905 research outputs found
An evolutionary approach to optimising neural network predictors for passive sonar target tracking
Object tracking is important in autonomous robotics, military applications, financial
time-series forecasting, and mobile systems. In order to correctly track through clutter,
algorithms which predict the next value in a time series are essential.
The competence of standard machine learning techniques to create bearing prediction
estimates was examined. The results show that the classification based algorithms
produce more accurate estimates than the state-of-the-art statistical models. Artificial
Neural Networks (ANNs) and K-Nearest Neighbour were used, demonstrating that this
technique is not specific to a single classifier. [Continues.
Extended Object Tracking: Introduction, Overview and Applications
This article provides an elaborate overview of current research in extended
object tracking. We provide a clear definition of the extended object tracking
problem and discuss its delimitation to other types of object tracking. Next,
different aspects of extended object modelling are extensively discussed.
Subsequently, we give a tutorial introduction to two basic and well used
extended object tracking approaches - the random matrix approach and the Kalman
filter-based approach for star-convex shapes. The next part treats the tracking
of multiple extended objects and elaborates how the large number of feasible
association hypotheses can be tackled using both Random Finite Set (RFS) and
Non-RFS multi-object trackers. The article concludes with a summary of current
applications, where four example applications involving camera, X-band radar,
light detection and ranging (lidar), red-green-blue-depth (RGB-D) sensors are
highlighted.Comment: 30 pages, 19 figure
A New Approach to Linear/Nonlinear Distributed Fusion Estimation Problem
Disturbance noises are always bounded in a practical system, while fusion
estimation is to best utilize multiple sensor data containing noises for the
purpose of estimating a quantity--a parameter or process. However, few results
are focused on the information fusion estimation problem under bounded noises.
In this paper, we study the distributed fusion estimation problem for linear
time-varying systems and nonlinear systems with bounded noises, where the
addressed noises do not provide any statistical information, and are unknown
but bounded. When considering linear time-varying fusion systems with bounded
noises, a new local Kalman-like estimator is designed such that the square
error of the estimator is bounded as time goes to . A novel
constructive method is proposed to find an upper bound of fusion estimation
error, then a convex optimization problem on the design of an optimal weighting
fusion criterion is established in terms of linear matrix inequalities, which
can be solved by standard software packages. Furthermore, according to the
design method of linear time-varying fusion systems, each local nonlinear
estimator is derived for nonlinear systems with bounded noises by using Taylor
series expansion, and a corresponding distributed fusion criterion is obtained
by solving a convex optimization problem. Finally, target tracking system and
localization of a mobile robot are given to show the advantages and
effectiveness of the proposed methods.Comment: 9 pages, 3 figure
Decentralized Riemannian Particle Filtering with Applications to Multi-Agent Localization
The primary focus of this research is to develop consistent nonlinear decentralized particle filtering approaches to the problem of multiple agent localization. A key aspect in our development is the use of Riemannian geometry to exploit the inherently non-Euclidean characteristics that are typical when considering multiple agent localization scenarios. A decentralized formulation is considered due to the practical advantages it provides over centralized fusion architectures. Inspiration is taken from the relatively new field of information geometry and the more established research field of computer vision. Differential geometric tools such as manifolds, geodesics, tangent spaces, exponential, and logarithmic mappings are used extensively to describe probabilistic quantities. Numerous probabilistic parameterizations were identified, settling on the efficient square-root probability density function parameterization. The square-root parameterization has the benefit of allowing filter calculations to be carried out on the well studied Riemannian unit hypersphere. A key advantage for selecting the unit hypersphere is that it permits closed-form calculations, a characteristic that is not shared by current solution approaches. Through the use of the Riemannian geometry of the unit hypersphere, we are able to demonstrate the ability to produce estimates that are not overly optimistic. Results are presented that clearly show the ability of the proposed approaches to outperform current state-of-the-art decentralized particle filtering methods. In particular, results are presented that emphasize the achievable improvement in estimation error, estimator consistency, and required computational burden
Localization of Non-Linearly Modeled Autonomous Mobile Robots Using Out-of-Sequence Measurements
This paper presents a state of the art of the estimation algorithms dealing with Out-of-Sequence (OOS) measurements for non-linearly modeled systems. The state of the art includes a critical analysis of the algorithm properties that takes into account the applicability of these techniques to autonomous mobile robot navigation based on the fusion of the measurements provided, delayed and OOS, by multiple sensors. Besides, it shows a representative example of the use of one of the most computationally efficient approaches in the localization module of the control software of a real robot (which has non-linear dynamics, and linear and non-linear sensors) and compares its performance against other approaches. The simulated results obtained with the selected OOS algorithm shows the computational requirements that each sensor of the robot imposes to it. The real experiments show how the inclusion of the selected OOS algorithm in the control software lets the robot successfully navigate in spite of receiving many OOS measurements. Finally, the comparison highlights that not only is the selected OOS algorithm among the best performing ones of the comparison, but it also has the lowest computational and memory cost
Fully Decentralized Estimation Using Square-Root Decompositions
Networks consisting of several spatially distributed sensor nodes are useful in many applications. While distributed information processing can be more robust and flexible than centralized filtering, it requires careful consideration of dependencies between local state estimates.
This paper proposes an algorithm to keep track of dependencies in decentralized systems where no dedicated fusion center is present. Specifically, it addresses double-counting of measurement information due to intermediate fusion results and correlations due to common process noise and common prior information. To limit the necessary amount of data, this paper introduces a method to partially bound correlations, leading to a more conservative fusion result than the optimal reconstruction while reducing the necessary amount of data. Simulation studies compare the performance and convergence rate of the proposed algorithm to other state-of-the-art methods
State Estimation Fusion for Linear Microgrids over an Unreliable Network
Microgrids should be continuously monitored in order to maintain suitable voltages over
time. Microgrids are mainly monitored remotely, and their measurement data transmitted through
lossy communication networks are vulnerable to cyberattacks and packet loss. The current study
leverages the idea of data fusion to address this problem. Hence, this paper investigates the effects of
estimation fusion using various machine-learning (ML) regression methods as data fusion methods
by aggregating the distributed Kalman filter (KF)-based state estimates of a linear smart microgrid
in order to achieve more accurate and reliable state estimates. This unreliability in measurements
is because they are received through a lossy communication network that incorporates packet loss
and cyberattacks. In addition to ML regression methods, multi-layer perceptron (MLP) and dependent
ordered weighted averaging (DOWA) operators are also employed for further comparisons.
The results of simulation on the IEEE 4-bus model validate the effectiveness of the employed ML
regression methods through the RMSE, MAE and R-squared indices under the condition of missing
and manipulated measurements. In general, the results obtained by the Random Forest regression
method were more accurate than those of other methods.This research was partially funded by public research projects of Spanish Ministry of
Science and Innovation, references PID2020-118249RB-C22 and PDC2021-121567-C22 - AEI/10.13039/
501100011033, and by the Madrid Government (Comunidad de Madrid-Spain) under the Multiannual
Agreement with UC3M in the line of Excellence of University Professors, reference EPUC3M17
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