59 research outputs found
Outlier-Detection Based Robust Information Fusion for Networked Systems
We consider state estimation for networked systems where measurements from
sensor nodes are contaminated by outliers. A new hierarchical measurement model
is formulated for outlier detection by integrating the outlier-free measurement
model with a binary indicator variable. The binary indicator variable, which is
assigned a beta-Bernoulli prior, is utilized to characterize if the sensor's
measurement is nominal or an outlier. Based on the proposed outlier-detection
measurement model, both centralized and decentralized information fusion
filters are developed. Specifically, in the centralized approach, all
measurements are sent to a fusion center where the state and outlier indicators
are jointly estimated by employing the mean-field variational Bayesian
inference in an iterative manner. In the decentralized approach, however, every
node shares its information, including the prior and likelihood, only with its
neighbors based on a hybrid consensus strategy. Then each node independently
performs the estimation task based on its own and shared information. In
addition, an approximation distributed solution is proposed to reduce the local
computational complexity and communication overhead. Simulation results reveal
that the proposed algorithms are effective in dealing with outliers compared
with several recent robust solutions
Practice and Innovations in Sustainable Transport
The book continues with an experimental analysis conducted to obtain accurate and complete information about electric vehicles in different traffic situations and road conditions. For the experimental analysis in this study, three different electric vehicles from the Edinburgh College leasing program were equipped and tracked to obtain over 50 GPS and energy consumption data for short distance journeys in the Edinburgh area and long-range tests between Edinburgh and Bristol. In the following section, an adaptive and robust square root cubature Kalman filter based on variational Bayesian approximation and Huber’s M-estimation is proposed to accurately estimate state of charge (SOC), which is vital for safe operation and efficient management of lithium-ion batteries. A coupled-inductor DC-DC converter with a high voltage gain is proposed in the following section to match the voltage of a fuel cell stack to a DC link bus. Finally, the book presents a review of the different approaches that have been proposed by various authors to mitigate the impact of electric buses and electric taxis on the future smart grid
Bayesian Filtering for Dynamic Systems with Applications to Tracking
This M.Sc. thesis intends to evaluate various algorithms based on Bayesian statistical
theory and validates with both synthetic data as well as experimental
data. The focus is given in comparing the performance of new kind of sequential
Monte Carlo filter, called cost reference particle filter, with other Kalman based
filters as well as the standard particle filter.
Different filtering algorithms based on Kalman filters and those based on sequential
Monte Carlo technique are implemented in Matlab. For all linear Gaussian
system models, Kalman filter gives the optimal solution. Hence only the
cases which do not have linear-Gaussian probabilistic model are analyzed in this
thesis. The results of various simulations show that, for those non-linear system
models whose probability model can fairly be assumed Gaussian, either Kalman
like filters or the sequential Monte Carlo based particle filters can be used. The
choice among these filters depends upon various factors such as degree of nonlinearity,
order of system state, required accuracy, etc. There is always a tradeoff
between the required accuracy and the computational cost. It is found that whenever
the probabilistic model of the system cannot be approximated as Gaussian,
which is the case in many real world applications like Econometrics, Genetics,
etc., the above discussed statistical reference filters degrade in performance.
To tackle with this problem, the recently proposed cost reference particle filter
is implemented and tested in scenarios where the system model is not Gaussian.
The new filter shows good robustness in such scenarios as it does not make any
assumption of probabilistic model.
The thesis work also includes implementation of the above discussed prediction
algorithms into a real world application, where location of a moving robot
is tracked using measurements from wireless sensor networks. The flexibility of
the cost reference particle filter to adapt to specific applications is explored and
is found to perform better than the other filters in tracking of the robot.
The results obtained from various experiments show that cost reference particle
filter is the best choice whenever there is high uncertainty of the probabilistic
model and when these models are not Gaussian. It can also be concluded that,contrary to the general perception, the estimation techniques based on ad-hoc
references can actually be more efficient than those based on the usual statistical
reference
Approximate Gaussian conjugacy: parametric recursive filtering under nonlinearity, multimodality, uncertainty, and constraint, and beyond
Since the landmark work of R. E. Kalman in the 1960s, considerable efforts have been devoted to time series state space models for a large variety of dynamic estimation problems. In particular, parametric filters that seek analytical estimates based on a closed-form Markov–Bayes recursion, e.g., recursion from a Gaussian or Gaussian mixture (GM) prior to a Gaussian/GM posterior (termed ‘Gaussian conjugacy’ in this paper), form the backbone for a general time series filter design. Due to challenges arising from nonlinearity, multimodality (including target maneuver), intractable uncertainties (such as unknown inputs and/or non-Gaussian noises) and constraints (including circular quantities), etc., new theories, algorithms, and technologies have been developed continuously to maintain such a conjugacy, or to approximate it as close as possible. They had contributed in large part to the prospective developments of time series parametric filters in the last six decades. In this paper, we review the state of the art in distinctive categories and highlight some insights that may otherwise be easily overlooked. In particular, specific attention is paid to nonlinear systems with an informative observation, multimodal systems including Gaussian mixture posterior and maneuvers, and intractable unknown inputs and constraints, to fill some gaps in existing reviews and surveys. In addition, we provide some new thoughts on alternatives to the first-order Markov transition model and on filter evaluation with regard to computing complexity
Distributed fusion filter over lossy wireless sensor networks with the presence of non-Gaussian noise
The information transmission between nodes in a wireless sensor networks
(WSNs) often causes packet loss due to denial-of-service (DoS) attack, energy
limitations, and environmental factors, and the information that is
successfully transmitted can also be contaminated by non-Gaussian noise. The
presence of these two factors poses a challenge for distributed state
estimation (DSE) over WSNs. In this paper, a generalized packet drop model is
