Localization Tracking Research Articles

Graph-Based Simultaneous Localization and Bias Tracking

Graph-Based Simultaneous Localization and Bias Tracking

Three-dimensional positioning and trajectory tracking of pigeons in large indoor spaces

Animal localization and trajectory tracking are of great value for the study of brain spatial cognition and navigation neural mechanisms. However, traditional optical lens video positioning techniques are limited in their scope due to factors such as camera perspective. For pigeons with excellent spatial cognition and navigation abilities, based on the beacon positioning technology, a three-dimensional (3D) trajectory positioning and tracking method suitable for large indoor spaces was proposed, and the corresponding positioning principle and hardware structure were provided. The results of in vitro and in vivo experiments showed that the system could achieve centimeter-level positioning and trajectory tracking of pigeons in a space of 360 cm × 200 cm × 245 cm. Compared with traditional optical lens video positioning techniques, this system has the advantages of large space, high precision, and high response speed. It not only helps to study the neural mechanisms of pigeon 3D spatial cognition and navigation, but also has high reference value for trajectory tracking of other animals.

Single-Molecule Localization Microscopy and Tracking with a Fluorescent Mechanosensitive Probe.

A milestone in optical imaging of mechanical forces in cells has been the development of the family of flipper fluorescent probes able to report membrane tension noninvasively in living cells through their fluorescence lifetime. The specifically designed Flipper-CF3 probe with an engineered inherent blinking mechanism was recently introduced for super-resolution fluorescence microscopy of lipid ordered membranes but was too dim to be detected in lipid disordered membranes at the single-molecule level (García-Calvo, J. J. Am. Chem. Soc. 2020, 142(28), 12034-12038). We show here that the original and commercially available probe Flipper-TR is compatible with single-molecule based super-resolution imaging and resolves both liquid ordered and liquid disordered membranes of giant unilamellar vesicles below the diffraction limit. Single probe molecules were additionally tracked in lipid bilayers, enabling to distinguish membranes of varying composition from the diffusion coefficient of the probe. Differences in brightness between Flipper-CF3 and Flipper-TR originate in their steady-state absorption and fluorescence properties. The general compatibility of the Flipper-TR scaffold with single-molecule detection is further shown in super-resolution experiments with targetable Flipper-TR derivatives.

Autonomous Trajectory Planning for Ultrasound-Guided Real-Time Tracking of Suspicious Breast Tumor Targets

Ultrasound image guidance could display the movement of soft tissue in real time, which provides an important basis for the operation path selection of tumor for examinations and interventions such as precise localization and puncture biopsy. However, factors such as tissue deformation, patient motion, and instrument contact pose great challenges for real-time smooth localization tracking of target tissues and maintaining a suitable acoustic window. Not only does it reduce the accuracy of ultrasound examination, but also may prolong the examination time. In this paper, a real-time autonomous ultrasound robot trajectory planning framework is proposed to achieve global scanning of breast tissue and real-time local tracking of suspicious tumor targets. In addition, an ultrasound suspicious tumor target next moment motion attitude and position estimation algorithm and an acceleration-continuous online trajectory generation (ACOTG) algorithm are proposed. Experiments on breast phantom showed that the fluctuation difference of both global scanning and tracking scanning force was less than 5 N <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\pm$</tex-math> </inline-formula> 15%, and ACOTG takes less than 1 microsecond to calculate the next moment state. The position, velocity, and acceleration of the tracking path did not change abruptly when the unknown target changed. The ultrasound image of the suspected tumor target could always be smoothly maintained in a suitable acoustic window, while the errors between the center of the suspected tumor target image and the center of the acoustic window in the horizontal direction and the depth direction were less than <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\pm$</tex-math> </inline-formula> 1 mm and 30 mm <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\pm$</tex-math> </inline-formula> 10%, respectively. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —The motivation of this study was to solve the clinical problem of difficulty in keeping the suspected breast tumor target stably in the center of the field of view under the influence of tissue deformation, patient motion and instrument contact in ultrasound guidance. We proposed the idea of global conventional scanning and local online tracking to achieve the autonomous scanning, detecting and tracking of suspicious tumor targets within the global scope of the breast. ACOTG algorithm is proposed and implemented, which can make the ultrasound image of suspicious tumor target can be displayed stably in the center of the acoustic window. With the help of this system, the accuracy of ultrasound examination can be improved and the time of examination can be reduced. The proposed system can also be applied to ultrasound scanning of other parts of the body.

