Real-time Position Estimation Research Articles

Omni-OTPE: Omnidirectional Optimal Real-Time Ground Target Position Estimation System for Moving Lightweight Unmanned Aerial Vehicle.

Ground target detection and positioning systems based on lightweight unmanned aerial vehicles (UAVs) are increasing in value for aerial reconnaissance and surveillance. However, the current method for estimating the target's position is limited by the field of view angle, rendering it challenging to fulfill the demands of a real-time omnidirectional reconnaissance operation. To address this issue, we propose an Omnidirectional Optimal Real-Time Ground Target Position Estimation System (Omni-OTPE) that utilizes a fisheye camera and LiDAR sensors. The object of interest is first identified in the fisheye image, and then, the image-based target position is obtained by solving using the fisheye projection model and the target center extraction algorithm based on the detected edge information. Next, the LiDAR's real-time point cloud data are filtered based on position-direction constraints using the image-based target position information. This step allows for the determination of point cloud clusters that are relevant to the characterization of the target's position information. Finally, the target positions obtained from the two methods are fused using an optimal Kalman fuser to obtain the optimal target position information. In order to evaluate the positioning accuracy, we designed a hardware and software setup, mounted on a lightweight UAV, and tested it in a real scenario. The experimental results validate that our method exhibits significant advantages over traditional methods and achieves a real-time high-performance ground target position estimation function.

Real-time estimation of barycenter position for fuel cell distributed drive electric tractor based on derived UKF

ABSTRACT The FCDET is considered as a promising and advanced agricultural machinery due to its convenience and controllability. To enhance the control performance of the FCDET unit dynamic system, understanding the accurate position of the center of gravity (CoG) of the tractor unit in longitudinal motion and the operating environment is essential. In this paper, we proposed a real-time estimation method of CoG position of longitudinal motion for FCDET plowing and transportation conditions. CoG height and longitudinal axis distance distribution coefficients are used as state estimation variables, and the data obtained from displacement and velocity sensors are used as state observation variables, establishing a discrete system state space model and proving the observability. The proposed Derived UKF algorithm introduces the Sage-Husa adaptive noise equation within the Square-Root UKF framework to address the non-positive definite matrix problem. The research shows that the precise CoG longitudinal position effectively reflects changes in operational pavement load. The Derived UKF algorithm exhibits high state estimation accuracy in plowing and transportation conditions, with mean absolute errors of 14.2% and 5.0% of the state amplitude, respectively. This study supports the practical application of real-time state parameter estimation algorithms in controlling advanced agricultural machinery distributed drive systems.

Real-Time Position, Velocity, and Timing Estimation of Lunar Surface Users with Joint Doppler and Ranging

Real-Time Position, Velocity, and Timing Estimation of Lunar Surface Users with Joint Doppler and Ranging

A general visual-servoing control strategy for active non-cooperative target tracking of space manipulators

Non-cooperative target tracking plays an important role in many space missions. Recent studies on non-cooperative target tracking are mostly concentrated in the field of passive target tracking. Passive non-cooperative target tracking can effectively be used in the initial phases of a close-range rendezvous manoeuvre. Instead, during the terminal phases of such manoeuvres, active object tracking(AOT) techniques are more convenient. This study propose a general visual servo strategy for the active non-cooperative target tracking of space manipulator. Our proposed method do not need any a priori information about the targets and can be used for space manipulators of any configuration and binocular cameras of any types. Our strategy includes a real-time and accurate position estimation algorithm for non-cooperative targets and a motion planning algorithm combined Position-based visual servo(PBVS) and Image-based visual servo(IBVS). The experiment verified that the proposed strategy well solved the active non-cooperative target tracking task and can be used as a baseline for solving such tracking problems which provides a reference for subsequent studies.