proposed to describe the packet loss phenomenon caused by DoS attacks and other
factors. Moreover, a modified maximum correntropy Kalman filter is given, and
it is extended to distributed form (DM-MCKF). In addition, a distributed
modified maximum correntropy Kalman filter incorporating the generalized data
packet drop (DM-MCKF-DPD) algorithm is provided to implement DSE with the
presence of both non-Gaussian noise pollution and packet drop. A sufficient
condition to ensure the convergence of the fixed-point iterative process of the
DM-MCKF-DPD algorithm is presented and the computational complexity of the
DM-MCKF-DPD algorithm is analyzed. Finally, the effectiveness and feasibility
of the proposed algorithms are verified by simulations
A flexible robust student’s t-based multimodel approach with maximum Versoria criterion
The performance of the state estimation for Gaussian state space models can be degraded if the models are affected by the non-Gaussian process and measurement noises with uncertain degree of non-Gaussianity. In this paper, we propose a flexible robust Student's t multi-model approach. More specifically, the degrees of freedom parameter from the Student's t distribution is assumed unknown and modelled by a Markov chain of state values. In order to capture more information of the Student's t distributions propagated through multiple models, we establish a model-based Versoria cost function in the form of a weighted mixture rather than the original form, and maximize the function to interact and fuse the multiple models. Simulated results prove the flexibility of the robustness of the proposed Student's t multi-model approach when the existence probability of the outliers is uncertain
Robust Variational-based Kalman Filter for Outlier Rejection with Correlated Measurements
State estimation is a fundamental task in many engineering fields, and therefore robust nonlinear filtering techniques able to cope with misspecified, uncertain and/or corrupted models must be designed for real-life applicability. In this contribution we explore nonlinear Gaussian filtering problems where measurements may be corrupted by outliers,and propose a new robust variational-based filtering methodology able to detect and mitigate their impact. This method generalizes previous contributions to the case of multiple outlier indicators for both independent and dependent observation models. An illustrative example is provided to support the discussion and show the performance improvement
CKF-Based Visual Inertial Odometry for Long-Term Trajectory Operations
The estimation error accumulation in the conventional visual inertial odometry (VIO) generally forbids accurate long-term operations. Some advanced techniques such as global pose graph optimization and loop closure demand relatively high computation and processing time to execute the optimization procedure for the entire trajectory and may not be feasible to be implemented in a low-cost robotic platform. In an attempt to allow the VIO to operate for a longer duration without either using or generating a map, this paper develops iterated cubature Kalman filter for VIO application that performs multiple corrections on a single measurement to optimize the current filter state and covariance during the measurement update. The optimization process is terminated using the maximum likelihood estimate based criteria. For comparison, this paper also develops a second solution to integrate VIO estimation with ranging measurements. The wireless communications between the vehicle and multiple beacons produce the ranging measurements and help to bound the accumulative errors. Experiments utilize publicly available dataset for validation, and a rigorous comparison between the two solutions is presented to determine the application scenario of each solution
Computationally-efficient visual inertial odometry for autonomous vehicle
This thesis presents the design, implementation, and validation of a novel nonlinearfiltering
based Visual Inertial Odometry (VIO) framework for robotic navigation in GPSdenied
environments. The system attempts to track the vehicle’s ego-motion at each time
instant while capturing the benefits of both the camera information and the Inertial Measurement
Unit (IMU). VIO demands considerable computational resources and processing
time, and this makes the hardware implementation quite challenging for micro- and nanorobotic
systems. In many cases, the VIO process selects a small subset of tracked features
to reduce the computational cost. VIO estimation also suffers from the inevitable accumulation
of error. This limitation makes the estimation gradually diverge and even fail to
track the vehicle trajectory over long-term operation. Deploying optimization for the entire
trajectory helps to minimize the accumulative errors, but increases the computational cost
significantly. The VIO hardware implementation can utilize a more powerful processor
and specialized hardware computing platforms, such as Field Programmable Gate Arrays,
Graphics Processing Units and Application-Specific Integrated Circuits, to accelerate the
execution. However, the computation still needs to perform identical computational steps
with similar complexity. Processing data at a higher frequency increases energy consumption
significantly. The development of advanced hardware systems is also expensive and
time-consuming. Consequently, the approach of developing an efficient algorithm will be
beneficial with or without hardware acceleration. The research described in this thesis
proposes multiple solutions to accelerate the visual inertial odometry computation while
maintaining a comparative estimation accuracy over long-term operation among state-ofthe-
art algorithms.
This research has resulted in three significant contributions. First, this research involved
the design and validation of a novel nonlinear filtering sensor-fusion algorithm using trifocal
tensor geometry and a cubature Kalman filter. The combination has handled the system
nonlinearity effectively, while reducing the computational cost and system complexity significantly.
Second, this research develops two solutions to address the error accumulation
issue. For standalone self-localization projects, the first solution applies a local optimization
procedure for the measurement update, which performs multiple corrections on a single
measurement to optimize the latest filter state and covariance. For larger navigation
projects, the second solution integrates VIO with additional pseudo-ranging measurements
between the vehicle and multiple beacons in order to bound the accumulative errors. Third,
this research develops a novel parallel-processing VIO algorithm to speed up the execution
using a multi-core CPU. This allows the distribution of the filtering computation on each
core to process and optimize each feature measurement update independently.
The performance of the proposed visual inertial odometry framework is evaluated using
publicly-available self-localization datasets, for comparison with some other open-source
algorithms. The results illustrate that a proposed VIO framework is able to improve the
VIO’s computational efficiency without the installation of specialized hardware computing
platforms and advanced software libraries
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