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Sequential Bayesian filters in non-linear dynamic systems require the recursive estimation of the predictive and posterior distributions. This paper introduces a Bayesian filter called the adaptive kernel Kalman filter (AKKF). With this filter, the arbitrary predictive and posterior distributions of hidden states are approximated using the empirical kernel mean embeddings (KMEs) in reproducing kernel Hilbert spaces (RKHSs). In parallel with the KMEs, some particles, in the data space, are used to capture the properties of the dynamical system model. Specifically, particles are generated and updated in the data space, while the corresponding kernel weight mean vector and covariance matrix associated with the feature mappings of the particles are predicted and updated in the RKHSs based on the kernel Kalman rule (KKR). Simulation results are presented to confirm the improved performance of our approach with significantly reduced particle numbers, by comparing with the unscented Kalman filter (UKF), particle filter (PF) and Gaussian particle filter (GPF). For example, compared with the GPF, the proposed approach provides around 5% logarithmic mean square error (LMSE) tracking performance improvement in the bearing-only tracking (BOT) system when using 50 particles., Comment: The manuscript has been accepted for publication as a regular paper in the IEEE Transactions on Signal Processing

Data assimilation (DA) is a methodology widely used by different disciplines of science and engineering. It is typically applied to continuous systems with numerical models. The application of DA to discrete-event and discrete-time systems including agent-based models is relatively new. Because of its non-linearity and non-Gaussianity, the particle filter (PF) method is often a good option for stochastic simulation models of discrete systems. The probability distributions of model runs, however, make it computationally intensive. The experimental conditions therein are understudied. This paper studied three critical conditions of PF-based DA in a discrete event model: (1) the time interval between two consecutive DA iterations, (2) the number of particles, and (3) the actual level and perceived level of measurement errors (or noises). The study conducted identical-twin experiments of an M/M/1 single server queuing system. The ground truth is imitated in a stand-alone simulation model. The measurement errors are superimposed so that the effect of the three conditions can be quantitatively evaluated in a controlled manner. The results show that the estimation accuracy of such a system using PF is more constrained by the choice of time intervals than the number of particles. An under estimation of measurement errors produces worse state estimates than an over estimation of errors. A correct perception of the measurement errors does not guarantee better state estimates. Moreover, a slight over estimation of errors results in better state estimates, and it is more responsive to abrupt system changes than an accurate perception of measurement errors.

State-space models (SSMs) are a rich class of dynamical models with a wide range of applications in economics, healthcare, computational biology, robotics, and more. Proper analysis, control, learning, and decision-making in dynamical systems modeled by SSMs depend on the accuracy of the inferred/learned model. Most of the existing inference techniques for SSMs are capable of dealing with very small systems, unable to be applied to most of the large-scale practical problems. Toward this, this article introduces a two-stage Bayesian optimization (BO) framework for scalable and efficient inference in SSMs. The proposed framework maps the original large parameter space to a reduced space, containing a small linear combination of the original space. This reduced space, which captures the most variability in the inference function (e.g., log likelihood or log a posteriori), is obtained by eigenvalue decomposition of the covariance of gradients of the inference function approximated by a particle filtering scheme. Then, an exponential reduction in the search space of parameters during the inference process is achieved through the proposed two-stage BO policy, where the solution of the first-stage BO policy in the reduced space specifies the search space of the second-stage BO in the original space. The proposed framework's accuracy and speed are demonstrated through several experiments, including real metagenomics data from a gut microbial community.

This article proposes a navigation scheme for a wheeled robot in unknown environments. The navigation scheme consists of obstacle boundary following (OBF), target seeking (TS), and vertex point seeking (VPS) behaviors and a behavior supervisor. The OBF behavior is achieved by a fuzzy controller (FC). This article formulates the FC design problem as a new constrained multiobjective optimization problem and finds a set of nondominated FC solutions through the combination of expert knowledge and data-driven multiobjective ant colony optimization. The TS behavior is achieved by new fuzzy proportional-integral-derivative (PID) and proportional-derivative (PD) controllers that control the orientation and speed of the robot, respectively. The VPS behavior is proposed to shorten the navigation route by controlling the robot to move toward a new subgoal determined from the vertex point of an obstacle. A new behavior supervisor that manages the switching among the OBF, TS, and VPS behaviors in unknown environments is proposed. In the navigation of a real robot, a new robot localization method through the fusion of encoders and an infrared localization sensor using a particle filter is proposed. Finally, this article presents simulations and experiments to verify the feasibility and advantages of the navigation scheme.

Short-term estimation and prediction of pedestrian density in urban hot spots (e.g., railway station, shopping mall, etc.) is an important topic for traffic management and control in densely populated areas. In this paper, we propose a short-term pedestrian density estimation and prediction method based on mobile phone data. Firstly, pedestrian density in hot spots is estimated using mobile phone data. To decrease the positioning errors of mobile phone data, a modified particle filter method, which considers the movements of pedestrians, is applied for pre-processing the data. An efficient spatial access method (i.e., Hilbert R-tree) is adopted to construct pedestrians' position indexes for realizing the short-term estimation. Secondly, based on the estimation results, the spatiotemporal extended Kalman filter (SEKF) is proposed for the short-term prediction of pedestrian density. A massive mobile phone dataset collected in Nanjing, China is used in the case study. The estimated pedestrian density from Monday to Thursday is used for pedestrian density prediction on Friday. The results show that the proposed method can estimate and predict pedestrian density in hot spots, especially in small-scale sites of hot spots efficiently in a short time. Comparing with classical prediction methods, the proposed SEKF method predicts short-term pedestrian density in urban hot spots more accurately.

