1,103 research outputs found
Probabilistic Framework for Sensor Management
A probabilistic sensor management framework is introduced, which maximizes the utility of sensor systems with many different sensing modalities by dynamically configuring the sensor system in the most beneficial way. For this purpose, techniques from stochastic control and Bayesian estimation are combined such that long-term effects of possible sensor configurations and stochastic uncertainties resulting from noisy measurements can be incorporated into the sensor management decisions
A Distributed ADMM Approach to Non-Myopic Path Planning for Multi-Target Tracking
This paper investigates non-myopic path planning of mobile sensors for
multi-target tracking. Such problem has posed a high computational complexity
issue and/or the necessity of high-level decision making. Existing works tackle
these issues by heuristically assigning targets to each sensing agent and
solving the split problem for each agent. However, such heuristic methods
reduce the target estimation performance in the absence of considering the
changes of target state estimation along time. In this work, we detour the
task-assignment problem by reformulating the general non-myopic planning
problem to a distributed optimization problem with respect to targets. By
combining alternating direction method of multipliers (ADMM) and local
trajectory optimization method, we solve the problem and induce consensus
(i.e., high-level decisions) automatically among the targets. In addition, we
propose a modified receding-horizon control (RHC) scheme and edge-cutting
method for efficient real-time operation. The proposed algorithm is validated
through simulations in various scenarios.Comment: Copyright 2019 IEEE. Personal use of this material is permitted.
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Finite Impulse Response Filtering Algorithm with Adaptive Horizon Size Selection and Its Applications
It is known, that unlike the Kalman filter (KF) finite impulse response (FIR) filters allow to avoid the divergence and unsatisfactory object tracking connected with temporary perturbations and abrupt object changes. The main challenge is to provide the appropriate choice of a sliding window size for them. In this paper, the new finite impulse response (FIR) filtering algorithm with the adaptive horizon size selection is proposed. The algorithm uses the receding horizon optimal (RHOFIR) filter which receives estimates, an abrupt change detector and an adaptive recurrent mechanism for choosing the window size. Monotonicity and asymptotic properties of the estimation error covariance matrix and the RHOFIR filter gain are established. These results form a solid foundation for justifying the principal possibility to tune the filter gain using them and the developed adaptation mechanism. The proposed algorithm (the ARHOFIR filter) allows reducing the impact of disturbances by varying adaptively the sliding window size. The possibility of this follows from the fact that the window size affects the filter characteristics in different ways. The ARHOFIR filter chooses a large horizon size in the absence of abrupt disturbances and a little during the time intervals of their action. Due to this, it has better transient characteristics compared to the KF and RHOFIR filter at intervals where there is temporary uncertainty and may provide the same accuracy of estimates as the KF in their absence. By simulation, it is shown that the ARHOFIR filter is more robust than the KF and RHOFIR filter for the temporarily uncertain systems
Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Age
Simultaneous Localization and Mapping (SLAM)consists in the concurrent
construction of a model of the environment (the map), and the estimation of the
state of the robot moving within it. The SLAM community has made astonishing
progress over the last 30 years, enabling large-scale real-world applications,
and witnessing a steady transition of this technology to industry. We survey
the current state of SLAM. We start by presenting what is now the de-facto
standard formulation for SLAM. We then review related work, covering a broad
set of topics including robustness and scalability in long-term mapping, metric
and semantic representations for mapping, theoretical performance guarantees,
active SLAM and exploration, and other new frontiers. This paper simultaneously
serves as a position paper and tutorial to those who are users of SLAM. By
looking at the published research with a critical eye, we delineate open
challenges and new research issues, that still deserve careful scientific
investigation. The paper also contains the authors' take on two questions that
often animate discussions during robotics conferences: Do robots need SLAM? and
Is SLAM solved
OPTIMAL RECURSIVE DATA PROCESSING ALGORITHM USING BAYESIAN INFERENCE FOR UNDERWATER VEHICLE LOCALISATION AND NAVIGATION SYSTEMS
In the ocean environment, two dimensional Range & Bearings target motion analysis (TMA) is generally used. In the underwater scenario, the active sonar, positioned on a observer, is capable of sensing the sound waves reflected from the target in water. The sonar sensors in the water pick up the target reflected signal in the active mode. The observer is assumed to be moving in straight line and the target is assumed to be moving mostly in straight line with maneuver occasionally. The observer processes the measurements and estimates the target motion parameters, viz., Range, Bearing, Course and Speed of the target. It also generates the validity of each of these parameters. Here we try to apply Kalman Filter for the sea scenario using the input estimation technique to detect target maneuver, estimate target acceleration and correct the target state vector accordingly.             There are mainly two versions of Kalman Filter – a linearised Kalman Filter (LKF) in which polar measurements are converted into Cartesian coordinates and the well-known Extended Kalman Filter (EKF). Recently S. T. Pork and L. E. Lee presented a detailed theoretical comparative study of the above two methods and stated that both the methods perform well. Here, EKF is used through out
A multi-model ensemble Kalman filter for data assimilation and forecasting
Data assimilation (DA) aims to optimally combine model forecasts and
observations that are both partial and noisy. Multi-model DA generalizes the
variational or Bayesian formulation of the Kalman filter, and we prove that it
is also the minimum variance linear unbiased estimator. Here, we formulate and
implement a multi-model ensemble Kalman filter (MM-EnKF) based on this
framework. The MM-EnKF can combine multiple model ensembles for both DA and
forecasting in a flow-dependent manner; it uses adaptive model error estimation
to provide matrix-valued weights for the separate models and the observations.
We apply this methodology to various situations using the Lorenz96 model for
illustration purposes. Our numerical experiments include multiple models with
parametric error, different resolved scales, and different fidelities. The
MM-EnKF results in significant error reductions compared to the best model, as
well as to an unweighted multi-model ensemble, with respect to both
probabilistic and deterministic error metrics.Comment: 23 pages, 10 figure
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