Localization Tracking Research Articles

A Portable FMCW Interferometry Radar With Programmable Low-IF Architecture for Localization, ISAR Imaging, and Vital Sign Tracking

This paper presents a portable radar system for short-range localization, inverse synthetic aperture radar imaging, and vital sign tracking. The proposed sensor incorporates frequency-modulated continuous-wave (FMCW) and interferometry (Doppler) modes, which enable this radar system to obtain both absolute range information and tiny vital signs (i.e., respiration and heartbeat) of human targets. These two different operation modes can be switched through an on-board microcontroller. To simplify the system, the proposed radar utilizes the audio card of a laptop to sample the baseband signal. The FMCW mode of the radar uses operational-amplifier-based circuits to generate an analog sawtooth signal and a reference pulse sequence (RPS). The RPS is locked to the sawtooth signal to obtain coherence for the radar system. For the interferometry mode, a low-intermediate-frequency modulation method is implemented to avoid the slow vital signs from being distorted by the high-pass filter of the audio card. Several experiments were carried out to reveal the capability and distinct operational features of the proposed portable hybrid radar. The experiments also showed that the system can easily detect glass, which is usually difficult to identify for optical-based sensors. In addition, 2-D scanning in a complex environment revealed that the proposed radar was able to differentiate human targets from other objects. Moreover, ISAR images were used to isolate moving human targets from surrounding clutter. Finally, the proposed radar also demonstrated its ability to accurately measure vital signs when a human subject sits still.

Adaptive-Repetitive Visual-Servo Control of Low-Flying Aerial Robots via Uncalibrated High-Flying Cameras

This paper presents the design and implementation of an adaptive-repetitive visual-servo control system for a moving high-flying vehicle (HFV) with an uncalibrated camera to monitor, track, and precisely control the movements of a low-flying vehicle (LFV) or mobile ground robot. Applications of this control strategy include the use of high-flying unmanned aerial vehicles (UAVs) with computer vision for monitoring, controlling, and coordinating the movements of lower altitude agents in areas, for example, where GPS signals may be unreliable or nonexistent. When deployed, a remote operator of the HFV defines the desired trajectory for the LFV in the HFV’s camera frame. Due to the circular motion of the HFV, the resulting motion trajectory of the LFV in the image frame can be periodic in time, thus an adaptive-repetitive control system is exploited for regulation and/or trajectory tracking. The adaptive control law is able to handle uncertainties in the camera’s intrinsic and extrinsic parameters. The design and stability analysis of the closed-loop control system is presented, where Lyapunov stability is shown. Simulation and experimental results are presented to demonstrate the effectiveness of the method for controlling the movement of a low-flying quadcopter, demonstrating the capabilities of the visual-servo control system for localization (i.e.,, motion capturing) and trajectory tracking control. In fact, results show that the LFV can be commanded to hover in place as well as track a user-defined flower-shaped closed trajectory, while the HFV and camera system circulates above with constant angular velocity. On average, the proposed adaptive-repetitive visual-servo control system reduces the average RMS tracking error by over 77% in the image plane and over 71% in the world frame compared to using just the adaptive visual-servo control law.

Intracellular responsive dual delivery by endosomolytic polyplexes carrying DNA anchored porous silicon nanoparticles

Bioresponsive cytosolic nanobased multidelivery has been emerging as an enormously challenging novel concept due to the intrinsic protective barriers of the cells and hardly controllable performances of nanomaterials. Here, we present a new paradigm to advance nano-in-nano integration technology amenable to create multifunctional nanovehicles showing considerable promise to overcome restrictions of intracellular delivery, solve impediments of endosomal localization and aid effectual tracking of nanoparticles. A redox responsive intercalator chemistry comprised of cystine and 9-aminoacridine is designed as a cross-linker to cap carboxylated porous silicon nanoparticles with DNA. These intelligent nanocarriers are then encapsulated within novel one-pot electrostatically complexed nano-networks made of a zwitterionic amino acid (cysteine), an anionic bioadhesive polymer (poly(methyl vinyl ether-alt-maleic acid)) and a cationic endosomolytic polymer (polyethyleneimine). This combined nanocomposite is successfully tested for the co-delivery of hydrophobic (sorafenib) or hydrophilic (calcein) molecules loaded within the porous core, and an imaging agent covalently integrated into the polyplex shell by click chemistry. High loading capacity, low cyto- and hemo-toxicity, glutathione responsive on-command drug release, and superior cytosolic delivery are shown as achievable key features of the proposed formulation. Overall, formulating drug molecules, DNA and imaging agents, without any interference, in a physico-chemically optimized carrier may open a path towards broad applicability of these cost-effective multivalent nanocomposites for treating different diseases.