A Decentralized Consistent Approach for Multi-Robot Simultaneous Localization and Object Tracking

This study focuses on the problem of multi-robot simultaneous localization and object tracking (MR-SLOT), which involves the collaborative effort of multiple mobile robots tracking a moving object on a plain. To address this challenge, this paper introduces a fully decentralized approach based on the extended Kalman filter and covariance intersection (CI), denoted as CI-SLOT. In the proposed approach, each robot can precisely track the robot-to-object correlations. Moreover information exchange occurs solely among one-hop communicating robots, and each robot only maintains the latest estimate of the object and its pose. The simulation results have shown that the performances of CI-SLOT are better than those of the state-of-the-art approaches of comparable processing and communication costs.

Target encirclement for multiple quadrotors with visual detection and localization

Target encirclement is widely used in the field of unmanned aerial vehicles(UAVs), which can effectively monitor and intercept external threats. However, the integration from target detection, localization to final tracking is difficult or costly. This article proposes a complete and inexpensive framework of the target encirclement for multiple quadrotors. The framework consists of three modules: object detection, target localization and formation tracking. Firstly, a one-stage object detector based on a convolutional neural network is used to achieve fast and accurate object detection. Then, combined with the position and attitude states of the quadrotor, a 3D target localization scheme to locate the target position is proposed. Based on consensus theory, a time-varying formation tracking control protocol is proposed. Finally, a multiple quadrotor platform composed of one reconnaissance quadrotor and four hunter quadrotors is built with self-organizing network communication, which avoids the expensive cost of deploying object detection modules on each quadrotor platform. We deployed the framework on the multiple quadrotor platform and conducted static and dynamic localization and encirclement experiments with a minibus as the target. The result shows that the reconnaissance quadrotors can detect and accurately locate targets over 30 fps, and the average deviation of locating the target minibus could reach a minimum of 0.0712 m. The hunter quadrotors could track and encircle the dynamic moving target minibus in a time-varying formation. Experiments demonstrate the effectiveness and practicality of the proposed framework of the target encirclement for multiple quadrotors.

Passive Infrared Sensor-Based Occupancy Monitoring in Smart Buildings: A Review of Methodologies and Machine Learning Approaches.

Buildings are rapidly becoming more digitized, largely due to developments in the internet of things (IoT). This provides both opportunities and challenges. One of the central challenges in the process of digitizing buildings is the ability to monitor these buildings' status effectively. This monitoring is essential for services that rely on information about the presence and activities of individuals within different areas of these buildings. Occupancy information (including people counting, occupancy detection, location tracking, and activity detection) plays a vital role in the management of smart buildings. In this article, we primarily focus on the use of passive infrared (PIR) sensors for gathering occupancy information. PIR sensors are among the most widely used sensors for this purpose due to their consideration of privacy concerns, cost-effectiveness, and low processing complexity compared to other sensors. Despite numerous literature reviews in the field of occupancy information, there is currently no literature review dedicated to occupancy information derived specifically from PIR sensors. Therefore, this review analyzes articles that specifically explore the application of PIR sensors for obtaining occupancy information. It provides a comprehensive literature review of PIR sensor technology from 2015 to 2023, focusing on applications in people counting, activity detection, and localization (tracking and location). It consolidates findings from articles that have explored and enhanced the capabilities of PIR sensors in these interconnected domains. This review thoroughly examines the application of various techniques, machine learning algorithms, and configurations for PIR sensors in indoor building environments, emphasizing not only the data processing aspects but also their advantages, limitations, and efficacy in producing accurate occupancy information. These developments are crucial for improving building management systems in terms of energy efficiency, security, and user comfort, among other operational aspects. The article seeks to offer a thorough analysis of the present state and potential future advancements of PIR sensor technology in efficiently monitoring and understanding occupancy information by classifying and analyzing improvements in these domains.