Real-time traffic state estimation and prediction are of importance to the traffic management systems. New opportunities are enabled by the emerging sensing and automation technologies to manage connected and automated traffic, particularly in terms of controlling trajectories of automated vehicles. Traffic information from connected and automated vehicles (CAV) and roadside detectors (RSD) can be fused and has great potential for providing detailed microscopic traffic states (i.e., vehicle speeds, positions) of all vehicles. In this paper, we propose a cooperative perception framework for this purpose. The proposed framework based on particle filtering is developed to provide an accurate estimation and prediction of the microscopic states of partially observed traffic systems, while accounting for different sources of errors that intrinsically exist in the system, including those from sensor data, vehicle movement, and process models. Selected freeway and arterial vehicle trajectory datasets from the Next Generation Simulation (NGSIM) program and CAV traffic simulation are applied to test the proposed methodological framework. The accuracy of position and speed estimation is between 50% and 70% when the CAV market penetration rate (MPR) is 12.5%, and between 80% and 90% when the MPR is 50%. The incorporation of RSD data can further increase the accuracy by up to 10% under low CAV MPRs. The framework can also provide an accurate short-term prediction (i.e., 5 - 15 seconds) of position and speed with 60% to 90% accuracy. The proposed framework provides efficient and accurate estimations and predictions of detailed microscopic traffic states, even at low CAV MPRs, creating dynamic traffic environment world models to enable fine control and management of the connected and automated traffic systems.

Multi-object tracking involves maintaining several trajectories of different objects moving in the scene throughout the video. With this objective, in this article, a fully automatic and real-time tracking algorithm to track multiple vehicles in a video is proposed. The proposed method specifically tries to address the challenges of occlusion and fast-motion in traffic surveillance. The algorithm begins with automatic detection of the moving targets by an adaptive GMM-based background subtraction method. The trajectories of these detected targets are then built using a three-level multi-motion modeled particle filter framework which allows to deal with the challenges of occlusion and fast-motions of the target. The likelihood model for targets is based on their color distribution and edge oriented histogram features. It is contended that the color distribution feature, which can represent the target appearance and the edge oriented histogram, which can describe the target structure are sufficient to represent it in an unique feature space. Based on the similarity of target likelihood, the locations of the targets are filtered. These filtered locations are then associated with the most likely detections using the proposed low-cost and fast data association algorithm based on Euclidean distance and prevailing motion vector. The performance evaluation of the proposed scheme is carried out based on the six measures: Multi Object Tracking Accuracy, Multi Object Tracking Precision, Mostly Tracked trajectories, Mostly Lost trajectories, Identity Switches and Frames Per Second. The results evaluated on stationary camera shot sequences from benchmark datasets as well as real-time shot videos indicate that the proposed algorithm ensures robust tracking and can be used effectively for real-time surveillance of highways.

Rejuvenation in particle filters is necessary to prevent the collapse of the weights when the number of particles is insufficient to properly sample the high-probability regions of the state space. Rejuvenation is often implemented in a heuristic manner by the addition of random noise that widens the support of the ensemble. This work aims at improving canonical rejuvenation methodology by the introduction of additional prior information obtained from climatological samples; the dynamical particles used for importance sampling are augmented with samples obtained from stochastic covariance shrinkage. A localized variant of the proposed method is developed. Numerical experiments with the Lorenz '63 model show that modified filters significantly improve the analyses for low dynamical ensemble sizes. Furthermore, localization experiments with the Lorenz '96 model show that the proposed methodology is extendable to larger systems.

The design of efficient autonomous navigation systems for mobile robots or autonomous vehicles is fundamental to perform the programmed tasks. Basically, two kind of sensors are used in urban road following: LIDAR and cameras. LIDAR sensors are highly accurate but expensive and extra work is needed for human understanding of the point cloud scenes; however, visual content is understood better by human beings, which should be used to develop human-robot interfaces. In this work, a computer vision-based urban road following software tool called AutoNavi3AT for mobile robots and autonomous vehicles is presented. The urban road following scheme proposed in AutoNavi3AT uses vanishing point estimation and tracking on panoramic images to control the mobile robot heading on the urban road. To do that, Gabor filters, region growing, and particle filters were used. In addition, laser range data are also employed for local obstacle avoidance. Quantitative results were achieved using two kind of tests, one uses datasets acquired at the Universidad del Valle campus, and field tests using a Pioneer 3AT mobile robot. As a result, important improvements in the vanishing point estimation of 68.26 % and 61.46 % in average were achieved, which is useful for mobile robots and autonomous vehicles when they are moving on urban roads. El diseño de sistemas de navegación autónomos eficientes para robots móviles o vehículos autónomos es fundamental para realizar las tareas programadas. Básicamente, se utilizan dos tipos de sensores en el seguimiento de vías urbanas: LIDAR y cámaras. Los sensores LIDAR son muy precisos, pero costosos y se necesita trabajo adicional para la comprensión humana de las escenas de nubes de puntos; sin embargo, los seres humanos entienden mejor el contenido visual, lo que debería usarse para desarrollar interfaces humano-robot. En este trabajo, se presenta una herramienta de software de seguimiento de carreteras urbanas basada en visión artificial llamada AutoNavi3AT para robots móviles y vehículos autónomos. El esquema de seguimiento de vías urbanas propuesto en AutoNavi3AT utiliza la estimación del punto de fuga y el seguimiento de imágenes panorámicas para controlar el avance del robot móvil en la vía urbana. Para ello se utilizaron filtros Gabor, crecimiento de regiones y filtros de partículas. Además, los datos de alcance del láser también se emplean para evitar obstáculos locales. Los resultados cuantitativos se lograron utilizando dos tipos de pruebas, una utiliza conjuntos de datos adquiridos en el campus de la Universidad del Valle y pruebas de campo utilizando un robot móvil Pioneer 3AT. Como resultado, se lograron mejoras importantes en la estimación del punto de fuga de 68.26% y 61.46% en promedio, lo cual es útil para robots móviles y vehículos autónomos cuando se desplazan por vías urbanas.

