fusion of Optimally Quantized Local Tracker Estimates for Underwater Wireless Sensor Networks

Multi-sensor underwater surveillance has been a significant research problem for civilian and naval applications. Due to limited bandwidth considerations, the underwater wireless sensor networks (UWSNs) use measurement quantization to transmit information from individual sensors to the fusion center to perform centralized tracking/fusion. However, at the measurement level, quantization of azimuth information is complex due to its non-linear behavior. To address this problem, this paper proposes to perform the distributed tracking and quantizing the local estimates (state and covariance) to provide improved bandwidth and reduce computational load. The local tracker estimates the updated state and covariance of a target’s time-varying dynamics in the given surveillance from the obtained measurements using extended Kalman filter (EKF) and global nearest neighbor (GNN) data association. The measurement model contains both detections of target and false alarms. This paper uses optimal quantization rather than linear quantization owing to its minimal bandwidth requirement. Once the quantized local tracks are obtained at the fusion center, these tracks are quantified using track-to-track association (T2TA) in the S-D assignment framework. The associated tracks are fused using correlation-free fusion algorithms like covariance intersect (CI), sampling covariance intersects (SCI), ellipsoidal intersect (EI), and arithmetic average (AA) algorithms to achieve the global track. The position root mean square error (PRMSE), bandwidth, and error ellipses are used to quantify the performance of the proposed framework. The simulation results show that the PRMSE of the optimally quantized fusion estimates yields good agreement with the unquantized method. Simulation results further reveals that, optimal quantization utilizes lower bandwidth compared to linear quantization. In addition, optimally quantized local estimates accomplishes promising covariance regions at the fusion center.

Sequential Fusion Based Approach for Estimating Range Gate Pull-Off Parameter in a Networked Radar System: An ECCM Algorithm

Networked radar is an emerging and effective alternative to traditional radar systems to provide improved performance by fusing information from multiple radars. Further, networked radar systems (NRS) have found numerous deployments in military and civilian infrastructures in recent years. Electronic countermeasures (ECM) like jamming, Range Gate Pull-Off (RGPO), and Velocity Gate Pull-Off (VGPO) generally pose a high risk to the radar systems by injecting intentional interference. This paper proposes networked radar to detect the RGPO ECM attack and estimate the range gate deception parameter of the deceived local track in an NRS. Each radar comprises a local tracker to provide the local estimates (updated state and updated covariance), and these estimates are then sent to the fusion node. Thereafter, a track-to-track association (T2TA) is formulated at the fusion node to detect the deceived tracks using all the available local tracks. For the deceived track, the pseudo-measurements are created using the inverse Kalman filter-based tracklets. All the local tracks except deceived track are compensated and sequentially fused to create a reference measurement. After that, the deception parameter of the deceived track is estimated by using pseudo-measurement and the reference measurement by employing the recursive least square estimator (RLSE). In addition, the proposed algorithm is analyzed for single and multiple RGPO based ECM scenarios. Further, the Cramer Rao Lower Bound (CRLB) for the proposed methodology is derived. The results are quantified with a Position Root Mean Square Error (PRMSE), CRLB, innovation test, normalized estimation error squared (NEES) test, and confidence interval. The simulation results demonstrate that the proposed estimation technique provides good performance in the presence of all the local tracks are being attacked by RGPO ECM. Besides, it is evident from the results that estimator efficiency is falling below the 5% tail probability of the chi-square distribution.

GNSS Spoofing Detection and Mitigation in Multireceiver Configuration via Tracklets and Spoofer Localization