Synthesis and characterization of the redox-responsive amphiphilic fluorescent alginate derivatives as the multi-functional hydrophobic anticancer drug delivery system with visualization and sustained release

To develop the multi-functional hydrophobic anticancer drug carriers with visualization and sustained release, a novel amphiphilic alginate derivative (LSA-SS-DEAC) containing chromophobe groups was designed and synthesized by grafting hydrophobic fluorescent molecule, 7-(diethylamino-coumarin-3-carboxylic acid (DEAC-COOH) onto hydrophilic low molecular alginate backbone through cystamine by water-soluble carbodiimide chemical method. The molecular structure and self-assembly properties of the synthesized LSA-SS-DEAC were characterized. Furthermore, the encapsulation of anti-cancer drugs, doxorubicin hydrochloride (DOX) in LSA-SS-DEAC self-assembled micelles was realized by ultrasonic dialysis method. Subsequently, the in vitro drug release behavior and in vitro cytotoxicity of LSA-SS-DEAC micelles in simulated tumor microenvironment were also studied. The resultant LSA-SS-DEAC with the critical micelle concentration (CMC) of 0.26 mg/mL exhibited the ability to spontaneously form spherical micelles with the particle size of 250.2 ± 4.3 nm (PDI = 0.23) and zeta potential of −49.1 ± 0.4 mV, which presented good stability in pH 7.4 PBS and excellent fluorescence properties and redox responsiveness. The drug-loaded LSA-SS-DEAC micelles had the optimal EE value of about 42.2% when the DOX/LSA-SS-DEAC mass ratio was 3:10. Meanwhile, The drug-loaded LSA-SS-DEAC micelles exhibited sustained release in GSH-free environment, and the cumulative drug release rates were 40.3% and 46.6% in pH 7.4 and 5.0 PBS for 48 h, respectively. However, in the simulated tumor cell microenvironment (pH 5.0 PBS + 10 mM GSH), the disulfide bond between the core-shell was broken under the GSH trigger, and the LSA-SS-DEAC micelles collapsed, resulting in a cumulative drug release rate of about 64% at 12 h. In vitro cytotoxicity results showed that LSA-SS-DEAC had low cytotoxicity, and revealed obvious inhibitory effect on HeLa cells after DOX loading. Fluorescence imaging results indicated that LSA-SS-DEAC micelles could emit green light after ingested by cells, and their fluorescence properties were not affected after encapsulation of DOX. Accordingly, LSA-SS-DEAC micelles had the potential for drug localization tracking and rapid drug release at focal sites, which could be expected to be an ideal drug carriers for hydrophobic anticancer drug delivery.

Research on Multi-Hole Localization Tracking Based on a Combination of Machine Vision and Deep Learning

In the process of industrial production, manual assembly of workpieces exists with low efficiency and high intensity, and some of the assembly process of the human body has a certain degree of danger. At the same time, traditional machine learning algorithms are difficult to adapt to the complexity of the current industrial field environment; the change in the environment will greatly affect the accuracy of the robot’s work. Therefore, this paper proposes a method based on the combination of machine vision and the YOLOv5 deep learning model to obtain the disk porous localization information, after coordinate mapping by the ROS communication control robotic arm work, in order to improve the anti-interference ability of the environment and work efficiency but also reduce the danger to the human body. The system utilizes a camera to collect real-time images of targets in complex environments and, then, trains and processes them for recognition such that coordinate localization information can be obtained. This information is converted into coordinates under the robot coordinate system through hand–eye calibration, and the robot is then controlled to complete multi-hole localization and tracking by means of communication between the upper and lower computers. The results show that there is a high accuracy in the training and testing of the target object, and the control accuracy of the robotic arm is also relatively high. The method has strong anti-interference to the complex environment of industry and exhibits a certain feasibility and effectiveness. It lays a foundation for achieving the automated installation of docking disk workpieces in industrial production and also provides a more favorable choice for the production and installation of the process of screw positioning needs.

Review of problem solving methods for simultaneous localization and multi-object 3D tracking by using technical vision

A review of existing approaches to the development of reliable visual localization systems in dynamic environments is carried out. Two schemes for solving the problem of simultaneous localization and multi-object 3D tracking, based on motion information and semantic information, respectively are described. Existing approaches to the problem of motion segmentation are briefly reviewed. Classic options for solving this problem are listed and their advantages and disadvantages are enumerated. Keywords visual localization, multi-object 3D tracking, motion segmentation, semantic information

A novel polymer platform for endoscopic tattooing with high efficacy and safety.