Estimation models from connected vehicles often assume low level parameters such as arrival rates and market penetration rates as known or estimate them in real-time. At low market penetration rates, such parameter estimators produce large errors making estimated queue lengths inefficient for control or operations applications. In order to improve accuracy of low level parameter estimations, this study investigates the impact of connected vehicles information filtering on queue length estimation models. Filters are used as multilevel real-time estimators. Accuracy is tested against known arrival rate and market penetration rate scenarios using microsimulations. To understand the effectiveness for short-term or for dynamic processes, arrival rates, and market penetration rates are changed every 15 min. The results show that with Kalman and Particle filters, parameter estimators are able to find the true values within 15 min and meet and surpass the accuracy of known parameter scenarios especially for low market penetration rates. In addition, using last known estimated queue lengths when no connected vehicle is present performs better than inputting average estimated values. Moreover, the study shows that both filtering algorithms are suitable for real-time applications that require less than 0.1 second computational time.

Most of siamese network and correlation filter (CF) based trackers usually employ the context regression scheme to achieve appealing performance in both accuracy and efficiency. However, they are prone to drifting in challenging situations exhibiting occlusion, out-of-view and large-scale variations due to the lack of failure correction ability in these regressors. Particle filter based trackers can help to recover from tracking failures since several particles of high confidence about the target can be remained for the probability estimation of next frames, but need the large numbers of particles for each frame. In this paper, we propose a generic dynamic particle filter framework, which can reasonably control the number of particles in different scenarios, to improve the robustness of siamese and CF trackers by jointing the target classifier to relieve drifting. Our fundamental insight is that general scenarios are processed efficiently by the context regressor with few particles, while special scenarios are handled effectively by the target classifier with many particles. We propose a novel measure to determine whether to adopt the regressor and few particles to estimate target states with high measure scores, or increase the number of particles to prevent drifting with the proposed multi-template matching strategy in the classifier. In extensive experiments on eight large-scale benchmarks including OTB, UAV, TC128, VOT2017, VOT2019, LaSOT, TrackingNet and Got-10k, the proposed framework enables many basic siamese and CF trackers to operate at least over 24 frames per second and achieve superior tracking performance than themself, as well as the comparable accuracy with the state-of-the-art trackers. Furthermore, our framework is flexible and still has great potential for improvement and generalization.

Anytime a robot manipulator is controlled via visual feedback, the transformation between the robot and camera frame must be known. However, in the case where cameras can only capture a portion of the robot manipulator in order to better perceive the environment being interacted with, there is greater sensitivity to errors in calibration of the base-to-camera transform. A secondary source of uncertainty during robotic control are inaccuracies in joint angle measurements which can be caused by biases in positioning and complex transmission effects such as backlash and cable stretch. In this work, we bring together these two sets of unknown parameters into a unified problem formulation when the kinematic chain is partially visible in the camera view. We prove that these parameters are non-identifiable implying that explicit estimation of them is infeasible. To overcome this, we derive a smaller set of parameters we call Lumped Error since it lumps together the errors of calibration and joint angle measurements. A particle filter method is presented and tested in simulation and on two real world robots to estimate the Lumped Error and show the efficiency of this parameter reduction., 18 pages, 13 figures, and 3 tables

This article develops a robust global strategy for identifying the linear parameter varying (LPV) errors-in-variables (EIVs) systems subjected to randomly missing observations and outliers. The parameter interpolated LPV autoregressive exogenous model with an uncertain/noisy input is investigated and a nonlinear state-space model is considered for the input generation model (IGM). The parameters estimation of the LPV EIV systems with nonideal observations is realized using the expectation-maximization algorithm which is particular effective for the incomplete data issue. To ensure the robustness in the identification, the Student's t-distribution which is characterized by its adjustable degree of freedom, is used to handle the measurement non-normality. Since the posterior distributions of the latent states in the IGM are also involved in the identification process and they are difficult to calculate directly, the particle filter is introduced to recursively approximate them instead. Finally, the verification examples are given to demonstrate the effectiveness of the developed strategy.

The understanding of nonlinear, high dimensional flows, e.g, atmospheric and ocean flows, is critical to address the impacts of global climate change. Data Assimilation techniques combine physical models and observational data, often in a Bayesian framework, to predict the future state of the model and the uncertainty in this prediction. Inherent in these systems are noise (Gaussian and non-Gaussian), nonlinearity, and high dimensionality that pose challenges to making accurate predictions. To address these issues we investigate the use of both model and data dimension reduction based on techniques including Assimilation in Unstable Subspaces, Proper Orthogonal Decomposition, and Dynamic Mode Decomposition. Algorithms that take advantage of projected physical and data models may be combined with Data Analysis techniques such as Ensemble Kalman Filter and Particle Filter variants. The projected Data Assimilation techniques are developed for the optimal proposal particle filter and applied to the Lorenz'96 and Shallow Water Equations to test the efficacy of our techniques in high dimensional, nonlinear systems., 30 pages, 13 figures, 3 tables To appear in Computers & Mathematics with Applications, 2021,ISSN 0898-1221

The revolution of Industry 4.0 and Internet of Things facilitate large and cheap data acquisition from complex systems. However, the online processing and analytics of such multi-stream data still suffer difficulties. The key reason is that data from different sensors (multi-stream data) contain nonlinear within-and-cross sensor correlations, which poses challenges in the modeling and analysis of such data, especially for the online scenarios. To jointly model multi-stream data, multi-output Gaussian Process (MGP) has become popular and achieved great success. However, existing methods assume different outputs are linearly correlated, which significantly limits their applications. In this paper, we propose a nonlinear online MGP framework to simultaneously realize nonlinear correlation modeling and online processing of multi-stream data. Specifically, a tailored co-variance structure is developed based on the convolution process, which can capture nonlinear self-and-cross correlations. To realize online processing, Bayesian analysis and marginalized particle filter are applied to estimate function values and model parameters. Comparing to existing methods, the proposed method features superiorities in i) the non-linear characterization of self-and-cross correlations, and ii) the efficient modeling and prediction capability. These features make the proposed method especially appropriate for industrial informatics with complex relationship and large-scale data sets. The proposed method is validated with simulation studies and a real-case study of biophysical signals.

