Tracking Estimation Research Articles

Projection-Based Constrained State Estimation and Fusion for Tracking Over Long-Haul Links

In many mission-critical systems, a number of sensors are deployed to track ground targets with linear or nonlinear constraints on their motion dynamics. These sensors generate state estimates to be sent over long-haul links to a remote fusion center. We propose closed-form projection-based methods to incorporate known constraints into the estimation and fusion process. The extensive performance evaluation results demonstrate the extent to which projection-based fusion can improve the overall tracking accuracy performance in the presence of communication loss and delay.

Objective Estimation of the Radius of the Outermost Closed Isobar in Tropical Cyclones

ABSTRACT Increased understanding of the importance of TC structure in dynamical, climatological and prediction studies makes determination of TC size important. A new algorithm for the objective estimation of the radius of outermost closed isobar (roci) has been developed. The new method uses storm position and global analyses of mean sea level pressure to compute a mean (axisymmetric) roci. This radius can be used, together with the central pressure, for the construction of a synthetic vortex that is initialized in a numerical prediction model. The method also has important applications in dynamical and climatological studies of TC intensity, size and structure. The algorithm is robust and capable of estimating roci, even in the case of a weak system that may not have a closed isobar in the global analysis. The values produced by the new method are shown to be more consistent than the corresponding operational estimates which are subjective and produced under strong time constraints. Statistical comparison between subjective and objective estimates gives a mean absolute difference of 110 km, which given the difficulty in making a subjective estimate, is satisfactory. In addition, even though limitations exist with the estimates of vortex parameters like the radius to gales (r34), comparison with estimates from an extended best track data set provides independent evaluation of the scheme. Mean absolute difference for r34 for around 3200 cases is near 80 km, even though the best track estimates are subjective and the objective r34 is estimated only from storm central pressure and the objective roci. This validation suggests that the algorithm can be used to obtain useful size estimates of TCs.

A new quantile tracking algorithm using a generalized exponentially weighted average of observations

The Exponentially Weighted Average (EWA) of observations is known to be a state-of-art estimator for tracking expectations of dynamically varying data stream distributions. However, how to devise an EWA estimator to track quantiles of data stream distributions is not obvious. In this paper, we present a lightweight quantile estimator using a generalized form of the EWA. To the best of our knowledge, this work represents the first reported quantile estimator of this form in the literature. An appealing property of the estimator is that the update step size is adjusted online proportionally to the difference between current observation and the current quantile estimate. Thus, if the estimator is off-track compared to the data stream, large steps will be taken to promptly get the estimator back on-track. The convergence of the estimator to the true quantile is proven using the theory of stochastic learning. Extensive experimental results using both synthetic and real-life data show that our estimator clearly outperforms legacy state-of-the-art quantile tracking estimators and achieves faster adaptivity in dynamic environments. The quantile estimator was further tested on real-life data where the objective is efficient in online control of indoor climate. We show that the estimator can be incorporated into a concept drift detector to efficiently decide when a machine learning model used to predict future indoor temperature should be retrained/updated.

Design and simulation of a snapshot multi-focal interferometric microscope.

Realizing both high temporal and spatial resolution across a large volume is a key challenge for 3D fluorescent imaging. Towards achieving this objective, we introduce an interferometric multifocus microscopy (iMFM) system, a combination of multifocus microscopy (MFM) with two opposing objective lenses. We show that the proposed iMFM is capable of simultaneously producing multiple focal plane interferometry that provides axial super-resolution and hence isotropic 3D resolution with a single exposure. We design and simulate the iMFM microscope by employing two special diffractive optical elements. The point spread function of this new iMFM microscope is simulated and the image formation model is given. For reconstruction, we use the Richardson-Lucy deconvolution algorithm with total variation regularization for 3D extended object recovery, and a maximum likelihood estimator (MLE) for single molecule tracking. A method for determining an initial axial position of the molecule is also proposed to improve the convergence of the MLE. We demonstrate both theoretically and numerically that isotropic 3D nanoscopic localization accuracy is achievable with an axial imaging range of 2um when tracking a fluorescent molecule in three dimensions and that the diffraction limited axial resolution can be improved by 3-4 times in the single shot wide-field 3D extended object recovery. We believe that iMFM will be a useful tool in 3D dynamic event imaging that requires both high temporal and spatial resolution.