Machine Learning-based reconstruction of single and double radiation source distribution using plastic scintillation optical fiber

In this study, we propose a novel method for reconstructing the radiation distribution of a one-dimensional radioactive source using Machine Learning (ML) algorithms and plastic scintillation optical fiber. The wavelength spectrum unfolding technique is used to estimate the source position accurately. We compare the accuracy and time efficiency of three different algorithms, namely, Generalized reduced gradient (GRG), Maximum likelihood expectation maximization (MLEM), and ML, in the single-source and dual-source cases. Our results demonstrate that MLEM algorithm has a shorter reconstruction time with comparable accuracy of position and intensity compared to GRG algorithm for single-source case. For dual-source case, ML algorithm provides real-time estimation of position and intensity with acceptable errors, while GRG algorithm has a larger error in intensity estimation and longer computation time. Our proposed ML algorithms offer useful guidance for practical applications in radiation source location.

Generalizable end-to-end deep learning frameworks for real-time attitude estimation using 6DoF inertial measurement units

This paper presents a novel end-to-end deep learning framework for real-time inertial attitude estimation using 6DoF IMU measurements. Inertial Measurement Units are widely used in various applications, including engineering and medical sciences. However, traditional filters used for attitude estimation suffer from poor generalization over different motion patterns and environmental disturbances. To address this problem, we propose two deep learning models that incorporate accelerometer and gyroscope readings as inputs. These models are designed to be generalized to different motion patterns, sampling rates, and environmental disturbances. Our models consist of convolutional neural network layers combined with Bi-Directional Long–Short Term Memory followed by a Fully Forward Neural Network to estimate the quaternion. We evaluate the proposed method on seven publicly available datasets, totaling more than 120 h and 200 kilometers of IMU measurements. Our results show that the proposed method outperforms state-of-the-art methods in terms of accuracy and robustness. Additionally, our framework demonstrates superior generalization over various motion characteristics and sensor sampling rates. Overall, this paper provides a comprehensive and reliable solution for real-time inertial attitude estimation using 6DoF IMUs, which has significant implications for a wide range of applications.

REAL-TIME LIDAR-INERTIAL POSITIONING AND MAPPING FOR FORESTRY AUTOMATION

Abstract. Accurate real-time positioning in forests is challenging due to GNSS signal degradation and an unstructured spatial environment that is difficult to conceptualize through visual sensing. Positioning is vital in any forestry automation application, such as collecting inventory, harvesting, or search and rescue missions. Lidar and inertial based solutions are popular, however they often obtain real-time computation by effectively compressing the number of measurement points to track utilizing regular geometric shapes that do not adapt well to forest. Other solutions sacrifice the high-frequency of positioning estimates or they rely on post-processing. We propose a real-time lidar-inertial SLAM-based approach that utilizes NDT scan registration, factor graphs and loop closure corrections to produce accurate and high-frequency pose estimates. To test our method, data was captured with a lidar and imu sensor mounted on a stick surveying forest sites. Ground truth trajectory for accuracy evaluation was computed by fine registering individual laser scans onto a high-quality reference point cloud recorded from the same forest area using terrestial and airborn laser scanning methods. Experiments shows, that our method can produce real-time position estimates up to 200 Hz within 2–15 cm error.

Real-time position and trajectory estimation based on deep learning and monocular cameras

Real-time position and trajectory estimation based on deep learning and monocular cameras

Real-time position and trajectory estimation based on deep learning and monocular cameras

Real-time position and trajectory estimation based on deep learning and monocular cameras

AUV-Assisted Diver Navigation

This letter presents a precise navigation technique for a scuba diver using an autonomous underwater vehicle (AUV) as a positioning aid. We develop and evaluate the state estimation algorithms and communication architecture for diver navigation based on subsurface human-AUV teaming with no requirement for ocean current data or exact diver speeds. By depending on acoustic communication and commercial AUV navigation capabilities, our method provides a unique capability for localizing a diver's position during an extended mission, without the requirement to maintain visual contact with the diver during the mission, or to deploy acoustic beacons. We utilize the Woods Hole Oceanographic Institution Micromodem 2 for range-only single-beacon navigation between two kayaks serving as proxies for the diver and REMUS 100 AUV. Range and odometry measurements are fused in a factor graph using incremental smoothing and mapping 2 (iSAM2) with appropriate motion and measurement models to provide real-time diver position estimates given unknown ocean currents. Field experiments demonstrate an average online endpoint error of 4.53 meters after 400-meter transits.