Global navigation satellite systems (GNSS) sensors estimate its position, velocity, and time (PVT) using pseudorange measurements. When there is no interference, the pseudoranges are due to authentic satellites, and the bearings is distinguishable. Whereas, in the presence of any intentional interference source like spoofer, the pseudorange measurements owing to spurious signals and all the bearings from the same direction. These spurious attacks yield either no position or falsified position to the GNSS receiver. This paper proposes to install multiple GNSS receivers on a vehicle (assumed to be cooperative) to detect and mitigate the spoofing attack. While installing multiple GNSS receivers, we assume that each GNSS receiver’s relative position vector (RPV) is assumed to be known to other GNSS receivers. The installed GNSS receivers use the extended Kalman filter (EKF) framework to estimate their PVT. We proposed to calculate the equivalent-measurement and equivalent-measurement covariance of each GNSS receiver in the Cartesian coordinates in the tracklet framework. These tracklets are translated to the vehicle center using RPV to obtain translated-tracklets. The translated tracklet based generalized likelihood ratio test (GLRT) is derived to detect the spoofing attack at a given epoch. In addition to that, these translated-tracklets are processed in a batch least square (LS) framework to obtain the vehicle position. Once the attack is detected at a specific epoch, it quantifies that the position information is false. Moreover, another spoofing test is also formulated using DOA of signals. Once both the tests confirm the spoofing attack, the spoofer localization is performed using pseudo-updated states of GNSS receivers and acquired bearings in the iterative least-squares (ILS) framework. Mitigation of spoofing attack can be achieved either by projecting a null beam in the direction of the spoofer or by launching a counter-attack on the spoofer. The simulation results demonstrate that the proposed algorithm detects spoofing attacks and ensures continuity in the navigation track. As the number of satellite signals increases, the algorithms provide better position root mean square error (PRMSE) for GNSS receivers track, vehicle track, and spoofer localization.

Cognitive Opportunistic Navigation in Private Networks With 5G Signals and Beyond

A receiver architecture is proposed to cognitively extract navigation observables from fifth generation (5G) new radio (NR) signals of opportunity. Unlike conventional opportunistic receivers which require knowledge of the signal structure, particularly the reference signals (RSs), the proposed cognitive opportunistic navigation (CON) receiver requires knowledge of only the frame duration and carrier frequency of the signal. In 5G NR, some of these RSs are only transmitted on demand, which limits the existing opportunistic navigation frameworks to signals which are on <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">always-on</i> ; hence, limiting the exploitable RS bandwidth. To exploit the full available bandwidth and improve ranging accuracy, the proposed CON receiver is designed to estimate all the RSs contained in the transmitted signals corresponding to multiple 5G base stations, (i.e., gNBs). Navigation observables (pseudorange and carrier phase) are subsequently derived from the estimated RSs. The proposed receiver operates in two stages: (i) acquisition and (ii) tracking. The acquisition stage of the CON receiver is modeled as a sequential detection problem where the number of gNBs and their corresponding RSs and Doppler frequencies are unknown. The generalized likelihood ratio (GLR) test for sequentially detecting active gNBs is derived and used to estimate the number of gNBs and their RSs. In order for the receiver to refine and maintain the Doppler and RS estimates provided by the acquisition stage, tracking loops are designed. A sufficient condition on the Doppler estimation error to ensure that the proposed GLR asymptotically achieves a constant false alarm rate (CFAR) is derived. The output of the tracking loops, namely carrier phase and code phase, are then used to estimate the receiver’s position. Extensive experimental results are presented demonstrating the capabilities of the proposed CON receiver with real 5G signals on ground and aerial platforms, with an experiment showing the first navigation results with real 5G signals on an unmanned aerial vehicle (UAV) navigating using the CON receiver over a 416 m trajectory with a position root mean-squared error (RMSE) of 4.35 m.

A New Triangulation Algorithm for Positioning Space Debris

A single electro-optical (EO) sensor used in space debris observation provides angle-only information. However, space debris position can be derived using simultaneous optical measurements obtained from two EO sensors located at two separate observation sites, and this is commonly known as triangulation. In this paper, we propose a new triangulation algorithm to determine space debris position, and its analytical expression of Root-Mean-Square (RMS) position error is presented. The simulation of two-site observation is conducted to compare the RMS positioning error of the proposed triangulation algorithm with traditional triangulation algorithms. The results show that the maximum RMS position error of the proposed triangulation algorithm is not more than 200 m, the proposed triangulation algorithm has higher positioning accuracy than traditional triangulation algorithms, and the RMS position error obtained in the simulation is nearly consistent with the analytical expression of RMS position error. In addition, initial orbit determination (IOD) is carried out by using the triangulation positioning data, and the results show that the IOD accuracy of two-site observation is significantly higher than that of the single-site observation.