We propose a likelihood-based Bayesian method that exploits up-to-date sequential Monte Carlo methods to efficiently estimate long-run risk models in which the conditional variance of consumption growth follows either an autoregressive (AR) process or an autoregressive gamma (ARG) process. We use the U.S. quarterly consumption and asset returns data from the postwar period to implement estimation. Our findings are: (1) informative priors on the preference parameters can help to improve model performance; (2) expected consumption growth has a very persistent component, whereas consumption volatility is less persistent; (3) while the ARG-based model performs better than the AR-based one statistically, the latter could fit asset returns better; and (4) the solution method matters more for estimation in the AR-based model than in the ARG-based model.

Eliminating the erroneous state bias from cyber attack is essential to ensure the real-time control and secure operation of the smart grid, especially when cyber-attack flourishes in recent years. For this reason, this paper, for the first time, proposes a new operating state reconstruction scheme to automatically filter out possible cyber-attacks in smart grid. This scheme consists of an attack separation method, a state forecasting algorithm, and a state recovery approach. Based on P-Q decomposition, the attack separation method takes into account the network parameter perturbations and prediction uncertainties to analytically estimate the regular deviation of each state, thereby identifying potential abnormal states. Then, a particle filtering-based state forecasting algorithm is developed to evaluate the original operating level of the detected contaminated states. Finally, we pro-pose a fast bilinear state recovery approach to mitigate the smearing effect of undetected contaminated states due to cyber attacks. The proposed state reconstruction method can not only detect cyber-attacks, but also realize automatic correction of the erroneous states, thus mitigating the devastating impact of attack on smart grids. The feasibility and effectiveness of our reconstruction scheme are extensively validated on IEEE standard 9-, 14-, 30-, 57- and 118-bus power systems. The obtained results show that the proposed scheme exhibits strong robustness, high stability, and promising performance for automatic attack filtering, indicating a great potential for implementations in deep cyber-penetrated smart grids.

The predictive vessel surveillance is one of the indispensable functional components in intelligent maritime traffic system. Vessel trajectory prediction serves as a prerequisite for collision detection and risk assessment. Perceiving the forthcoming traffic situation in advance helps decide the succeeding actions to mitigate the potential risk. The availability of maritime big data brings great potential to extract vessel movement patterns to support trajectory forecasting. In this paper, a novel vessel trajectory and navigating state prediction methodology is proposed based on AIS data, which synergizes properly designed learning, motion modelling and knowledge base assisted particle filtering processes. The primary contributions of this work also comprise several critical research findings to handle the key challenges in vessel trajectory and navigating state prediction problem, such as the adaptive training window determination for the learning process and effective knowledge storage and searching algorithm intended to reduce the query time of waterway pattern retrieval. The studies for these challenges are still missing in the reported literatures but they are essentially important for improving the prediction accuracy, efficiency and practicality. With the maritime traffic data collected for Singapore water, a thorough evaluation of the prediction performance has been conducted for different navigating scenarios. It is also observed that better prediction outperforms on account of allowing earlier alert in risk detection.

New clustering methods are proposed to develop novel particle filters with Gaussian mixture models (PFGMM). In the PFGMM, the propagated samples are clustered to recover a Gaussian mixture model (GMM) using a clustering algorithm, which plays a fundamental role in the filter's performance. Two clustering methods are introduced that simultaneously minimize the covariance of each of the GMM components and maximize the likelihood function. Under the scenarios considered in this paper, it is shown through numerical simulation that the PFGMMs with the proposed clustering algorithms lead to better performance than the PFGMM employing the K-means or the expectation-maximization (EM) algorithms as well as the regularized particle filter (RPF) and the Gaussian sum particle filter (GSPF).

This paper proposes a new diagnostic and prognostic approach that integrates joint time-frequency reflectometry (JTFDR), continuous wavelet transform (CWT), and particle filter to estimate the degradation of insulation and predict the remaining useful life of coaxial cables. In the proposed approach, the degradation process of the insulation is monitored by using an optimized JTFDR incident signal in an accelerated thermal aging test. CWT is adopted to obtain the time-frequency scalogram of the reflected JTFDR signals. The features are then extracted from the CWT magnitude scalogram to represent the degradation level of the insulation. A particle filter-based fault diagnostic and prognostic approach is developed. A series of experiments are conducted, and the results demonstrate the effectiveness of the proposed method.

The introduction of data-driven inertial navigation provides new opportunities that the pedestrian dead reckoning could not well provide for constraining inertial system error drift on smartphones, and has been considered as another promising approach to meet the requirement of location-based services. However, indoor localization systems based on a single technology still have their limitations, such as the drift of inertial navigation and the received signal strength fluctuation of Bluetooth, making them unable to provide reliable positioning. To exploit the complementary strengths of each technology, this paper proposes a feasible fusion framework by utilizing a particle filter to integrate data-driven inertial navigation with localization based on Bluetooth Low Energy (BLE). For data-driven inertial navigation, under the premise of using the deep neural network with great potential in model-free generalization to regress pedestrian motion characteristics, we effectively combined the method of using gravity to stabilize inertial measurement units data to make the network more robust. Experimental results show that in the test of different smartphone usages, the proposed data-driven inertial navigation and BLE-based localization technology have good results in modeling user’s movement and positioning respectively. And due to this, the proposed fusion algorithm has almost unaffected by the usages of smartphones. Compared with BLE-based localization that achieved a good mean positional error (MPE) of 1.76m, for the four usages of texting, swinging, calling and pocket, the proposed fusion algorithm reduced the MPE by 32.35%, 20.51%, 20.74%, and 45.37%, respectively, and can further improve localization accuracy on the basis of existing fusion method.