Attitude Estimation of On-Orbit Spacecraft Based on the U-Linked Network

Real-time attitude estimation of on-orbit spacecraft is a core task in various space applications. Most of the existing methods are based on long-term observation by high-resolution sensors, such as space-borne cameras and ground-based radars. However, when the observation period is limited, it is difficult to obtain target instantaneous attitude information by these methods. To achieve instantaneous attitude estimation from a single camera image, a U-Linked network (ULNet) is proposed in this work. The prior structural constraints of key points are used to reflect the relationship between three-dimensional (3D) target attitude parameters and two-dimensional (2D) images. In this way, target attitude estimation can be solved through the feature point regression when the large-perspective image dataset can be built. The simulation results confirm the feasibility of the proposed method. Besides, the estimation performance of the proposed method also is investigated under different imaging observation conditions.

Bluetooth Indoor Localization With Gaussian–Bernoulli Restricted Boltzmann Machine Plus Liquid State Machine

The location information of sensors and devices plays an important role in Internet of Things (IoT). Low cost and energy efficient Bluetooth Low Energy (BLE)-based localization solutions are ideal for extensive use in IoT applications. The main challenge is to combat the signal fluctuation in non-line-of-sight (NLOS) propagation. Most Machine Learning (ML) algorithms available in the literature to address this issue belong to classification and batch learning. This article proposes a novel online learning algorithm for BLE localization based on Gaussian–Bernoulli Restricted Boltzmann Machine (GBRBM) plus Liquid State Machine (LSM), which learns the training data one-by-one. Unsupervised GBRBM is able to extract sound patterns of fluctuated RSS inputs, and LSM manages to map the patterns to real-time position estimation. Extensive experimental results demonstrate the superiority of the proposed method over other state-of-the-art batch learning methods in terms of localization accuracy and complexity.

An Efficient and Low-Latency Deep Inertial Odometer for Smartphone Positioning

The Global Positioning System (GPS) is not an effective solution for pedestrian indoor navigation using embedded devices because of poor signal penetration and high power requirements. Indoor positioning based purely on commercial-grade inertial measurement units (IMUs) may provide a good alternative but their significant noise and random bias must be eliminated. Although Kalman filters and pedestrian dead reckoning helped reduce the errors using IMUs, these methods cannot prevent the divergence of estimated position error. Deep learning was proposed for accurate position estimation with IMUs. Despite the progress, robust position estimation in mobile embedded devices using deep learning has not been established yet because of memory requirements, latency and inaccurate position estimation especially for stationary pedestrians. In this study, we present an efficient embedded deep learning approach for robust, real-time and accurate pedestrian position estimation using commercial Android smartphone IMUs. We first extended a publicly available deep inertial navigation dataset (OxIOD) with stationary data to enhance the positioning accuracy for both steady state and motion. Next, we trained and tested a deep learning architecture that yields a higher positioning accuracy with 50% lower network latency and 31% lower network size compared with earlier deep positioning networks such as IONet. Our real-time tests of the model in an Android smartphone indicated that the extension for the dataset reduces the position shift when the smartphone is stationary. Since our embedded deep learning solution simultaneously decreases the positioning error, latency and memory requirements, the solution paves the way for numerous practical indoor navigation applications.

