Conditionally Minimax Nonlinear Filter and Unscented Kalman Filter: Empirical Analysis and Comparison

Bosov, A. V.; Miller, Gregory

doi:10.1134/s0005117919070026

Cited by 3 publications

(6 citation statements)

References 20 publications

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“…The control based on the extended Kalman filter and the pseudo-measurements filter fail to follow the nominal path. In Figure 11, we show the results for the conditionally minimax nonlinear filter based on the data fusion from the dead reckoning seabed sensing and external bearing-only measurements defined by Equation (22) with structure Functions (24). The increase of the sampled standard deviation shown in Figure 11 in comparison with the prediction by virtue of the system case shown in Figure 8 is not very high.…”

Section: Auv Control Simulationmentioning

confidence: 98%

“…The details on the CMNF approach to the nonlinear stochastic systems state estimation, including the thorough justification of Equation (22) being the solution to Equation (23) and the conditions of the solution in Equation (23) existence, could be found in [22]. Further application of the concept along with the comparative numerical study is the matter of the works [23,24].…”

Section: Conditionnaly Minimax Nonlinear Filtermentioning

confidence: 99%

“…• CMNF: Conditionally minimax nonlinear Filter (22) with basic structure Functions (24) and covariances calculated by the Monte-Carlo sampling on a separate test set of 10 5 AUV paths; • UPMF: Unbiased pseudo-measurements Filter (21); • EKF: Extended Kalman filter, which provides a common approach to deal with the non-linearity in the system dynamics or observations [18].…”

Section: Auv Control Simulationmentioning

confidence: 99%

“…An alternative to traditional Kalman filtering is the conditionally optimal nonlinear filter proposed by V. S. Pugachev and developed by his successors into the conditionally minimax nonlinear filter (CMNF) [22]. This theory permits to develop the estimation procedure with given properties, for example, either with minimum dispersion or without bias, which is extremely important, for example, in target tracking based on bearing-only observations [23,24].In acoustic position estimation and target tracking, the bearing-only observation plays the principal role. As it was observed many years ago, the bearing angles may be transformed into linear observation, but with bias [25].…”

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confidence: 99%

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On AUV Control with the Aid of Position Estimation Algorithms Based on Acoustic Seabed Sensing and DOA Measurements

Miller

2019

Sensors

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This article discusses various approaches to the control of autonomous underwater vehicles (AUVs) with the aid of different velocity-position estimation algorithms. Traditionally this field is considered as the area of the extended Kalman filter (EKF) application: It became a universal tool for nonlinear observation models and its use is ubiquitous. Meanwhile, the specific characteristics of underwater navigation, such as an incomplete sets of measurements, constraints on the range metering or even impossibility of range measurements, observations provided by rather specific acoustic beacons, sonar observations, and other features seriously narrow the applicability of common instruments due to a high level of uncertainty and nonlinearity. The AUV navigation system, not being able to rely on a single source of position estimation, has to take into account all available information. This leads to the necessity of various complex estimation and data fusion algorithms, which are the matter of the present article. Here we discuss some approaches to the AUV position estimation such as conditionally minimax nonlinear filtering (CMNF) and unbiased pseudo-measurement filters (UPMFs) in conjunction with velocity estimation based on the seabed profile acoustic sensing. The presented estimation algorithms serve as a basis for a locally optimal AUV motion control algorithm, which is also presented. environment, all electromagnetic (and therefore video tools) have a very restricted range of operation, and only acoustic waves permit sensing and communication for rather long distances. The problem is that acoustic measurements are notably barren in comparison with the electromagnetic ones: They do not have such features as color or contrast and just measure the direction of arrival (DOA) and/or the distance from the vehicle to the reflecting surface, such as obstacle, seabed, or ship. Therefore, even using multiple irradiating sensors like acoustic sonars, one can only obtain an image of distances to the seabed relief. An introduction of acoustical beacons with known positions can help to determine the own AUV position by using an angular observation filter, and this is the area of the (short and/or long) baseline methods application [2]. These methods are especially effective in the limited area of operation; moreover, they may be used in the case of a moving baseline established on a ship [3] and even in docking maneuvering [4]. Acoustic methods used in active mode give access to the distance measurements, which make the position estimation problem much easier, though in some areas of application such measurements are not acceptable or must be restricted due to the energetic or masking constraints [5].The possibility of the sonar employment in AUV navigation has already been mentioned by some authors either in application to the situation, when an accurate seabed map is available, or in the case of presence of significant seabed features [6]. Moreover, the active pinging can be used to determine one's speed over the seabed by ...