Tracking the multi‐well surface dynamometer card state for a sucker‐rod pump by using a particle filter

For a non-linear sucker-rod pumping system, a surface dynamometer card estimation algorithm based on a particle filter is presented. The dynamometer card is a plot of the polished rod load at various positions of a pump stroke. Since the polished rod load measured by a load sensor is frequently affected by drift problems, a local characteristic correlation method is proposed while building the state-space model for the pumping unit. The local characteristic correlation method makes the system insensitive to load drift problems. Moreover, the prior data recorded from different wells are used to construct the importance density. To make the k -time importance density closer to the real posterior distribution, current measurement information is used. The performance of the proposed algorithm is evaluated on the actual operating data of a Xinjiang oil field containing typical daily production activities that can cause sudden system state changes. The results show that the proposed algorithm can adapt to sudden changes of the underground environment caused by various human factors, and it can provide robust estimation for multi-well long-term state tracking.

Adaptive state estimation for tracking of civilian aircraft

Tracking of manoeuvring subsonic aerospace vehicles has traditionally been handled by state estimators. Ordinary state estimators perform poorly as the concerned process model can only be defined imprecisely. This contribution evaluates and compares the performance of adaptive single mode non-linear estimators against several versions of other estimators. The primary comparison is with the recently introduced smooth variable structure filter (SVSF) which is claimed to inherit the robustness of variable structure approach. Both the above types of estimators are then benchmarked with a well-known version of interacting multiple model (IMM) estimator which treats the manoeuvring aircraft as a hybrid system consisting of multiple modes. Monte Carlo simulation has been used and several descriptors have been used for comparison. The comparison demonstrates that a version of non-linear adaptive estimators incorporating sigma points and a single constant turn model performs substantially better than the SVSF and its tracking performance approaches that obtainable by the IMM estimator. Novelty of this contribution lies in providing a detailed comparison of the above three families of estimators, which provides adequate insight for selecting tracking estimators by trading off estimation accuracy, algorithm complexity, tuning requirement, and computational load.

A robust D–S fusion algorithm for multi-target multi-sensor with higher reliability

This paper addresses the problem of track fusion for unordered distributed sensors with unknown measurement noise. A robust Dempster–Shafer (D–S) fusion algorithm is proposed, which includes three parts, namely, the local track estimation, the track association, and the state fusion. First, a labeling VB-PHD filter is derived to present target states with track labels and the unknown measurement noises of local sensors. Next, a heuristic D–S method is proposed to determine the relationship of local tracks and fused tracks, where the accumulated information is taken into account. Finally, a fusion method is given to show the state fusion results, which can fully utilize local state estimates and measurement noise information. Simulation results are provided to illustrate the high precision of tracking and good robustness, comparing with the traditional methods.

Inertial Properties Estimation of a Passive On-orbit Object Using Polhode Analysis

Many objects in space are passive, with unknown inertial properties. If attempting to dock autonomously to an uncooperative object (one not equipped with working sensors or actuators), a motion model is required to predict the location of the desired docking location into the future. Additionally, for cooperative satellites that fail to deploy hardware, accurate knowledge of the object’s principal axes and inertia ratios may aid in diagnosing the problem. This paper develops algorithms for estimation of the analytical motion model, principal axes, and inertia ratios of a passive on-orbit object. The polhode of the object is estimated visually (for uncooperative targets) or with gyroscopes (for cooperative targets). Estimation of the principal axes is performed by calculating the body frame orientation for which ellipses and hyperbolas optimally fit the projections of the polhode onto the principal planes. Given the polhode in the object’s body frame, constraints are used to restrict the feasible inertia ratios to a single degree of freedom. Constrained optimization is then used to estimate the inertia ratios. The algorithms are validated using visual and gyroscope data from the Synchronized Position, Hold, Engage, Reorient, Experimental Satellites/Visual Estimation for Relative Tracking and Inspection of Generic Objects test platform on the International Space Station and visual data from the simulation.

Kernel density analysis reveals a halo pattern of breast cancer incidence in Connecticut

Breast cancer (BC) incidence rates in Connecticut are among the highest in the United States, and are unevenly distributed within the state. Our goal was to determine whether artificial light at night (ALAN) played a role. Using BC records obtained from the Connecticut Tumor Registry, we applied the double kernel density (DKD) estimator to produce a continuous relative risk surface of a disease throughout the State. A multi-variate analysis compared DKD and census track estimates with population density, fertility rate, percent of non-white population, population below poverty level, and ALAN levels. The analysis identified a “halo” geographic pattern of BC incidence, with the highest rates of the disease observed at distances 5-15 km from the state's major cities. The “halo” was of high-income communities, with high ALAN, located in suburban fringes of the state's main cities.