The deployment of an Indoor Position System (IPS) in the real-world raised many challenges, such as installation of infrastructure, the calibration process or modelling of the building’s floor plan. For Wi-Fi-based IPSs, deployments often require a laborious and time-consuming site survey to build a Radio Map (RM), which tends to become outdated over time due to several factors. In this paper, we evaluate different deployment methods of a Wi-Fi-based IPS in an industrial environment. The proposed solution works in scenarios with different space restrictions and automatically builds a RM using industrial vehicles in operation. Localization and tracking of industrial vehicles, equipped with low-cost sensors, is achieved with a particle filter, which combines Wi-Fi measurements with heading and displacement data. This allows to automatically annotate and add new samples to a RM, named vehicle Radio Map (vRM), without human intervention. In industrial environments, vRMs can be used with Wi-Fi fingerprinting to locate human operators, industrial vehicles, or other assets, allowing to improve logistics, monitoring of operations, and safety of operators. Experiments in an industrial building show that the proposed solution is capable of automatically building a high-quality vRM in different scenarios, i.e., considering a complete floor plan, a partial floor plan or without a floor plan. Obtained results revealed that vRMs can be used in Wi-Fi fingerprinting with better accuracy than a traditional RM. Sub-meter accuracies were obtained for an industrial vehicle prototype after deployment in a real building.

Location-based services have become more and more important with the development of the Internet of Things (IoT) technology in recent years. Global Navigation Satellite System (GNSS) is widely used for positioning outdoors while it is still challenging to realize accurate and universal 3D indoor localization in complex indoor environments. Crowdsourcing-based positioning method is proposed aiming at autonomously constructing the navigation database based on the pedestrians’ daily-life data. This paper proposes an autonomous 3D indoor localization algorithm using the combination of crowdsourced Wi-Fi fingerprinting and Micro-Electro-Mechanical System sensors (3D-CSWS). An enhanced complementary filter is applied to provide accurate attitude information by integrating multi-sensor data with the detection of external acceleration and quasi-static magnetic field. In addition, the gradient descent (GD) algorithm is proposed to optimize the forward pedestrian dead reckoning and the optimized trajectories are weighted fused to construct the final navigation database after quality evaluation. In the on-line phase, the adaptive particle filter is used to integrate the results of Wi-Fi fingerprinting and multiple sensors to provide accurate and concrete 3D indoor localization performance. The real-world experimental results demonstrate that the proposed 3D-CSWS is proved to achieve autonomous and precise 3D indoor localization performance among complex indoor environments.

Since vision-based methods are vulnerable to challenging environmental conditions such as bad whether, shadows, occlusions and illumination, robustness is important. In this paper, a robust algorithm for detecting and tracking multiple lanes with arbitrary shape is proposed. We extend previous lane detection and tracking processes from the spatial domain to the temporal-spatial domain using our more robust and general multiple lane model. Distinct from traditional image pre-processing procedures, we adopt slice images generated from image sequence decomposition and reconstruction that contains important structured historical information. In this domain, we propose assumptions to reduce the algorithm's complexity and computation further. First, instead of common binarization, a more general lane marker detector is proposed with weak constraints to obtain cleaner binary image. Then, the lane marker candidates are used as the initialization of following particle filtering tracking process. We propose a confidence map computed from binary slice images by using an improved distance transform for particles sampling and weights calculation. The multi-lane model supports the use of multiple independent particle filters for tracking each lane boundary separately. Finally, a range of experimental results on testing dataset indicate that the proposed algorithm is effective with high performance and comparing with other methods, it has better tolerance to challenging environments.

Real-time high-precision navigation has many applications, such as pedestrian navigation, emergency rescue, and vehicle networks. In practice, the measurement models are often non-linear, and sequential Bayesian filters, such as Kalman and particle filter, suffer from accumulative errors, which cannot provide long-time high-precision services for localization. To solve arbitrary noise distribution, this paper proposes a Gaussian condensation filter algorithm to achieve high-precision localization in a non-Gaussian noise environment. To this end, we proposed an error-ellipse re-sampling-based Gaussian condensation filter (EER-GCF), which establishes error-ellipses with different confidence probabilities and implements a re-sampling algorithm based on the sampling points’ geometrical positions. Furthermore, a cooperative constrained Gaussian condensation filter (CEER-GCF) is proposed to enhance information fusion in the multi-target network. This study accomplishes cooperative tracking based on spatial-temporal constraints to enhance error correction. Experiment results show that CEER-GCF can effectively eliminate the accumulative error and optimize state estimation, which outperforms state-of-the-arts, such as unscented Kalman filter and particle filter.

Both siamese network and correlation filter (CF)-based trackers have exhibited superior performance by formulating tracking as a similarity measure problem, where a similarity map is learned by the correlation between a target template and a region of interest (ROI) with a cosine window. Nevertheless, this window function is usually fixed for various targets and not changed, undergoing significant noise variations during tracking, which easily makes model drift. In this article, we focus on the study of a noise-aware (NA) framework for robust visual tracking. To this end, the impact of various window functions is first investigated in visual tracking. We identify that the low signal-to-noise ratio (SNR) of windowed ROIs makes the above trackers degenerate. At the prediction phase, a novel NA window customized for visual tracking is introduced to improve the SNR of windowed ROIs by adaptively suppressing the variable noise according to the observation of similarity maps. In addition, to further optimize the SNR of windowed pyramid ROIs for scale estimation, we propose to use the particle filter to dynamically sample several windowed ROIs with more favorable signals in temporal domains instead of this pyramid ROIs extracted in spatial domains. Extensive experiments on the popular OTB-2013, OTB-50, OTB-2015, VOT2017, TC128, UAV123, UAV123@10fps, UAV20L, and LaSOT datasets show that our NA framework can be extended to many siamese and CF trackers and our variants obtain superior performance than baseline trackers with a modest impact on efficiency.