Endoscopic tattooing plays a pivotal role in modern endoscopic localization of gastrointestinal lesions, facilitating further surgical intervention and aiding in the postoperative identification and repositioning of lesions. However, traditional endoscopic tattoo dyes often suffer from drawbacks such as side effects, short tattoo duration, and high overall costs. In this study, we developed polyvinylpyrrolidone (PVP)-modified polypyrrole (PPy) nanoparticles by oxidizing pyrrole in a PVP aqueous solution to create a PPy/PVP nanoparticle solution. This innovation aims to enhance endoscopic tattooing efficiency and mitigate the limitations associated with current tattooing methods. Both in vitro and in vivo evaluations confirmed the biosafety of PPy/PVP nanoparticles. Endoscopic tattooing experiments conducted in a pig model demonstrated the dye's stability within the digestive tract. Similarly, subcutaneous tissue tattooing experiments performed in a mouse model revealed the sustained stability of the PPy/PVP tattoo dye for at least 180days. With its robust stability, safety, and longevity, PPy/PVP nanoparticles hold promise as novel tattoo dyes for marking intestinal lesion sites. This advancement has the potential to enhance the accuracy of lesion localization and long-term tracking.

Cooperative Localization and Target Tracking With Data Exception Based on Classification

Cooperative Localization and Target Tracking With Data Exception Based on Classification

A Study on Wireless Sensor Node Localization and Target Tracking Based on Improved Locust Algorithm

A Study on Wireless Sensor Node Localization and Target Tracking Based on Improved Locust Algorithm

No-wash fluorogenic labeling of proteins for reversible photoswitching in live cells.

Photoswitchable fluorescent molecules (PSFMs) are positioned as valuable tools for biomolecule localization tracking and super-resolution imaging technologies due to their unique ability to reversibly control fluorescence intensity upon light irradiation. Despite the high demand for PSFMs that are suitable for live-cell imaging, no general method has been reported that enables reversible fluorescence control on proteins of interest in living cells. Herein, we have established a platform to realize reversible fluorescence switching in living cells by adapting a protein labeling system. We have developed a new PSFM, named HTL-Trp-BODIPY-FF, which exhibits strong fluorogenicity upon recognition of Halo-tag protein and reversible fluorescence photoswitching in living cells. This is the first example of a PSFM that can be applicable to a general-purpose Halo-tag protein labeling system for no-wash live-cell imaging.

Deep learning aided multi-source passive 3D AOA wireless positioning using a moving receiver: A low complexity approach

Passive localization of multiple sources while the receiver is in motion is a fundamental problem in wireless communication with practical applications in various fields such as acoustic event localization and object tracking. In this study, we propose a novel method for three dimensional (3D) angle of arrival (AOA) localization using a moving receiver with a limited number of sensors. In the proposed method, we record signals from sources in a series of time windows. Then we estimate the elevation and azimuth AOAs of sources along with their pairing by using the proposed three low-complexity deep learning (DL)-based AOA estimators. Following the initial AOA estimation, a low-complexity off-grid based refinement technique is employed to further enhance the accuracy of the estimated elevation and azimuth AOAs based on the maximum likelihood (ML) criterion. Next, a multi-source 3D-localization algorithm is proposed to estimate the positions of sources across the recorded time windows. The simulation results validate the effectiveness and efficiency of the proposed method. The results highlight that the DL-based AOA and location estimators outperform recent studies, while also demonstrating robustness in the face of practical imperfections, such as situations where the receiver’s position or direction is uncertain during movement. Our complexity analysis verifies that the proposed method outperforms existing methods in terms of speed and the number of computations. This enhanced computational efficiency makes the proposed method highly attractive and practically significant for application in various fields.

Simultaneously Intelligent Sensing and Beamforming Based on an Adaptive Information Metasurface.

Due to its ability to adapt to a variety of electromagnetic (EM) environments, the sensing-enabled metasurface has garnered significant attention. However, large-scale EM-field sensing to obtain more information is still very challenging. Here, an adaptive information metasurface is proposed to enable intelligent sensing and wave manipulating simultaneously or more specifically, to realize intelligent target localization and beam tracking adaptively. The metasurface is composed of an array of meta-atoms, and each is loaded with two PIN diodes and a sensing-channel structure, for polarization-insensitive and programmable beamforming and sensing. By controlling the state of the PIN diode, the proposed meta-atom has 1-bit phase response in the designed frequency band, while the sensing loss keeps higher than -10dB for both "ON" and "OFF" states. Hence there is nearly no interaction between the beamforming and sensing modes. Experiments are conducted to show multiple functions of the metasurface, including intelligent target sensing and self-adaptive beamforming, and the measured results are in good agreement with the numerical simulations and theoretical calculations.