Extended Target GMPHD Filter Based on Mean Shift and Graph Structure

In view of excessive measurements partition number, a large computation load of extended target tracking and leakage estimation when the extended targets cross, an extended target tracking algorithm based on GMPHD with mean shift and graph structure is proposed. Firstly, the kernel density estimation is used to eliminate the clutter measurements. Secondly, mean shift algorithm is adopted to divide the extended target measurements set, and sub-division is considered to carry or not based on the information fed back from the updated graph structure. Then, the extended target GMPHD algorithm is used to filter. Finally, the graph structure is updated by the one-step predicted value of the filtering result, and the updated graph structure information is used to guide the measurement partition at the next moment. Matlab simulation shows that the algorithm proposed decreases largely the number of measurements partition, reduces the computational complexity, and solves the leakage estimation problem when the targets cross.

Robust eye tracking based on multiple corneal reflections for clinical applications.

A set of methods in terms of both image processing and gaze estimation for accurate eye tracking is proposed. The eye-tracker used in this study relies on the dark-pupil method with up to 12 corneal reflections and offers an unprecedented high resolution imaging of the pupil and the cornea. The potential benefits of a higher number of glints and their optimum arrangement are analyzed considering distinct light sources configurations with 12, 8, 6, 4, and 2 corneal reflections. Moreover, a normalization factor of the pupil-glint vector is proposed for each configuration. There is a tendency for increasing accuracy with the number of glints, especially vertically (0.47deg for 12 glints configuration versus 0.65deg for 2 glints configuration). Besides the number of corneal reflections, their arrangement seems to have a stronger effect. A configuration that minimizes the interference of the eyelids with the corneal reflections is desired. Finally, the normalization of the pupil-glint vectors improves the vertical eye tracking accuracy up to 43.2%. In addition, the normalization also limits the need for a higher number of light sources to achieve better spatial accuracy.

Estimation of three-dimensional radar tracking using modified extended kalman filter

Kalman filter is an estimation method by combining data and mathematical models then developed be extended Kalman filter to handle nonlinear systems. Three-dimensional radar tracking is one of example of nonlinear system. In this paper developed a modification method of extended Kalman filter from the direct decline of the three-dimensional radar tracking case. The development of this filter algorithm can solve the three-dimensional radar measurements in the case proposed in this case the target measured by radar with distance r, azimuth angle θ, and the elevation angle ϕ. Artificial covariance and mean adjusted directly on the three-dimensional radar system. Simulations result show that the proposed formulation is effective in the calculation of nonlinear measurement compared with extended Kalman filter with the value error at 0.77% until 1.15%.

Received signal strength difference–based tracking estimation method for arbitrarily moving target in wireless sensor networks

The surveillance system, which is mainly used for detecting and tracking moving targets, is one of the most significant applications of wireless sensor networks. Up to present, received signal strength indicator is the most common measuring mean for estimating the distance in sensor networks. However, in the presence of noise, it is impossible to gain the accurate distance based on received signal strength indicator. In this article, we propose a new tracking scheme based on received signal strength difference, which is the difference value of received signal strength indicators between two neighboring sampling steps. Supposing the noise has a certain degree of correlation in a certain time interval, received signal strength difference can effectively reduce the negative impact from noise. The tracking algorithm based on received signal strength difference is built: The sensor nodes collectively estimate a possible zone of the target via the signs of received signal strength difference. Next, the possible zone is further immensely shrunk to the refined zone via the absolute values of received signal strength difference. Finally, we determine the target’s final location by choosing the reference dot with the minimum norm in the refined zone. The simulation results demonstrate that the proposed tracking method achieves higher localization accuracy than the typical received signal strength indicator–based scheme. The received signal strength difference–based method also has good generality and robustness with respect to the noises with different deviation values and the target following arbitrarily state model.

Can Family Planning Service Statistics Be Used to Track Population-Level Outcomes?

