Passive location of the nonlinear systems with fuzzy uncertainty

Yang, Xiaojun; Zou, Hongxing; Jian-jiang, Ding; Zhou, Zhijie; Liu, Daizhi

doi:10.1016/j.simpat.2009.11.005

Cited by 3 publications

(5 citation statements)

References 17 publications

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“…Meanwhile, the variables in (1) follow the probability distribution as the Gauss distribution.x( + 1 | ) represents the one-step estimation value;x( + 1) andẑ( + 1) represent the estimation values of x( + 1) and z( + 1). What is more, x( + 1 | ) and w( + 1) are independent and w( + 1) and k( + 1) are independent too [6,12]. All of these parameters obey trapezoidal distribution described as follows:…”

Section: The Fuzzy Extended Kalman Filtermentioning

confidence: 99%

“…What is more, the low performance of the noise statistics estimation may lead to poor filter performance or even lead to the divergence of the filter. To solve the problem listed above, some new methods [5][6][7][8][9][10][11][12] are given.…”

Section: Introductionmentioning

confidence: 99%

“…Among the above algorithms, [6] is more suitable for passive location and tracking with uncertainty that is based on quantifying uncertainty by trapezoidal possibility distributions. Being motivated by [6], [11] and [12] apply the FEKF in fault prediction and passive location in the nonlinear system with uncertainty, respectively. However, similar to the EKF, the FEKF could not estimate the state in the mismatching model system such as the maneuvering target tracking.…”

Section: Introductionmentioning

confidence: 99%

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The Robust Passive Location Algorithm for Maneuvering Target Tracking

Yang

Liu

Guo

et al. 2015

Mathematical Problems in Engineering

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With the advantages such as high security and far responding distance, the passive location has a broad application in military and civil domains such as radar and aerospace. However, most of the current passive location methods are based on the framework of the probability theory and cannot be used to deal with fuzzy uncertainty in the passive location systems. Though the fuzzy Kalman filter can be used in the uncertainty systems, it could not deal with the abrupt change of state like the maneuvering target which will lead to the filter divergence. Therefore, in order to track the maneuvering target in the fuzzy passive system, we proposed a robust fuzzy extended Kalman filter based on the orthogonality principle and the fuzzy filter in the paper. Conclusion can be made based on the simulation result that this new approach is more precise and more robust than the fuzzy filter.

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Section: The Fuzzy Extended Kalman Filtermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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The Robust Passive Location Algorithm for Maneuvering Target Tracking

Yang

Liu

Guo

et al. 2015

Mathematical Problems in Engineering

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“…The LS algorithms, including Taylor series method, extended Kalman filter, Chan, etc., [2]- [5], smooth the measurements noise and utilize utmost available information by transforming nonlinear measurement equations to linear ones. Svecova improved the Taylor series method, and applied a suitable weight of the input data to exploit for target positioning not only actual TOA measurements but also the positions estimated in the previous time instant [3].…”

Section: Introduction (Heading 1)mentioning

confidence: 99%

“…Li advanced the regularized constrained total least square algorithm to solve the location equations [4]. Yang advanced an iterated fuzzy extended Kalman filter to reduce the truncation error of linear processing and fuzzy uncertainty [5]. However, there are many limitations of utilize the measurements.…”

Section: Introduction (Heading 1)mentioning

confidence: 99%

The Virtual Antenna of Measurement Report Based Methods for Wireless Location

Gai

Tao

2011

2011 7th International Conference on Wireless Communications, Networking and Mobile Computing

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Though many location algorithms, such as least square methods, fingerprints, data fusion and proximity methods, have been suggested for positions estimation, most of their performances have been valuated based on the proposed simulations. The solution of distinguishing NLOS environments is still a Gordian knot. Our studies focus on the utilization of measurement report which is generated by the user equipment and contains the information of cell configurations and signal strengths. As the cell antenna azimuths which belong to the same base stations direct respectively, this paper suggests a notion of virtual antenna for wireless location in practical environment. The cells are categorized and a virtual antenna is defined to indicate mobile's direction more accurately. A step-by-step algorithm is also suggested for fitting the azimuths. The experiment of field measurements exhibits the high accuracies of our virtual antenna and a comparability of the fingerprints algorithm and our studies is shown.

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Distributed Kalman Filter With Fuzzy Noises Over Multiagent Systems

Lin

Deng

et al. 2022

IEEE Trans. Fuzzy Syst.

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Passive location of the nonlinear systems with fuzzy uncertainty

Cited by 3 publications

References 17 publications

The Robust Passive Location Algorithm for Maneuvering Target Tracking

The Robust Passive Location Algorithm for Maneuvering Target Tracking

The Virtual Antenna of Measurement Report Based Methods for Wireless Location

Distributed Kalman Filter With Fuzzy Noises Over Multiagent Systems

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