Range–Doppler‐based centralised framework for human target tracking in multistatic radar

Human target detection and tracking have great potential in surveillance, rescue and security applications. Traditional human detection and tracking are performed on the range profile. If targets are close or overlapped in range, it is difficult to distinguish these targets. As velocity provides another aspect to distinguish targets, a novel framework is proposed for human tracking using both range and velocity information. The tracking framework consists of six steps, including clutter reduction, range–Doppler (RD) calculation, target detection, measurement estimation, target localisation and target tracking. Primary attention is devoted to the middle four steps. The calculation process of the RD image is described in detail. The ordered statistics constant false alarm rate detector is extended to a two-dimensional scenario for the RD target detection. An efficient approach is given for automatic measurement estimation. A minimum root-mean-square error pruning algorithm is proposed for multi-target localisation. As the algorithm combines both range and velocity information for measurement association, it clearly shows a lower wrong association probability than the method using range information only. The effectiveness of the proposed tracking framework is evaluated by the experimental data in the foliage-penetration environment.

EM-Based High Speed Wireless Sensor Networks for Underwater Surveillance and Target Tracking

Underwater Wireless Sensor Networks (UWSNs) are considered as tangible, low cost solution for underwater surveillance and exploration. Existing acoustic wave-based UWSN systems fail to meet the growing demand for fast data rates required in military operations, oil/gas exploration, and oceanographic data collection. Electromagnetic (EM) wave-based communication systems, on the other hand, have great potential for providing high speed data rates in such scenarios. This paper will(1)discuss the challenges faced in the utilization of EM waves for the design of tactical underwater surveillance systems and(2)evaluate several EM wave-based three-dimensional (3D) UWSN architectures differing in topologies and/or operation principles on the performance of localization and target tracking. To the best of our knowledge, this is the first of its kind in the field of underwater communications where underwater surveillance techniques for EM wave-based high speed UWSNs have been investigated. Thus, this will be a major step towards achieving future high speed UWSNs.

On the Probability of Detection Ability in Observing Dynamic Environmental Phenomena using Wireless Sensor Networks

Wireless Sensor Network (WSN) is being utilised for several purposes in military and civil domains, including surveillance, monitoring, and management, where networked sensors monitor and detect an event of interest and report to the concerned party through the WSN or the Internet infrastructure. Due to the characteristics of WSN, there are many fundamental technical challenges including the node deployment, event localization, and event tracking, among which, the probability of the event observability has a crucial role. The observability is defined as the capability of observing an evolving event in the monitoring area. The probability of detection ability in observing an event depends on the parameters of the detection function, which in turn rely on the sensor technology and the nature of the surrounding environment. This paper addresses the observation of an event using WSN and how accurately the event is observed in the monitoring area. It presents a practical solution for event observability after formulizing and establishing the complexity of observability issue and tackling its relation and impact on node deployment and event localization. Hence, a feasible event observation model has been proposed and validated in this paper. The numerical results of the experimental evaluation have confirmed that an accurate detection of an occurring event can be achieved by the proposed model.

Mean LBP and modified fuzzy C-means weighted hybrid feature for illumination invariant mean-shift tracking

Object tracking is a critical task in surveillance and activity analysis. Two main issues for tracking are appearance (illumination) and structural (size of a target) variations of the object. We propose a method which is robust and addresses these issues by incorporating features that are less variant to these changes. The proposed features are mean local binary pattern (mLBP), an illumination invariant texture feature, and modified fuzzy c-means (MFCM) weighted color histogram to handle both illumination and scale changes. These features are combined to form a hybrid mean-shift (MS) vector and used in the MS vector framework for target tracking. Experimental results using standard benchmark videos show that the proposed scheme can lead to better localization and robust tracking in challenging illumination scenarios, when compared to several existing tracking algorithms.

Performance Analysis of Routing Protocols for Target Tracking in Wireless Sensor Networks

Wireless sensor networks (WSNs) are large scale integration in large topology deployed with thousands sensor nodes. Nodes sizes are very small, with low cost, low weight, and limited battery, primary storage, processing power. Sensor nodes have wireless communication capabilities with sensor to monitor physical or environmental conditions. This paper study and evaluate performance for localization and target tracking application with proposed hierarchical localization tracking scheme based on hierarchical binary tree structure. The target detected information is stored at multiple sensor nodes (e.g. node, parent node and grandparent node) which deployed using complete binary tree structure to improve fault tolerance. This drastically reduces number of messaging in the network. Performance of proposed scheme and some existing routing scheme is evaluated using NS2. Simulation result proof increased in network lifetime by 25%, target detection probability by 25%, and reduces error rate by 20%, increased energy efficiency by 20%, fault tolerance, and routing efficiency.