Low-Speed Sensorless Control for the Interior Permanent Magnet Synchronous Motors with Sliding Discrete Fourier Transform

An efficient estimation of the rotor position has always been a premise of the reliable operation for the interior permanent magnet synchronous motors (IPMSM), especially for low-speed conditions because of the small back electromotive force (EMF) and low signal-to-noise ratio (SNR). The commonly used observation method, e.g., sliding mode observer (SMO), is suitable for these surface mounted motors and has no great adaptability to the saliency. In this paper, a novel rotor position (including the real-time position and initial position) estimation method was proposed based on the traditional high-frequency signal injection method. Firstly, high-frequency signals were injected to induce the high-frequency current components which contain the rotor position information. Then, the sliding discrete Fourier transform (SDFT) algorithm was used to extract the amplitudes of the induced current components which could be used to get the real-time and initial rotor positions by a proportional integral (PI) regulator and a polarity identification. Lastly, with the established experiments’ platform, the estimation tests of the rotor position at a low speed have been completed to make verification of the effectiveness of the approach studied in this paper.

Cuckoo Search-based SVM (CS-SVM) Model for Real-Time Indoor Position Estimation in IoT Networks

Internet of Things (IoT), an emerging technology, is becoming an essential part of today’s world. Machine learning (ML) algorithms play an important role in various applications of IoT. For decades, the location information has been extremely useful for humans to navigate both in outdoor and indoor environments. Wi-Fi access point-based indoor positioning systems get more popularity, as it avoids extra calibration expenses. The fingerprinting technique is preferred in an indoor environment as it does not require a signal’s Line of Sight (LoS). It consists of two phases: offline and online phase. In the offline phase, the Wi-Fi RSSI radio map of the site is stored in a database, and in the online phase, the object is localized using the offline database. To avoid the radio map construction which is expensive in terms of labor, time, and cost, machine learning techniques may be used. In this research work, we proposed a hybrid technique using Cuckoo Search-based Support Vector Machine (CS-SVM) for real-time position estimation. Cuckoo search is a nature-inspired optimization algorithm, which solves the problem of slow convergence rate and local minima of other similar algorithms. Wi-Fi RSSI fingerprint dataset of UCI repository having seven classes is used for simulation purposes. The dataset is preprocessed by min-max normalization to increase accuracy and reduce computational speed. The proposed model is simulated using MATLAB and evaluated in terms of accuracy, precision, and recall with K-nearest neighbor (KNN) and support vector machine (SVM). Moreover, the simulation results show that the proposed model achieves high accuracy of 99.87%.

Analog-Hall-Sensor-Based Position Detection Method With Temperature Compensation for Permanent-Magnet Linear Motor

Analog Hall sensors are utilized to detect the mover position of tubular permanent-magnet synchronous linear motors because of their low cost and small size. Typical Hall-sensor-based position estimation methods rely on additional displacement sensors, such as optical grating rulers, to identify the measurement model of the Hall sensors, but this reliance increases the difficulty of implementation of these methods. Moreover, most position detection methods disregard temperature change in the motors, leading to a position estimation error. Thus, a measurement model that considers temperature change is established in this study to overcome these problems. An algorithm consisting of particle swarm optimization and a successive solving algorithm (SSA) is used to identify the measurement model using only Hall sensors. A new SSA with temperature compensation is proposed for real-time position estimation. In this method, the forgetting factor recursive least-squares algorithm is modified to update the measurement model at the present temperature to avoid model mismatch. The proposed method is validated using a self-designed experimental platform. The experimental results show that the model’s identification accuracy is equivalent to that of the fast Fourier transform algorithm using an optical grating ruler. The position estimation error is less than $120~\mu \text{m}$ under different operating conditions.

Jet transport particle filter for attitude estimation of tumbling space objects

The particle filter (PF) is one of the most potential methods for nonlinear state estimation of spacecraft on account of its accuracy and stability. However, the heavy computational burden limits its application in real-time estimations. In this paper, we propose an improvement for the PF based on the Jet Transport (JT) theory, and apply it to the real-time attitude estimation of tumbling space objects. The main innovation of the Jet Transport Particle Filter (JTPF) is to employ the JT technique in the particle evolution process, rather than using the numerical integration as the classical PF, so as to reduce the computational burden of the algorithm. Furthermore, the proposed JTPF uses the multiplicative error quaternion to avoid further errors in the normalizing process, and the regularization technique to avoid the particle degeneracy. The JTPF is tested in three scenarios with different state and observation dimensions. Monte Carlo simulations demonstrate that the JTPF has a similar accuracy as the classical PF, and costs only 7% ∼ 13% of processing time as the latter. Moreover, some empirical principals are summarized about the optimal JT expansion order and particle number of the JTPF.