The need for annual family planning program tracking data under the Family Planning 2020 (FP2020) initiative has contributed to renewed interest in family planning service statistics as a potential data source for annual estimates of the modern contraceptive prevalence rate (mCPR). We sought to assess (1) how well a set of commonly recorded data elements in routine service statistics systems could, with some fairly simple adjustments, track key population-level outcome indicators, and (2) whether some data elements performed better than others. We used data from 22 countries in Africa and Asia to analyze 3 data elements collected from service statistics: (1) number of contraceptive commodities distributed to clients, (2) number of family planning service visits, and (3) number of current contraceptive users. Data quality was assessed via analysis of mean square errors, using the United Nations Population Division World Contraceptive Use annual mCPR estimates as the "gold standard." We also examined the magnitude of several components of measurement error: (1) variance, (2) level bias, and (3) slope (or trend) bias. Our results indicate modest levels of tracking error for data on commodities to clients (7%) and service visits (10%), and somewhat higher error rates for data on current users (19%). Variance and slope bias were relatively small for all data elements. Level bias was by far the largest contributor to tracking error. Paired comparisons of data elements in countries that collected at least 2 of the 3 data elements indicated a modest advantage of data on commodities to clients. None of the data elements considered was sufficiently accurate to be used to produce reliable stand-alone annual estimates of mCPR. However, the relatively low levels of variance and slope bias indicate that trends calculated from these 3 data elements can be productively used in conjunction with the Family Planning Estimation Tool (FPET) currently used to produce annual mCPR tracking estimates for FP2020.

Object Tracking with LiDAR: Monitoring Taxiing and Landing Aircraft

Mobile light detection and ranging (LiDAR) sensors used in car navigation and robotics, such as the Velodyne’s VLP-16 and HDL-32E, allow for sensing the surroundings of the platform with high temporal resolution to detect obstacles, tracking objects and support path planning. This study investigates the feasibility of using LiDAR sensors for tracking taxiing or landing aircraft close to the ground to improve airport safety. A prototype system was developed and installed at an airfield to capture point clouds to monitor aircraft operations. One of the challenges of accurate object tracking using the Velodyne sensors is the relatively small vertical field of view (30°, 41.3°) and angular resolution (1.33°, 2°), resulting in a small number of points of the tracked object. The point density decreases with the object–sensor distance, and is already sparse at a moderate range of 30–40 m. The paper introduces our model-based tracking algorithms, including volume minimization and cube trajectories, to address the optimal estimation of object motion and tracking based on sparse point clouds. Using a network of sensors, multiple tests were conducted at an airport to assess the performance of the demonstration system and the algorithms developed. The investigation was focused on monitoring small aircraft moving on runways and taxiways, and the results indicate less than 0.7 m/s and 17 cm velocity and positioning accuracy achieved, respectively. Overall, based on our findings, this technology is promising not only for aircraft monitoring but for airport applications.

Motion prediction and delay compensation for improved teleoperation of an exoskeletal robot

Delay is a major challenge in teleoperation. To teleoperate a robot in real-time with specific human motion such as walking, prediction and compensation algorithms are needed to minimize the tracking delay. Human walking possesses the inherent features of synergy and quasi-periodicity with nonlinearity. By analyzing these features, three prediction methods were designed. A delay-coordinate phase space reconstruction method is based on the delay embedding theorem and uses the historical data. The echo prediction method uses data from the coupling joints of the other body side and takes the Euclidean distances in the delay-coordinate phase space reconstruction method for data search and match. The fused prediction method combines the delay-coordinate phase space reconstruction and echo prediction methods, uses the best matching historical data of the same joint and the corresponding joint of the other limb. These methods were implemented for teleoperation of an exoskeletal robot with inertial measurement unit measurements of human walking. The predicted trajectories were used to compensate the teleoperation delay and improve the robot tracking performance. Experimental results demonstrated that the fused method brought apparent improvement over other methods and can serve as a promising solution for time delay estimation, prediction, and compensation of human walking tracking and teleoperation.

Output Feedback Look-ahead Position Control of Electrically Driven Fast Surface Vessels

Robust tracking control is of great importance for the surface vessels applications. This paper addresses the design of a trajectory tracking controller for fast underactuated ships in the presence of model uncertainties without velocity measurements in the yaw and surge directions. An observer-based trajectory tracking controller is proposed for the fast underactuated ship model. Then, the dynamic surface control approach is effectively exploited to propose a tracking controller considering the actuator dynamics. An adaptive robust controller is also used to compensate both the parametric and non-parametric uncertainties in the fast underactuated ship model. A Lyapunov-based stability analysis is utilised to guarantee that tracking and state estimation errors are uniformly ultimately bounded. Simulation results are presented to illustrate the feasibility and efficiency of the proposed controller.