In this paper, we propose a simultaneous localization and mapping (SLAM) framework that combines the methods of the unscented article filter (UPF) and particle smothers (PS). The UPF is used to estimate the position and orientation of the mobile robot and particle smothers are used to update the locations of landmarks. We show that UPF is capable of estimating the pose of the robot more consistently and accurately than generic particle filter and unscented Kalman filter (UKF) do, especially when the dynamic models of the robot are highly nonlinear or noises are non-Gaussian. Then, the proposed SLAM framework is applied to high frequency (HF) band radio-frequency identification (RFID) system. Since detection model of the HF band RFID system is highly non-Gaussian, the traditional SLAM frameworks with the Gaussian assumption are not suitable and the proposed UPF-PS framework presents the superior performance. The proposed SLAM framework is evaluated through simulated experiments with a HF band RFID system and a range and bearing sensor. The comparisons between the proposed SLAM framework and four other SLAM frameworks, i.e., FastSLAM, UFastSLAM, and P-SLAM 1.0 and 2.0 are also given. Finally, the proposed SLAM framework is verified in a real experimental environment. The experimental results validate the effectiveness and superiority of the proposed SLAM framework.

This paper presents an improved approach based on the equivalent-weights particle filter (EWPF) that uses the proposal density to effectively improve the traditional particle filter. The proposed approach uses historical data to calculate statistical observations instead of the future observations used in the EWPF’s proposal density and draws on the localization scheme used in the localized PF (LPF) to construct the localized EWPF. The new approach is called the statistical observation localized EWPF (LEWPF-Sobs); it uses statistical observations that are better adapted to the requirements of real-time assimilation and the localization function is used to calculate weights to reduce the effect of missing observations on the weights. This approach not only retains the advantages of the EWPF, but also improves the assimilation quality when using sparse observations. Numerical experiments performed with the Lorenz 96 model show that the statistical observation EWPF is better than the EWPF and EAKF when the model uses standard distribution observations. Comparisons of the statistical observation localized EWPF and LPF reveal the advantages of the new method, with fewer particles giving better results. In particular, the new improved filter performs better than the traditional algorithms when the observation network contains densely spaced measurements associated with model state nonlinearities.

Markov switching (MS-)GARCH(1,1) models allow for structural changes in volatility dynamics between a finite number of regimes. Since the regimes are not observed, computation of the likelihood requires integrating over an exponentially increasing number of regime paths, which is intractable. An existing smooth likelihood estimation procedure for sequential Monte Carlo (SMC), that is currently limited to hidden Markov models with a one-dimensional state variable, is modified to enable likelihood estimation and maximisation for MS-GARCH(1,1) models, a model which requires two dimensions, volatility and regime, to evolve its hidden state process. Furthermore, the modified SMC procedure is shown to be easily adapted to fitting MS-GARCH(1,1) models even when there are missing observations. The proposed methodology is validated with simulated data and is also illustrated with analysis of two financial time series, the daily returns on the S&P 500 index and on the Henry Hub natural gas spot price, with the latter series containing a gap caused by shutdown in response to hurricane Rita in 2005.

Particle filter is commonly used in various indoor positioning schemes, but sample impoverishment and weight degradation generally exist in particle filter. To solve this problem, this paper uses firefly algorithm to optimize particle filter. By using the global optimal value in the particle swarm to guide the remaining particles to update their positions, the particles tend to move to the high-likelihood area, which can more accurately describe the true state of the target observation. At the same time, in order to avoid particles falling into local optimum and oscillating repeatedly at the extreme point, the mathematical model of firefly algorithm is reconstructed in this paper, which is named as adaptive optimization firefly algorithm (AOFA). The experimental results show that the positioning scheme can provide the positioning accuracy with an average error of less than 0.5m. Compared with the traditional particle filter, the positioning accuracy is improved by 120%.

Both routine radiation surveys and incident response--as currently performed by human workers--are time-consuming and involve significant, potentially unanticipated radiation dose (especially for accident response where levels prohibit human presence entirely). Previous efforts addressed routine surveys using robotic systems, but they must also localize and characterize discrete sources when anomalies occur. To be effective, characterization must account for real-world complications, including multiple sources, environmental attenuation, spatial localization, and isotopic identification. Recursive Bayesian estimation (RBE) using grid-based estimators and particle filters has been previously investigated, but we eliminate previous open-space assumptions by including attenuation models and sensor position height in the sequential sampling strategy. The method also incorporates autonomous isotopic identification via gamma spectroscopy, which supports the attenuation modeling and improves the computational efficiency in multisource cases. In addition, survey points are autonomously optimized using the Fisher information. A radioactive decay model is implemented to generalize the method by addressing short-lived nuclides. The developed hardware is evaluated in multiple scenarios where no operator intervention is required, effectively eliminating operator dose uptake from localization/characterization in real-world scenarios. Two different mobile robots are used, demonstrating the portability of the data collection and analysis software. Also, the generalized complexity is documented to support planning in future scenarios.

Nonlinear, non-Gaussian state space models have found wide applications in many areas. These models usually do not allow for an analytical representation of their likelihood function and thus, sequential Monte Carlo or particle filter methods are mostly applied to estimate their parameters. Finding the best-fitting parameters of a model is a non-trivial task since stochastic approximations lead to non-smooth likelihood functions. Recently proposed iterative filtering algorithms developed for this purpose are compared with simpler on-line filters and more traditional methods of inference. A highly nonlinear class of Markov-switching models, the so called Markov-switching multifractal model (MSM) is used as illustrative example in the comparison of different optimisation routines. Besides the well-established univariate discrete-time MSM, univariate and multivariate continuous-time versions of MSM are considered. Monte Carlo simulation experiments indicate that across a variety of MSM specifications, the classical Nelder-Mead or simplex algorithm appears still as more efficient and robust compared to a number of online and iterated filters. A very close competitor is one of the recently proposed iterated filters while other alternatives are mostly dominated by these two algorithms. An empirical application of both discrete and continuous-time MSM to seven financial time series shows that both models dominate GARCH and FIGARCH models in terms of in-sample goodness-of-fit. Out-of-sample forecast comparisons show in the majority of cases a clear dominance of the continuous-time MSM under a mean absolute error criterion, and less conclusive results under a mean squared error criterion.