An Automatic Facial Localization Tracking Identification Biometric System through PCA and Wavelet Distribution in 3D Mesh Environment

Background/Objectives: In this paper, the process of automatically facial region localization and tracking in video frames through 3D mesh model is estimated. Methods/Statistical Analysis: The morphological regions of the face are modeled into 18 geometry based regions based on the various shape and expressions. The feature is estimated based on the covariance matrix in high region space. Then, it undergoes to PCA to estimate facial deformations. Findings: The patterns feature is extracted and it mapped with the multi geometry mapping. Then, the corresponding wavelet transforms extracted region into various dimension for geometry matching for classification. The proposed method achieves robustness based on its accurate transformation through wavelet analysis. The XM2VTSDB multi-modal face database is used to compare the real video sequence images. Application/Improvements: The experimental evaluation shows favorable results and yields 99% detection rate over 15000 video frame images. The frames in the facial tracking data are calculated through various parameters and compared with the ground truth against standard Euler angles of the muti-modal database.

An Optimization Based Approach for Relative Localization and Relative Tracking Control in Multi-Robot Systems

In this paper, an optimization based method is used for relative localization and relative trajectory tracking control in Multi-Robot Systems (MRS's). In this framework, one or more robots are located and commanded to follow time varying trajectories with respect to another (possibly moving) robot reference frame. Such systems are suitable for a considerable number of applications, e.g. patrolling missions, searching operations, perimeter surveillance, and area coverage. Here, the nonlinear and constrained motion and measurement models in an MRS are incorporated to achieve an accurate state estimation algorithm based on nonlinear Moving Horizon Estimation (MHE) and a tracking control method based on Nonlinear Model Predictive Control (NMPC). In order to fulfill the real-time requirements, a fast and efficient algorithm based on a Real Time Iteration (RTI) scheme and automatic C-code generation, is adopted. Numerical simulations are conducted to: first, compare the performance of MHE against the traditional estimator used for relative localization, i.e. extended Kalman filter (EKF); second, evaluate the utilized relative localization and tracking control algorithm when applied to a team of multiple robots; finally, laboratory experiments are performed, for real-time performance evaluation. The conducted simulations validated the adopted algorithm and the experiments demonstrated its practical applicability.

Proteomics Analysis with a Nano Random Forest Approach Reveals Novel Functional Interactions Regulated by SMC Complexes on Mitotic Chromosomes

Packaging of DNA into condensed chromosomes during mitosis is essential for the faithful segregation of the genome into daughter nuclei. Although the structure and composition of mitotic chromosomes have been studied for over 30 years, these aspects are yet to be fully elucidated. Here, we used stable isotope labeling with amino acids in cell culture to compare the proteomes of mitotic chromosomes isolated from cell lines harboring conditional knockouts of members of the condensin (SMC2, CAP-H, CAP-D3), cohesin (Scc1/Rad21), and SMC5/6 (SMC5) complexes. Our analysis revealed that these complexes associate with chromosomes independently of each other, with the SMC5/6 complex showing no significant dependence on any other chromosomal proteins during mitosis. To identify subtle relationships between chromosomal proteins, we employed a nano Random Forest (nanoRF) approach to detect protein complexes and the relationships between them. Our nanoRF results suggested that as few as 113 of 5058 detected chromosomal proteins are functionally linked to chromosome structure and segregation. Furthermore, nanoRF data revealed 23 proteins that were not previously suspected to have functional interactions with complexes playing important roles in mitosis. Subsequent small-interfering-RNA-based validation and localization tracking by green fluorescent protein-tagging highlighted novel candidates that might play significant roles in mitotic progression.

Moving-horizon nonlinear least squares-based multirobot cooperative perception

In this article we present an online estimator for multirobot cooperative localization and target tracking based on nonlinear least squares minimization. Our method not only makes the rigorous optimization-based approach applicable online but also allows the estimator to be stable and convergent. We do so by employing a moving horizon technique to nonlinear least squares minimization and a novel design of the arrival cost function that ensures stability and convergence of the estimator. Through an extensive set of real robot experiments, we demonstrate the robustness of our method as well as the optimality of the arrival cost function. The experiments include comparisons of our method with (i) an extended Kalman filter-based online-estimator and (ii) an offline-estimator based on full-trajectory nonlinear least squares.