Cooperative Localization and Target Tracking in Mobile Sensor Networks With Uncertainties in Measurement Models

Cooperative Localization and Target Tracking in Mobile Sensor Networks With Uncertainties in Measurement Models

LoRa-based outdoor localization and tracking using unsupervised symbolization

This paper proposes a long-range (LoRa)-based outdoor localization and tracking method. Our method presents an unsupervised localization approach that utilizes symbolized LoRa received signal features, such as RSSI, SNR, and path loss, where each symbol represents a system state. To identify the partitioning boundaries between the symbols in time series, we employ maximum entropy partitioning. The D-Markov machine is used to construct nondeterministic finite-state automata for extracting temporal patterns. We incorporate the Chinese restaurant process for online estimation, especially in scenarios with an unbounded number of probable areas around each LoRa gateway. An adaptive trilateration approach is then used to localize the target node from the estimated ranged radii of areas. The point-wise localization data was used for time-series continuous tracking. We collected a dataset using three LoRaWAN gateways, sensor nodes powered by single-use batteries, and a Chirpstack server on a sports oval. We thoroughly evaluated the proposed method from the perspectives of localization accuracy and tracking capability. Our method outperformed state-of-the-art machine learning-driven range-based and fingerprint-based localization techniques.

Adaptive Neural Control of Flight-Style AUV for Subsea Cable Tracking Under Electromagnetic Localization Guidance

This article focuses on an industrial system for the intelligent inspection of subsea cables based on a flight-style autonomous underwater vehicle (AUV) that tracks the route of subsea cable guided by electromagnetic localization signals. The intelligent inspection system consists of electromagnetic localization and autonomous tracking control modules. The electromagnetic localization algorithm for subsea cable is geometrically derived based on the electromagnetic sequences measured with two triaxial sensors that are symmetrically mounted on the forewings of a flight-style AUV. The argument results on the singularity, sensitivity, and consistency of the localization algorithm provide guidelines for designing the cable-tracking AUV prototypes and planning the near-bottom cable-tracking tasks. Subsequently, the cable-tracking control schemes for decoupled AUV heading and diving subsystems are developed by using the localization results. Particularly, the vector field guidance based on the localization results is designed to overcome the challenges, including underactuation and the lack of cable-tracking error dynamics. To minimize the electromagnetic noise interference and to relax the dependence on the accurate AUV hydrodynamics, the kinematic virtual control signals are filtered with low-pass filters, and neural networks are applied to approximate dynamics. Finally, an integrated simulation study is presented to demonstrate the effectiveness and robustness of the proposed electromagnetic localization-guided autonomous cable-tracking system.

A 5.8-GHz Phased Array FMCW Doppler Radar Front End for Object Localization and Movement Tracking in 65-nm CMOS

This article presents a fully integrated phased array frequency-modulated continuous-wave (FMCW) Doppler radar in 65-nm CMOS for noncontact object localization and movement tracking. The core 5.8-GHz sensor chip with the size of 2.3 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times$</tex-math> </inline-formula> 1.5 mm provides a peak homodyne structure receiver (RX) array gain of 47 dB and a maximum effective isotropic radiated power (EIRP) of 26 dBm on the transmitter (TX) array output with a maximum FMCW bandwidth of 275 MHz. Two <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2$</tex-math> </inline-formula> <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times$</tex-math> </inline-formula> <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$4$</tex-math> </inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1$</tex-math> </inline-formula> <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times$</tex-math> </inline-formula> <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$4$</tex-math> </inline-formula> antenna arrays have been implemented for RX and TX units, respectively, utilizing patch antenna elements each with a gain of 8 dBi. The phased array transceiver radar system achieves <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\pm{50}^{\circ}$</tex-math> </inline-formula> beam-scanning coverage and object localization from 0.5 up to 6 m away, with movement tracking.