Audio-visual tracking of an unknown number of concurrent speakers in 3D is a challenging task, especially when sound and video are collected with a compact sensing platform. In this paper, we propose a tracker that builds on generative and discriminative audio-visual likelihood models formulated in a particle filtering framework. We localize multiple concurrent speakers with a de-emphasized acoustic map assisted by the image detection-derived 3D video observations. The 3D multimodal observations are either assigned to existing tracks for discriminative likelihood computation or used to initialize new tracks. The generative likelihoods rely on color distribution of the target and the de-emphasized acoustic map value. Experiments on AV16.3 and CAV3D datasets show that the proposed tracker outperforms the uni-modal trackers and the state-of-the-art approaches both in 3D and on the image plane.

This article describes the development and testing of a passive sonar, multitarget tracker, and adaptive behavior that enable an autonomous underwater vehicle (AUV) to detect and actively track nearby surface vessels. A planar hull-mounted hydrophone array, originally designed for active sonar, is repurposed for passive sonar use and provides acoustic data to a time-delay-and-sum beamformer that generates multiple angle-only contacts. A particle filter tracker assimilates these contacts with a single-hypothesis data association strategy to estimate the position and velocity of targets. Summary statistics of each track are periodically reported to an onboard database along with qualitative labels. To improve tracking performance, detections trigger an adaptive behavior that maneuvers the AUV to maintain multiple targets in the field of view by minimizing the worst case aspect angle deviation from broadside (across all targets). The tracking system is demonstrated through at-sea experiments in which a Bluefin-21 AUV adaptively tracks multiple surface vessels, including another autonomous platform, in the approaches to Boston Harbor.

Tracking a mobile object is one of the important topics in pattern recognition, but style has some obstacles. A Reliable tracking system must adjust their tracking windows in real time according to appearance changes of the tracked object. Furthermore, it has to deal with many challenges when one or multiple objects need to be tracked, for instance when the target is partially or fully occluded, background clutter, or even some target region is blurred. In this paper, we will present a novel approach for a single object tracking that combines particle filter algorithm and kernel distribution that update its tracking window according to object scale changes, whose name is multi-scale adaptive particle filter tracker. We will demonstrate that the use of particle filter combined with kernel distribution inside the resampling process will provide more accurate object localization within a research area. Furthermore, its average error for target localization was significantly lower than 21.37 pixels as the mean value. We have conducted several experiments on real video sequences and compared acquired results to other existing state of the art trackers to demonstrate the effectiveness of the multi-scale adaptive particle filter tracker.

Paleoclimate data assimilation (DA) is a novel tool for reconstructing past climates that directly integrates proxy records with climate model output. Despite the potential for DA to expand the scope of quantitative paleoclimatology, these methods remain difficult to implement in practice due to the multi-faceted requirements and data handling necessary for DA reconstructions, the diversity of DA methods, and the need for computationally efficient algorithms. Here, we present DASH, a MATLAB toolbox designed to facilitate paleoclimate DA analyses. DASH provides command line and scripting tools that implement common tasks in DA workflows. The toolbox is highly modular and is not built around any specific analysis, and thus DASH supports paleoclimate DA for a wide variety of time periods, spatial regions, proxy networks, and algorithms. DASH includes tools for integrating and cataloguing data stored in disparate formats, building state vector ensembles, and running proxy (system) forward models. The toolbox also provides optimized algorithms for implementing ensemble Kalman filters, particle filters, and optimal sensor analyses with variable and modular parameters. This paper reviews the key components of the DASH toolbox and presents examples illustrating DASH's use for paleoclimate DA applications.

In recent decades, due to the population growth and low precipitation, the overexploitation of ground water resources has become an important issue. To ensure a sustainable scheme for these resources, understanding the behavior of the aquifers is a key step. This study takes a numerical modeling approach to investigate the behavior of an unconfined aquifer in an arid area located in the east of Iran. A novel hybrid model is proposed that couples the numerical modeling to a data assimilation model to remove the uncertainty in the hydrodynamic parameters of the aquifer including the hydraulic conductivity coefficients and specific yields. The uncertainty that exists in these parameters results in unreliability of the head values acquired from the models. Meshless local Petrov-Galerkin (MLPG) is used as the numerical model, and particle filter (PF) is our data assimilation model. These models are implemented in the MATLAB software. We have calibrated and validated our PF-MLPG model by the observation head data from the piezometers. The RMSE in head values for our model and other commonly used numerical models in the literature including the finite difference method and MPLG are calculated as 0.166, 1.197 and 0.757 m, respectively. This fact shows the necessity of using this method in each aquifer. HIGHLIGHTS The PF-MLPG method is compatible with different kinds of aquifers and different conditions, including confined, unconfined, transient, steady, irregular geometry and regular geometry.; PF-MLPG simulates groundwater flow with satisfactory accuracy that helps decision makers to manage the aquifers properly.; This new calibration–simulation model removes high uncertainty of model and input parameters such as hydraulic conductivity coefficient and specific yield.

This paper proposes a new method for the tracking of three-dimensional (3D) hand palms from the whole human standing body using fuzzy rule-based fusion of explainable fuzzy features from a monocular video. The characteristics of this method include visually and linguistically explainable fuzzy features and rules and computational efficiency. This paper first tracks the two-dimensional (2D) palms using the following four fuzzy features: optical flows, the degree of a pixel in the foreground, skin color information, and the search area around a hand palm candidate from a segmented body. Afterward, a fuzzy system is proposed to fuse the four fuzzy features to estimate the 2D palm positions. Localization of the elbows is based on the estimated palm locations, human body skeletons, and body contour. The 2D palms and elbows are tracked using a modified particle filter. To estimate the depth of each palm, the locations of the palm and elbow are fed as inputs to a neural fuzzy system. The 3D palm tracking result is applied to a robot upper-body imitation system. Experiments with comparisons of different hand palm tracking methods are performed to verify the real-time computational ability and accuracy of the proposed method.