A statewide investigation of geographic lung cancer incidence patterns and radon exposure in a low-smoking population

BackgroundLung cancer is the leading cause of cancer-related mortality in Utah despite having the nation’s lowest smoking rate. Radon exposure and differences in lung cancer incidence between nonmetropolitan and metropolitan areas may explain this phenomenon. We compared smoking-adjusted lung cancer incidence rates between nonmetropolitan and metropolitan counties by predicted indoor radon level, sex, and cancer stage. We also compared lung cancer incidence by county classification between Utah and all SEER sites.MethodsSEER*Stat provided annual age-adjusted rates per 100,000 from 1991 to 2010 for each Utah county and all other SEER sites. County classification, stage, and sex were obtained from SEER*Stat. Smoking was obtained from Environmental Public Health Tracking estimates by Ortega et al. EPA provided low (< 2 pCi/L), moderate (2–4 pCi/L), and high (> 4 pCi/L) indoor radon levels for each county. Poisson models calculated overall, cancer stage, and sex-specific rates and p-values for smoking-adjusted and unadjusted models. LOESS smoothed trend lines compared incidence rates between Utah and all SEER sites by county classification.ResultsAll metropolitan counties had moderate radon levels; 12 (63%) of the 19 nonmetropolitan counties had moderate predicted radon levels and 7 (37%) had high predicted radon levels. Lung cancer incidence rates were higher in nonmetropolitan counties than metropolitan counties (34.8 vs 29.7 per 100,000, respectively). Incidence of distant stage cancers was significantly higher in nonmetropolitan counties after controlling for smoking (16.7 vs 15.4, p = 0.02*). Incidence rates in metropolitan, moderate radon and nonmetropolitan, moderate radon counties were similar. Nonmetropolitan, high radon counties had a significantly higher incidence of lung cancer compared to nonmetropolitan, moderate radon counties after adjustment for smoking (41.7 vs 29.2, p < 0.0001*). Lung cancer incidence patterns in Utah were opposite of metropolitan/nonmetropolitan trends in other SEER sites.ConclusionLung cancer incidence and distant stage incidence rates were consistently higher in nonmetropolitan Utah counties than metropolitan counties, suggesting that limited access to preventative screenings may play a role in this disparity. Smoking-adjusted incidence rates in nonmetropolitan, high radon counties were significantly higher than moderate radon counties, suggesting that radon was also major contributor to lung cancer in these regions. National studies should account for geographic and environmental factors when examining nonmetropolitan/metropolitan differences in lung cancer.

Efficient visual tracking using multi-feature regularized robust sparse coding and quantum particle filter based localization

Visual object tracking is a challenging task in the field of computer vision, due to many constraints like scene variation, occlusion, cluttered background and higher data size. The sparse representation tool of the compressive sensing theory, turns out to be an effective way to implement the object tracking algorithm with less computational load and at a faster speed. In this paper, we have proposed an effective object tracking framework by using a regularized robust sparse coding (RRSC) for representing the multi-feature templates of the candidate objects. Moreover, an efficient Quantum Particle Filter (QPF) based Bayesian state estimation for tracking is also proposed for localizing the target object in the subsequent frames. The RRSC assures robustness to occlusion and noise, while the QPF successfully deals with the abrupt motion of the object. The Local Binary Pattern (LBP) feature and the Ohta color feature, reconciled in appearance modelling enhance the discriminant description. Both subjective as well as objective evaluation of the proposed tracking method is carried out for validating its efficacy in comparison to the other state-of-the-art methods. The evaluation is executed on different publicly available data sets, which illustrates the superiority of the proposed method.

Wearable Heading Estimation for Motion Tracking in Health Care by Adaptive Fusion of Visual-Inertial Measurements.

The increasing demand for health informatics has become a far-reaching trend in the ageing society. The utilization of wearable sensors enables monitoring senior people daily activities in free-living environments, conveniently and effectively. Among the primary health-care sensing categories, the wearable visual-inertial modality for human motion tracking gradually exerts promising potentials. In this paper, we present a novel wearable heading estimation strategy to track the movements of human limbs. It adaptively fuses inertial measurements with visual features following locality constraints. Body movements are classified into two types: general motion (which consists of both rotation and translation). or degenerate motion (which consists of only rotation). A specific number of feature correspondences between camera frames are adaptively chosen to satisfy both the feature descriptor similarity constraint and the locality constraint. The selected feature correspondences and inertial quaternions are employed to calculate the initial pose, followed by the coarse-to-fine procedure to iteratively remove visual outliers. Eventually, the ultimate heading is optimized using the correct feature matches. The proposed method has been thoroughly evaluated on the straight-line, rotatory and ambulatory movement scenarios. As the system is lightweight and requires small computational resources, it enables effective and unobtrusive human motion monitoring, especially for the senior citizens in the long-term rehabilitation.