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Section: Auv Control Simulationmentioning

confidence: 98%

Section: Conditionnaly Minimax Nonlinear Filtermentioning

confidence: 99%

Section: Auv Control Simulationmentioning

confidence: 99%

mentioning

confidence: 99%

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On AUV Control with the Aid of Position Estimation Algorithms Based on Acoustic Seabed Sensing and DOA Measurements

Miller

2019

Sensors

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“…Furthermore, the extended KF (EKF) and the unscented KF (UKF) [17] are two common usages of NKF. Most importantly, the UKF approach has not yet been employed to lessen the uncertainty of multi-step-ahead flood forecasting driven by a recurrent neural network (RNN) according to a review of literature [18][19][20][21]. Despite there are several researches associated with the combination of UKF/EKF and hydrological models [22][23][24] on hydrological domain, all of them concentrate on quantifying the uncertainty of hydrological forecast driven by static (i.e., non-recurrent) artificial neural networks (ANNs), e.g., feed-forward neural network and local linear models as well as the hydraulic model.…”

mentioning

confidence: 99%

Improving the Reliability of Probabilistic Multi-Step-Ahead Flood Forecasting by Fusing Unscented Kalman Filter with Recurrent Neural Network

Zhou

Guo

et al. 2020

Water

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It is fundamentally challenging to quantify the uncertainty of data-driven flood forecasting. This study introduces a general framework for probabilistic flood forecasting conditional on point forecasts. We adopt an unscented Kalman filter (UKF) post-processing technique to model the point forecasts made by a recurrent neural network and their corresponding observations. The methodology is tested by using a long-term 6-h timescale inflow series of the Three Gorges Reservoir in China. The main merits of the proposed approach lie in: first, overcoming the under-prediction phenomena in data-driven flood forecasting; second, alleviating the uncertainty encountered in data-driven flood forecasting. Two commonly used artificial neural networks, a recurrent and a static neural network, were used to make the point forecasts. Then the UKF approach driven by the point forecasts demonstrated its competency in increasing the reliability of probabilistic flood forecasts significantly, where predictive distributions encountered in multi-step-ahead flood forecasts were effectively reduced to small ranges. The results demonstrated that the UKF plus recurrent neural network approach could suitably extract the complex non-linear dependence structure between the model’s outputs and observed inflows and overcome the systematic error so that model reliability as well as forecast accuracy for future horizons could be significantly improved.

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Navigation of Underwater Drones and Integration of Acoustic Sensing with Onboard Inertial Navigation System

Miller

2021

Drones

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The navigation of autonomous underwater vehicles is a major scientific and technological challenge. The principal difficulty is the opacity of the water media for usual types of radiation except for the acoustic waves. Thus, an acoustic transducer (array) composed of an acoustic sonar is the only tool for external measurements of the AUV attitude and position. Another difficulty is the inconstancy of the speed of propagation of acoustic waves, which depends on the temperature, salinity, and pressure. For this reason, only the data fusion of the acoustic measurements with data from other onboard inertial navigation system sensors can provide the necessary estimation quality and robustness. This review presents common approaches to underwater navigation and also one novel method of velocity measurement. The latter is an analog of the well-known Optical Flow method but based on a sequence of sonar array measurements.

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Conditionally Minimax Nonlinear Filter and Unscented Kalman Filter: Empirical Analysis and Comparison

Cited by 3 publications

References 20 publications

On AUV Control with the Aid of Position Estimation Algorithms Based on Acoustic Seabed Sensing and DOA Measurements

On AUV Control with the Aid of Position Estimation Algorithms Based on Acoustic Seabed Sensing and DOA Measurements

Improving the Reliability of Probabilistic Multi-Step-Ahead Flood Forecasting by Fusing Unscented Kalman Filter with Recurrent Neural Network

Navigation of Underwater Drones and Integration of Acoustic Sensing with Onboard Inertial Navigation System

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