Multipath Mitigation via Synthetic Aperture Beamforming for Indoor and Deep Urban Navigation

A synthetic aperture navigation (SAN) system that exploits downlink cellular long-term evolution (LTE) signals and an inertial measurement unit (IMU) is developed. The system is suitable for multipath-rich environments, such as indoors and deep urban canyons. The proposed SAN system mitigates multipath via a spatial discriminator, which utilizes the motion of a single antenna element to synthesize a geometrically separated antenna array from time-separated snapshots, alleviating the need for a physical antenna array. Signals from the synthesized antenna array are used to beamform towards the line-of-sight (LOS) LTE direction, while suppressing multipath components. Different stages of the beamforming process are discussed, and the computational complexity of the proposed system is analyzed. To deal with the unknown clock biases of the LTE eNodeBs, two navigation frameworks are developed: (1) base/rover and (2) standalone rover. The proposed SAN system is validated experimentally, and the navigation solutions achieved from the following systems are compared: (i) IMU only, (ii) LTE only, (iii) feedforward LTE-SAN, (iv) feedback LTE-SAN, (v) LTE-IMU, and (vi) feedback LTE-SAN-IMU. In the performed experiment, a pedestrian-mounted receiver navigated indoors for 109 m in 50 seconds, while receiving LTE signals from 5 LTE eNodeBs. The proposed LTE-SAN-IMU system exhibited a two-dimensional position root mean-squared error (RMSE) of 1.44 m with a standard deviation of 1.85 m.

Comparative Study of the Parallel and Angular Electrical Gripper for Industrial Applications

Abstract The aim of this paper is to study the position and power performances of an electrical lead screw-driven industrial gripper mechanism (LSDIGM). This work consists of designing and developing an electrical LSDIGM that has the potential to meet various demands in the automation industry and factories. The performances of both angular electrical gripper (AEG) and parallel electrical gripper (PEG) mechanisms were compared based on their position and power efficiency. The position efficiency of these electrical LSDIGM is computed from the position root mean square error (PRMSE) obtained from errors between the two measured positions (input incremental encoder and output linear encoder). In the experimental setup, a current sensor and a spring were employed to measure the current in the input of the system and the stiffness in the output of the system, respectively. The electrical power in the input of the electrical LSDIGM and the mechanical power in the output of the LSDIGMs were calculated using the current and the spring force, respectively. Finally, the power efficiency of these electrical LSDIGMs was examined and compared at different velocity circumstances.

Autonomous Integrity Monitoring for Vehicular Navigation With Cellular Signals of Opportunity and an IMU

A receiver autonomous integrity monitoring (RAIM) framework for ground vehicle navigation using ambient cellular signals of opportunity (SOPs) and an inertial measurement unit (IMU) is developed. The proposed framework accounts for two types of errors that compromise the integrity of the navigation solution: (i) multipath and (ii) unmodeled biases in the cellular pseudorange measurements due to line-of-sight (LOS) signal blockage and high signal attenuation. This paper, first, characterizes the multipath in a cellular-based navigation framework. Next, a fault detection and exclusion technique for a cellular-based navigation framework is developed. Simulation and experimental results with real long-term evolution (LTE) signals are presented evaluating the efficacy of the proposed RAIM-based fault detection and exclusion technique on a ground vehicle navigating in a deep urban environment in the absence of global navigation satellite system (GNSS) signals. The experimental results on a ground vehicle traversing 825 m in an urban environment show that the proposed RAIM-based measurement exclusion technique reduces the position root mean-squared error (RMSE) by 66%.

Comparison of two second-order sliding mode control algorithms for an articulated intervention AUV: Theory and experimental results

An articulated intervention autonomous underwater vehicle (AIAUV) is a slender, multi-articulated underwater robot. Accurate trajectory tracking is essential for AIAUV operations. Furthermore, due to hydrodynamic and hydrostatic parameter uncertainties, uncertain thruster characteristics, unknown disturbances, and unmodelled dynamic effects, robustness is crucial. In this paper, we present a super-twisting algorithm (STA) with adaptive gains and a generalized super-twisting algorithm (GSTA) for trajectory tracking of the position and orientation of AIAUVs. A higher-order sliding mode observer (HOSMO) for estimating the linear and angular velocities when velocity measurements are unavailable is also presented. The tracking errors for the resulting system are proven to converge asymptotically to zero. Finally, we demonstrate the applicability of the presented control laws with comprehensive simulation and experimental results and perform a comparison study, with two tests (C-shape and C-shape with a moving head), between the two algorithms and also a benchmark PID controller. The STA with adaptive gains exhibits the best overall tracking performance, with average position root mean square error (RMSE) 0.0121 m and average orientation RMSE 0.0335 rad. The GSTA also presented good results with average position RMSE 0.0267 m and average orientation RMSE 0.0292 rad. The PID controller gave average position RMSE 0.0371 m and average orientation RMSE 0.0491 rad.