SU‐F‐T‐321: The Effect of an Electromagnetic Array Used for Patient Localization and Tumor Tracking On OSLD in Vivo Dosimetry

Purpose:The purpose of this study was to observe the effect of an electromagnetic array used for patient localization and tumor tracking on optically‐stimulated luminescent in‐vivo dosimetry.Methods:A linear accelerator equipped with four photon energies was used to irradiate optically stimulated luminescent dosimeters (OSLDs) at the respective dmax depths and in the buildup region, with and without the presence of an electromagnetic array used for tumor tracking and patient localization. The OSLDs were placed on solid water slabs under 5 mm bolus and on each face of an octagonal phantom, and irradiated using both static beam and arc geometry, with and without the electromagnetic array under our setup. The electromagnetic array was placed 6 cm above the phantom to coincide with similar distances used during patient treatment. Ionization chamber measurements in a water phantom were also taken initially for comparison with the simple geometry OSLD measurements and published data.Results:Under simple geometry, a negligible change was observed at dmax for all energies when the electromagnetic array was placed over the setup. When measuring at five millimeter depth, increases of 1.3/3.1/16/18% were observed for energies 4X/6X/10X/15X respectively when the electromagnetic array was in place. Measurements using the octagonal phantom yielded scattered results for the lateral and posterior oblique fields, and showed increases in dose to the OSLDs placed on the anterior and lateral anterior faces of the phantom.Conclusion:Placing the electromagnetic array very close to the patient's surface acts as a beam spoiler in the buildup region (at 5 mm depth), which in turn causes an increase in the measured dose reading of the OSLD. This increase in dose is more pronounced when the OSLD is placed directly underneath the electromagnetic array than off to one side or the other.

Enhanced Monte Carlo localization incorporating a mechanism for preventing premature convergence

SUMMARYIn this paper, we propose an enhanced Monte Carlo localization (EMCL) algorithm for mobile robots, which deals with the premature convergence problem in global localization as well as the estimation error existing in pose tracking. By incorporating a mechanism for preventing premature convergence (MPPC), which uses a “reference relative vector” to modify the weight of each sample, exploration of a highly symmetrical environment can be improved. As a consequence, the proposed method has the ability to converge particles toward the global optimum, resulting in successful global localization. Furthermore, by applying the unscented Kalman Filter (UKF) to the prediction state and the previous state of particles in Monte Carlo Localization (MCL), an EMCL can be established for pose tracking, where the prediction state is modified by the Kalman gain derived from the modified prior error covariance. Hence, a better approximation that reduces the discrepancy between the state of the robot and the estimation can be obtained. Simulations and practical experiments confirmed that the proposed approach can improve the localization performance in both global localization and pose tracking.

Multi-View 3D Human Tracking in Crowded Scenes

This paper presents a robust multi-view method for tracking people in 3D scene. Our method distinguishes itself from previous works in two aspects. Firstly, we define a set of binary spatial relationships for individual subjects or pairs of subjects that appear at the same time, e.g. being left or right, being closer or further to the camera, etc. These binary relationships directly reflect relative positions of subjects in 3D scene and thus should be persisted during inference. Secondly, we introduce an unified probabilistic framework to exploit binary spatial constraints for simultaneous 3D localization and cross-view human tracking. We develop a cluster Markov Chain Monte Carlo method to search the optimal solution. We evaluate our method on both public video benchmarks and newly built multi-view video dataset. Results with comparisons showed that our method could achieve state-of-the-art tracking results and meter-level 3D localization on challenging videos.

Kinematic trajectory tracking controller for an all-terrain Ackermann steering vehicle

In the last few years the number of applications of autonomous mobile robots to outdoor and off-road tasks, like border surveillance and monitoring, search and rescue, agriculture, driveless mobility has been rapidly increasing. Among the huge number of functionalities that are required to make a vehicle an autonomous robot, localisation, path planning and trajectory tracking are the most important. This paper proposes a novel approach to solve the trajectory tracking problem of an Ackermann steering vehicle. A multi-body dynamic model of the vehicle is proposed as a mean to tune and validate the tracking controller. The proposal is supported by an experimental validation, that shows the effectiveness of the trajectory tracking controller and its performance in comparison with the accuracy of the localisation algorithm.

Adaptive Environmental Source Localization and Tracking with Unknown Permittivity and Path Loss Coefficients.

