Convergence issues of taylor series method in determining unknown target location using hyperbolic multilateration

Chaitanya, D. Eswara; Kumar, Manoj; Rao, G. Sasibhushana; Goswami, Rajkumar

doi:10.1109/icsemr.2014.7043670

Cited by 10 publications

(8 citation statements)

References 3 publications

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“…The nonlinear approach lateration algorithm utilizes linear approximation methods and iteration process to establish a linear relationship between the TDOA measurements and emitter position [2, 9,10]. Due to the iteration process, convergence is an issue and thus, not suitable for a passive positioning application [11]. Algebraic manipulation is used in the linear approach of the lateration algorithm to obtain the linear relationship [12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Position estimation performance evaluation of a linear lateration algorithm with an SNR-based reference station selection technique

Yaro

2018

Nig. J. Tech.

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The position estimation accuracy of the multilateration system lateration algorithm depends on several factors such as the number of stations deployed, time difference of arrival (TDOA) estimation technique and the choice of reference station. In this paper, a technique to select the suitable reference station for the lateration algorithm based on received signal-to-noise (SNR) at each of the deployed stations is presented. The position estimation performance analysis of the lateration algorithm with the reference selection technique is carried out and improvement in the position estimation accuracy is determined by comparing it with the convention approach of using fixed reference station. Monte Carlo simulation results when compared with the conventional approach based on a square station configuration showed a reduction in the position estimation error of about 20%.

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Section: Introductionmentioning

confidence: 99%

Position estimation performance evaluation of a linear lateration algorithm with an SNR-based reference station selection technique

Yaro

2018

Nig. J. Tech.

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“…(10) to solve the position of emitter. Given i-th and j-th GRSs at position ( , , ) and ( , , ) respectively, a blind spot exits if (15) To obtain the mathematical representation of the blind spot in range ( ) and bearing ( ), substituting Eq. (15) …”

Section: B Position Estimation Process Blind Spotsmentioning

confidence: 99%

“…The linear approach has no convergence issue since it does not require the estimate of the initial position of emitter [15]. It is faster than the nonlinear method but is very sensitive to the input measurement error.…”

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confidence: 99%

“…An initial random position of emitter is first inputted and iteratively refined to the final position estimate by minimizing the least square (LS) cost function. The nonlinear approach has limitations for real-time application since the PE solution convergence depends on the difference between the initial PE estimates with the actual position of emitter [15]. For the linear method, the algebraic manipulation of a pair of hyperbolic equations results in a single linear plane equation with the position of emitter as the unknown variable [16][17][18][19][20][21][22].…”

mentioning

confidence: 99%

“…Due to this nonlinearity, several methods are developed to solve these equations [12][13][14][15][16][17][18][19][20][21][22][23][24][25] either in linear/closed-form method or nonlinear/open-form method [1,12]. In the nonlinear method, the first step is to linearize the nonlinear hyperbolic equations using series expansion such as the Taylor series expansion method [13][14][15]. An initial random position of emitter is first inputted and iteratively refined to the final position estimate by minimizing the least square (LS) cost function.…”

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confidence: 99%

See 2 more Smart Citations

Position Estimation Error Performance Model for a Minimum Configuration 3-D Multilateration

Yaro¹,

Sha’ameri²,

Kamel³

2018

IJEEI

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A multilateration system estimates the position of emitter using time difference of arrival (TDOA) measurements with a lateration algorithm. It involves solving a set of hyperbolic plane equations to determine the position of the emitter given the TDOA measurements that corresponds to the path difference (PD) measurement in distance. A performance model is developed using the relative maximum error bound (RMEB) which relates the plane equation condition number, the relative ground receiving station (GRS) geometry and the PD measurement error to estimate the position estimation (PE) error. By using air traffic monitoring for civil aviation as an application, Monte Carlo simulation verifies the PE error of the performance model for a square GRS configuration. The coverage assumed a 360 0 bearing, a range of up to 200 km and a maximum altitude of 15 km. Simulation results also show that the performance model estimates the horizontal position error with a maximum absolute error of 0.1 km up to a range of 200 km at an altitude of 15 km and a minimum absolute error of 0.2 km at an altitude of 15 km.

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Positioning Strategies: Implementation and Applications of Major Source Localization and Positioning Approaches Over Indian Subcontinent

Laveti¹,

Chaitanya

Devi

et al. 2020

Lecture Notes in Electrical Engineering

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Convergence issues of taylor series method in determining unknown target location using hyperbolic multilateration

Cited by 10 publications

References 3 publications

Position estimation performance evaluation of a linear lateration algorithm with an SNR-based reference station selection technique

Position estimation performance evaluation of a linear lateration algorithm with an SNR-based reference station selection technique

Position Estimation Error Performance Model for a Minimum Configuration 3-D Multilateration

Positioning Strategies: Implementation and Applications of Major Source Localization and Positioning Approaches Over Indian Subcontinent

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