Real-Time Position and Attitude Estimation for Homing and Docking of an Autonomous Underwater Vehicle Based on Bionic Polarized Optical Guidance

As an important tool for marine exploration, the autonomous underwater vehicle (AUV) must home in and dock at a docking station (DS) to be recharged, repaired, or to exchange information at set intervals. However, the complex and hostile underwater environment makes this process challenging. This study proposes a real-time method based on polarized optical guidance for determining the position and attitude of the AUV relative to its DS. Four polarized artificial underwater landmarks are positioned at the DS, which are recognized by the AUV vision system. Compared with light intensity, the polarization of a light beam is known to be better maintained at greater propagation distances, especially in underwater environments. The proposed method, which is inspired by the ability of marine animals to communicate, calculates the pose parameters in less than 10 ms without any other navigational information. The simulation results reveal that the angle errors are small and the position errors are no more than 0.116 m within 100 m in the coastal ocean. The results of underwater experiments further demonstrate the feasibility of the proposed method, which extends the operating distance of the AUV beyond what is currently possible while maintaining the precision of traditional optical guidance.

Online Calibration of Sensorless Position Estimation for Switched Reluctance Motors With Parametric Uncertainties

This article proposes an online calibration strategy of sensorless position estimation for switched reluctance motors (SRMs) with parametric uncertainties. For conventional sensorless SRM drives, the voltage drops on power devices and the winding resistance changing with the current and temperature are neglected, which affects the accuracy of the estimated position. In this article, an inductance calculation scheme is proposed to improve the real-time position estimation with these parametric uncertainties. First, the calculation errors of the flux linkage and inductance are both investigated and the error correction of the estimated rotor position is analyzed in detail. Then, the inductance values of the current overlapped phases are calculated. The dc component of the phase inductance is online adjusted, where all three phase inductances can be easily obtained accordingly. Finally, the real-time rotor position is acquired by the coordinate transformation of the calculated three-phase inductance during the current overlapped region. Compared to conventional SRM sensorless drives, the error of the estimated position due to parametric uncertainties can be significantly reduced without pulse injection, extra hardware, and prestored flux linkage and inductance profiles. The simulation and experiments are carried out on three-phase and four-phase SRMs to validate the effectiveness of the proposed scheme.

BLAS: Broadcast Relative Localization and Clock Synchronization for Dynamic Dense Multiagent Systems

The spatiotemporal information plays crucial roles in a multi-agent system (MAS). However, for a highly dynamic and dense MAS in unknown environments, estimating its spatiotemporal states is a difficult problem. In this paper, we present BLAS: a wireless broadcast relative localization and clock synchronization system to address these challenges. Our BLAS system exploits a broadcast architecture, under which a MAS is categorized into parent agents that broadcast wireless packets and child agents that are passive receivers, to reduce the number of required packets among agents for relative localization and clock synchronization. We first propose an asynchronous broadcasting and passively receiving (ABPR) protocol. The protocol schedules the broadcast of parent agents using a distributed time division multiple access (D-TDMA) scheme and delivers inter-agent information used for joint relative localization and clock synchronization. We then present distributed state estimation approaches in parent and child agents that utilize the broadcast inter-agent information for joint estimation of spatiotemporal states. The simulations and real-world experiments based on ultra-wideband (UWB) illustrate that our proposed BLAS cannot only enable accurate, high-frequency and real-time estimation of relative position and clock parameters but also support theoretically an unlimited number of agents.