New results and applications for the Ideal Extended Kalman Filter asa Cramer-Rao lower bound estimator

Network ranging and clock synchronization based on two-way timing stamps exchange mechanism in complex GPS-denied environments is addressed in this paper. An estimator based on the Extended Kalman filter (EKF) is derived, according to which, the clock skew, clock offset, and ranging information can be jointly estimated. The proposed estimator provides off-line computation by storing the transmitting timing stamps in advance and could be implemented in asymmetrical and asynchronous scenarios. The simulation results show that the proposed estimator achieves a relative good performance than the existed estimators. In addition, a new Bayesian Cramér–Rao Lower Bound (B-CRLB) is derived. Numerous simulation results show that the proposed estimator meets the B-CRLB.

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Localization and Tracking of Discrete Mobile Scatterers in Vehicular Environments Using Delay Estimates

Sensors ◽

10.3390/s19214802 ◽

2019 ◽

Vol 19 (21) ◽

pp. 4802 ◽

Cited By ~ 3

Author(s):

Martin Schmidhammer ◽

Christian Gentner ◽

Benjamin Siebler ◽

Stephan Sand

Keyword(s):

Kalman Filter ◽

Lower Bound ◽

Extended Kalman Filter ◽

Optimization Problem ◽

Moving Objects ◽

Multipath Propagation ◽

Measurement Data ◽

Position Estimation ◽

Performance Bounds ◽

Cramer Rao Lower Bound

This paper describes an approach to detect, localize, and track moving, non-cooperative objects by exploiting multipath propagation. In a network of spatially distributed transmitting and receiving nodes, moving objects appear as discrete mobile scatterers. Therefore, the localization of mobile scatterers is formulated as a nonlinear optimization problem. An iterative nonlinear least squares algorithm following Levenberg and Marquardt is used for solving the optimization problem initially, and an extended Kalman filter is used for estimating the scatterer location recursively over time. The corresponding performance bounds are derived for both the snapshot based position estimation and the nonlinear sequential Bayesian estimation with the classic and the posterior Cramér–Rao lower bound. Thereby, a comparison of simulation results to the posterior Cramér–Rao lower bound confirms the applicability of the extended Kalman filter. The proposed approach is applied to estimate the position of a walking pedestrian sequentially based on wideband measurement data in an outdoor scenario. The evaluation shows that the pedestrian can be localized throughout the scenario with an accuracy of 0 . 8 m at 90% confidence.

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Underwater surveillance in non-Gaussian noisy environment

Measurement and Control ◽

10.1177/0020294019877515 ◽

2020 ◽

Vol 53 (1-2) ◽

pp. 250-261

Author(s):

B Omkar Lakshmi Jagan ◽

S Koteswara Rao

Keyword(s):

Kalman Filter ◽

Lower Bound ◽

Extended Kalman Filter ◽

Gaussian Noise ◽

Unscented Kalman Filter ◽

Doppler Frequency ◽

Gaussian Mixture ◽

Noisy Environment ◽

Sampled Measurements ◽

Non Gaussian

The aim of this paper is to evaluate the performance of different filtering algorithms in the presence of non-Gaussian noise environment for tracking underwater targets, using Doppler frequency and bearing measurements. The tracking using Doppler frequency and bearing measurements is popularly known as Doppler-bearing tracking. Here the measurements, that is, bearings and Doppler frequency, are considered to be corrupted with two types of non-Gaussian noises namely shot noise and Gaussian mixture noise. The non-Gaussian noise sampled measurements are assumed to be obtained (a) randomly throughout the process and (b) repeatedly at some particular time samples. The efficiency of these filters with the increase in non-Gaussian noise samples is discussed in this paper. The performance of filters is compared with that of Cramer-Rao Lower Bound. Doppler-bearing extended Kalman filter and Doppler-bearing unscented Kalman filter are chosen for this work.

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A Dynamic-Weighted Attenuation Memory Extended Kalman Filter Algorithm and Its Application in the Underwater Positioning

Mathematical Problems in Engineering ◽

10.1155/2021/3625362 ◽

2021 ◽

Vol 2021 ◽

pp. 1-11

Author(s):

Bo Guo ◽

Jianye Ma ◽

Cui Wang

Keyword(s):

Kalman Filter ◽

Attenuation Coefficient ◽

Extended Kalman Filter ◽

Attenuation Factor ◽

Model Verification ◽

Dynamic Selection ◽

Underwater Positioning ◽

Kalman Filter Algorithm ◽

Statistical Form ◽

The Ideal

Extended Kalman filter (EKF) plays an important role in the acoustic signal processing of underwater positioning. However, accumulative errors and model inaccuracies lead to divergence. Then, attenuation memory EKF is created in response to this issue which needs to manually select all or part of the parameters. Thus, a dynamic-weighted attenuation memory EKF is proposed. Firstly, several underwater positioning simulations under different conditions are carried out. Results show, with the change of parameter conditions in positioning, the ideal attenuation coefficient changes between 0.5 and 1, but it is difficult to express it in function formula or statistical form. Secondly, a dynamic selection method of attenuation factor is designed. In the later contrast simulation, the proposed method has improved the positioning performance compared with the existing attenuation memory filter algorithm. Finally, the results of physical model verification experiment show that the dynamic-weighted attenuation memory EKF algorithm not only suppresses divergence better but also avoids the subjectivity of attenuation coefficient selection to a certain extent.

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