Passive Position Location Estimation Using the Extended Kalman Filter

Spingarn, K.

doi:10.1109/taes.1987.310889

Cited by 82 publications

(19 citation statements)

References 9 publications

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“…[25][26][27][28][29] Remark 1. Generally, it is proved that if the prior model sequence set has a large scale and the observations are effective, the MEMM approach is effective.…”

Section: Memm Frameworkmentioning

confidence: 99%

Geometrical entropy approach for variable structure multiple-model estimation

Han

Xue

Peng

2015

Chinese Journal of Aeronautics

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The variable structure multiple-model (VSMM) estimation approach, one of the multiple-model (MM) estimation approaches, is popular in handling state estimation problems with mode uncertainties. In the VSMM algorithms, the model sequence set adaptation (MSA) plays a key role. The MSA methods are challenged in both theory and practice for the target modes and the real observation error distributions are usually uncertain in practice. In this paper, a geometrical entropy (GE) measure is proposed so that the MSA is achieved on the minimum geometrical entropy (MGE) principle. Consequently, the minimum geometrical entropy multiple-model (MGEMM) framework is proposed, and two suboptimal algorithms, the particle filter k-means minimum geometrical entropy multiple-model algorithm (PF-KMGEMM) as well as the particle filter adaptive minimum geometrical entropy multiple-model algorithm (PF-AMGEMM), are established for practical applications. The proposed algorithms are tested in three groups of maneuvering target tracking scenarios with mode and observation error distribution uncertainties. Numerical simulations have demonstrated that compared to several existing algorithms, the MGE-based algorithms can achieve more robust and accurate estimation results when the real observation error is inconsistent with a priori. ª 2015 Production and hosting by Elsevier Ltd. on behalf of CSAA & BUAA. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

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“…[25][26][27][28][29] Remark 1. Generally, it is proved that if the prior model sequence set has a large scale and the observations are effective, the MEMM approach is effective.…”

Section: Memm Frameworkmentioning

confidence: 99%

Geometrical entropy approach for variable structure multiple-model estimation

Han

Xue

Peng

2015

Chinese Journal of Aeronautics

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“…The problem of geolocating an emitter has been approached through many well known methods: batch approaches such as the Gauss-Newton routine 14 , and iterative approaches like the Kalman filter 15 . These are usually discussed in the context of one receiver, which must then be moving if it is to geolocate even the simplest case: a stationary emitter.…”

Section: 1mentioning

confidence: 99%

<title>Target location by self-organizing autonomous air vehicles</title>

2002

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Target location is a problem where the application of multiple sensors that are geographically distributed can determine or improve the location estimate of a target. If these sensors are capable of cooperative behaviour then the information from each sensor can be autonomously fused to provide an estimate of the target position. The individual sensors may be quite unsophisticated, yet the observation system that is created through cooperation and adaptive networking of these sensors provides sufficient process gain to achieve target location accuracies similar to those of expensive centralised sensor systems.The accuracy of target location estimates depends heavily on the separation distance between the sensors. Large baseline geometry takes advantage of many seemingly unsophisticated bearing measurements that are organised into a coordinated observation system to locate a target.Team formation is one method to address coordination of distributed sensors, data fusion, sensor resource and energy management, and communication link control based on the concept of cooperating machines 1,2,3 . We apply an algorithm for agent team formation 4 inspired by the self-organising behaviour observed in colonies of ants, to the problem of integrating the sensors of a group of networked mini-Autonomous Air Vehicles (AAVs). The mini-AAVs are tasked to locate targets within a region of interest. The challenge we address is to make the location estimation system adaptive to a dynamic environment and robust to failure. Simulation results are presented which address issues in distributed data fusion, sensor resource and energy management, and communication link control, for a group of mini-AAVs.

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“…The algorithm beats the Stanfield's algorithm in such conditions but is rather computationally demanding. In [7] the Extended Kalman Filter and the Iterated Extended Kalman Filter are proposed for the problem of passive position location.…”

Section: Introductionmentioning

confidence: 99%

“…An analysis of the performance of the direction-finding location systems is presented in [18]. In [7], the performances of the Extended Kalman Filter, the Iterated Extended Kalman Filter, and the method of least squares are compared.…”

Section: Introductionmentioning

confidence: 99%

Least-squares estimation techniques in stationary bearings-only target localization

Ahmadian

Pezeshk

2014

2014 22nd Iranian Conference on Electrical Engineering (ICEE)

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A complete review of the Least Square (LS) estimation techniques, in stationary position finding using bearing measurements is presented. Equations are presented/ derived in various ways to estimate the target position as well as the sensor bearing bias. Simulation results are compared in a variety of bearing random error and bias as well as different geometries to help the designer decide on an appropriate algorithm.

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Passive Position Location Estimation Using the Extended Kalman Filter

Cited by 82 publications

References 9 publications

Geometrical entropy approach for variable structure multiple-model estimation

Geometrical entropy approach for variable structure multiple-model estimation

<title>Target location by self-organizing autonomous air vehicles</title>

Least-squares estimation techniques in stationary bearings-only target localization

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