Accurate signal-source and signal-reflector target localization tasks via mobile sensory units and wireless sensor networks (WSNs), including those for environmental monitoring via sensory UAVs, require precise knowledge of specific signal propagation properties of the environment, which are permittivity and path loss coefficients for the electromagnetic signal case. Thus, accurate estimation of these coefficients has significant importance for the accuracy of location estimates. In this paper, we propose a geometric cooperative technique to instantaneously estimate such coefficients, with details provided for received signal strength (RSS) and time-of-flight (TOF)-based range sensors. The proposed technique is integrated to a recursive least squares (RLS)-based adaptive localization scheme and an adaptive motion control law, to construct adaptive target localization and adaptive target tracking algorithms, respectively, that are robust to uncertainties in aforementioned environmental signal propagation coefficients. The efficiency of the proposed adaptive localization and tracking techniques are both mathematically analysed and verified via simulation experiments.

Self-contained image mapping of placental vasculature in 3D ultrasound-guided fetoscopy.

Surgical navigation technology directed at fetoscopic procedures is relatively underdeveloped compared with other forms of endoscopy. The narrow fetoscopic field of views and the vast vascular network on the placenta make examination and photocoagulation treatment of twin-to-twin transfusion syndrome challenging. Though ultrasonography is used for intraoperative guidance, its navigational ability is not fully exploited. This work aims to integrate 3D ultrasound imaging and endoscopic vision seamlessly for placental vasculature mapping through a self-contained framework without external navigational devices. This is achieved through development, integration, and experimentation of novel navigational modules. Firstly, a framework design that addresses the current limitations based on identified gaps is conceptualized. Secondly, integration of navigational modules including (1) ultrasound-based localization, (2) image alignment, and (3) vision-based tracking to update the scene texture map is implemented. This updated texture map is projected to an ultrasound-constructed 3D model for photorealistic texturing of the 3D scene creating a panoramic view of the moving fetoscope. In addition, a collaborative scheme for the integration of the modular workflow system is proposed to schedule updates in a systematic fashion. Finally, experiments are carried out to evaluate each modular variation and an integrated collaborative scheme of the framework. The modules and the collaborative scheme are evaluated through a series of phantom experiments with controlled trajectories for repeatability. The collaborative framework demonstrated the best accuracy (5.2% RMS error) compared with all the three single-module variations during the experiment. Validation on an ex vivo monkey placenta shows visual continuity of the freehand fetoscopic panorama. The proposed developed collaborative framework and the evaluation study of the framework variations provide analytical insights for effective integration of ultrasonography and endoscopy. This contributes to the development of navigation techniques in fetoscopic procedures and can potentially be extended to other applications in intraoperative imaging.

UWB-Aided Inertial Motion Capture for Lower Body 3-D Dynamic Activity and Trajectory Tracking

This paper introduces a novel method for simultaneous 3-D trajectory tracking and lower body motion capture (MoCap) under various dynamic activities such as walking and jumping. The proposed method uses wearable inertial sensors fused with an ultrawideband localization system using a cascaded Kalman filter-based sensor fusion algorithm, which consists of a separate orientation filter cascaded with a position/velocity filter. In addition, to further improve the joint angle tracking accuracy, anatomical constraints are applied to the knee joint. Currently, available self-contained inertial tracking methods are not only drift-prone over time but also their accuracy is degraded under fast movements with unstable ground contact states due to the lack of external references. However, our experimental results, which benchmark the system against a reference camera-based motion tracking system, show that the proposed fusion method can accurately capture the dynamic activities of a subject without drift. The results show that the proposed system can maintain similar accuracies between fast and slow motions in lower body MoCap and 3-D trajectory tracking. The obtained accuracies of the system for 3-D body localization and knee joint angle tracking for fast motions were less than 5 cm and 2.1°, respectively.

Indoor Positioning of a Robotic Walking Assistant for Large Public Environments

Indoor localization and position tracking are essential to support applications and services for ambient-assisted living. While the problem of indoor localization is still open and already quite complex per se, in large public places, additional issues of cost, accuracy, and scalability arise. In this paper, the position estimation and tracking technique developed within the project devices for assisted living (DALi) is described, analyzed through simulations, and finally validated by means of a variety of experiments on the field. The goal of the DALi project is to design a robotic wheeled walker guiding people with psychomotor problems. Indeed, people with motor or cognitive impairments are often afraid of moving in large and crowded environments (e.g., because they could lose the sense of direction). In order to mitigate this problem, the position tracking approach described in this paper is based on multisensor data fusion and it is conceived to assure a good tradeoff between target accuracy, level of confidence, and deployment costs. Quite interestingly, the same approach could be used for indoor automated guided vehicles and robotics.