Block LMS-based source localization using range measurement

Park, Chee‐Hyun; Hong, Kwang-Seok

doi:10.1016/j.dsp.2010.06.014

Cited by 6 publications

(9 citation statements)

References 8 publications

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“…This proposed algorithm modifies the block LMS source localization algorithm [1] to robustify the block LMS algorithm against outliers by using a weighting matrix which is given as the inverse of the square error or an exponential function with a negative exponent. Also, we adopt the block Newton method instead of the block LMS algorithm because the convergence rate of the Newton method is known to be much faster than that of the LMS algorithm [24].…”

Section: Wbn Methodsmentioning

confidence: 99%

“…, superscript is the iteration number, and μ is a positive step size (see [1] for details). Note that the input signal in the conventional LMS algorithm was substituted with A T in the block LMS method.…”

Section: Block Lms Source Localization [1]mentioning

confidence: 99%

“…The first is that the wireless localization systems must be able to cope with rapidly varying channel conditions. To do this, adaptive filters have been important in adaptive source localization methods to stabilize localization performance under varying channel conditions [1][2][3]. However, these adaptive source localization algorithms were designed for the Gaussian noise situation and their localization performance is severely degraded in impulsive noise environments, such as Gaussian mixture and Student's t distribution.…”

Section: Introductionmentioning

confidence: 99%

“…Adaptive localization methods have been proposed for positioning in changing environments [1][2][3]. However, conventional adaptive localization algorithms, which minimize the squared error sum, are not robust to non-Gaussian noise (impulsive noise) situations, so they must be adapted to impulsive noise conditions.…”

Section: Introductionmentioning

confidence: 99%

“…However, these adaptive source localization algorithms were designed for the Gaussian noise situation and their localization performance is severely degraded in impulsive noise environments, such as Gaussian mixture and Student's t distribution. Also, the convergence rate of the least mean square (LMS) algorithm adopted in *Correspondence: jchang@hanyang.ac.kr 2 Department of Electronic Engineering, Hanyang University, Seoul 133-791, Republic of Korea Full list of author information is available at the end of the article [1][2][3] may be much slower in some adverse environments. The second key issue is the challenge of localization in environments of line-of-sight (LOS)/non-line-of-sight (NLOS) mixture.…”

Section: Introductionmentioning

confidence: 99%

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Adaptive robust time-of-arrival source localization algorithm based on variable step size weighted block Newton method

Park

2017

J Wireless Com Network

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We propose a line-of-sight (LOS)/non-line-of-sight (NLOS) mixture source localization algorithms that utilize the weighted block Newton (WBN) and variable step size WBN (VSSWBN) method, in which the weighting matrix is determined in the form of the inverse of the squared error or as an exponential function with a negative exponent. The proposed WBN and VSSWBN algorithms converge in two iterations; thus, the required number of extra samples in the transient period is negligible. Also, we perform an analysis of the mean square error (MSE) of the weighted block Newton method. To verify the superiority of the proposed methods, the MSE performances are compared via extensive simulation.

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Section: Wbn Methodsmentioning

confidence: 99%

Section: Block Lms Source Localization [1]mentioning

confidence: 99%