Intelligent transport systems demand the provision of a continuous high-accuracy positioning service. However, a vehicle positioning system typically has to operate in dense urban areas where conventional satellite-based positioning systems suffer severe performance degradation. 5G technology presents a new paradigm to provide ubiquitous connectivity, where the vehicle-to-everything (V2X) communication turns out to be highly conducive to enable both accurate positioning and the emerging Internet of Vehicles (IoV). Due to the high probability of Line-of-Sight (LoS) communication, as well as the diversity and number of reference stations, the application of ultradense networks (UDN) in the vehicle-to-infrastructure (V2I) subsystem is envisaged to complement the existing positioning technologies. Moreover, the cooperative determination of location information could be enhanced by the vehicle-to-vehicle (V2V) subsystem. In this article, we propose a V2X-integrated positioning methodology in UDN, in which the V2I, V2V, and inertial navigation systems (INSs) are unified for data fusion. This formulation is an iterative high-dimensional estimation problem, and an efficient multiple particle filter (MPF)-based method is proposed for solving it. In order to mitigate the non-LoS (NLoS) impact and provide a relatively accurate input to the MPF, we introduce an advanced anchor selection method using the geometry-based ${K}$ -means clustering (GK) algorithm based on the characteristics of network densification. Numerical results demonstrate that utilizing the GK algorithm in the proposed integrated positioning system could achieve 18.7% performance gains in accuracy, as compared with a state-of-the-art approach.

Detecting and tracking moving vehicles is one of the most fundamental functions of autonomous vehicles driving in complex scenarios, as it forms the foundation of decision making and path planning. In order to estimate the pose information of moving vehicles accurately, 3D-LiDAR is widely used for accurate distance data. This paper proposed a real-time tracking algorithm based on L-Shape fitting. The algorithm detects the corners of moving vehicles and uses RANSAC to take a limited amount of noisy data. In addition, a vehicle tracking system with multi-weight Rao-Blackwellized Particle Filtering (RBPF) is built upon the orientation estimation given by L-Shape fitting. The proposed algorithm is validated on the KITTI dataset and a manually labeled dataset acquired from an autonomous vehicle at Carnegie Mellon University. Furthermore, the proposed solution is implemented in an autonomous vehicle at Tongji University. The experiments illustrate that the proposed algorithm achieves real-time performance, mitigates the effect of noisy data and improves estimation accuracy.

For nonlinear discrete-time systems where measurements can be randomly delayed by multiple sampling periods, measurements are dependent conditioned on the state trajectory, and the dependence becomes more complicated with the increase of step of random delay. A particle filtering for this system is developed, which is novel in that the likelihood is computed allowing multi step of delay and dependence of measurements. Multi step of delay is dealt with through utilizing the formula of total probability skillfully, and dependence is dealt with through estimating the filtering probability distribution of random delay. The novel particle filtering is applied to two examples to validate its effectiveness and superiority.

This study proposes a novel approach for a vision-based navigation problem using semantically segmented aerial images generated by a convolutional neural network. Vision-based navigation provides a...

For safety-critical industrial systems, standby redundancy is widely applied to guarantee safety requirements. With the advancement of sensing technology, real-time system reliability evaluation can be executed to prevent failures, for which the load-sharing characteristic has to be considered because multiple components jointly accomplish the mission. In this article, a novel methodology is proposed to online assess the reliability and optimize the mode transfer scheduling of the degrading standby system considering the load-sharing characteristic. First, the stepwise drifted Wiener process is invoked to characterize the component degradation process. Then, to deal with the process and measurement noise, the monitored degradation data are processed online to update the component degradation state and rate, based on the particle filter. Besides, a Monte Carlo method involving the closed Newton–Cotes quadrature rule is employed to quantify the real-time system reliability. Finally, the mode transfer scheduling of standby components is optimized online to maximize the system reliability at mission time. A numerical example and a case study referring to the aircraft hydraulic system are presented to demonstrate the effectiveness of the developed methodology.

Classification and clustering methods based on univariate functions have been well developed. Recent work has extended the techniques to the domain of bivariate functions by incorporating the techn...

Nonlinear differential equations (DEs) are used in a wide range of scientific problems to model complex dynamic systems. The differential equations often contain unknown parameters that are of scientific interest, which have to be estimated from noisy measurements of the dynamic system. Generally, there is no closed-form solution for nonlinear DEs, and the likelihood surface for the parameter of interest is multi-modal and very sensitive to different parameter values. We propose a Bayesian framework for nonlinear DE systems. A flexible nonparametric function is used to represent the dynamic process such that expensive numerical solvers can be avoided. A sequential Monte Carlo algorithm in the annealing framework is proposed to conduct Bayesian inference for parameters in DEs. In our numerical experiments, we use examples of ordinary differential equations and delay differential equations to demonstrate the effectiveness of the proposed algorithm. We developed an R package that is available at https://github.com/shijiaw/smcDE. Supplementary files for this article are available online.

A standard way to move particles in a SMC sampler is to apply several steps of a MCMC (Markov chain Monte Carlo) kernel. Unfortunately, it is not clear how many steps need to be performed for optimal performance. In addition, the output of the intermediate steps are discarded and thus wasted somehow. We propose a new, waste-free SMC algorithm which uses the outputs of all these intermediate MCMC steps as particles. We establish that its output is consistent and asymptotically normal. We use the expression of the asymptotic variance to develop various insights on how to implement the algorithm in practice. We develop in particular a method to estimate, from a single run of the algorithm, the asymptotic variance of any particle estimate. We show empirically, through a range of numerical examples, that waste-free SMC tends to outperform standard SMC samplers, and especially so in situations where the mixing of the considered MCMC kernels decreases across iterations (as in tempering or rare event problems)., revised version