Recursive robust least squares estimator for time-varying linear systems with a noise corrupted measurement matrix

Ra, Won‐Sang; Whang, Ick‐Ho; Ahn, Jo Young; Park, J.B.

doi:10.1049/iet-cta:20050331

Cited by 20 publications

(17 citation statements)

References 15 publications

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“…In order to successfully eliminate the estimation errors, (14) and (15), and to flee from the burden, the RoLS approach should be considered for the localization problem [13].…”

Section: Lemma 1: (Estimation Errors With Nls)mentioning

confidence: 99%

“…If the errors can be represented with the available matrix and the predefined stochastic information, it is possible to remove from the NLS. With this strategy, the RoLS was developed [13]. Based on the RoLS, the target localization algorithm can be shown the convergence in probability.…”

Section: Target Localization Algorithm Based On the Robust Least Squaresmentioning

confidence: 99%

“…In spite of its usefulness, the localization performance of the existing PL localization schemes is severely deteriorated under low SNR conditions. This is because the noisy data is reused for constituting the measurement matrix [11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

“…In 2007, the RoLS (robust least squares) estimator was proposed which can compensate the estimation error caused by the noises of the measurement matrix and the measurement [13]. By using the predefined auto-correlation of the measurement matrix noise and the cross-correlation between the measurement matrix noise and the measurement noise, the RoLS compensates the estimation error without computational burden.…”

Section: Introductionmentioning

confidence: 99%

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Performance Analysis of the Robust Least Squares Target Localization Scheme using RDOA Measurements

Choi

Sang

Park³

et al. 2012

Journal of Electrical Engineering and Technology

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-A practical recursive linear robust estimation scheme is proposed for target localization in the sensor network which provides range difference of arrival (RDOA) measurements. In order to radically solve the known practical difficulties such as sensitivity for initial guess and heavy computational burden caused by intrinsic nonlinearity of the RDOA based target localization problem, an uncertain linear measurement model is newly derived. In the suggested problem setting, the target localization performance of the conventional linear estimation schemes might be severely degraded under the low SNR condition and be affected by the target position in the sensor network. This motivates us to devise a new sensor network localization algorithm within the framework of the recently developed robust least squares estimation theory. Provided that the statistical information regarding RDOA measurements are available, the estimate of the proposition method shows the convergence in probability to the true target position. Through the computer simulations, the omnidirectional target localization performance and consistency of the proposed algorithm are compared to those of the existing ones. It is shown that the proposed method is more reliable than the total least squares method and the linear correction least squares method.

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“…In order to successfully eliminate the estimation errors, (14) and (15), and to flee from the burden, the RoLS approach should be considered for the localization problem [13].…”

Section: Lemma 1: (Estimation Errors With Nls)mentioning

confidence: 99%

Section: Target Localization Algorithm Based On the Robust Least Squaresmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Performance Analysis of the Robust Least Squares Target Localization Scheme using RDOA Measurements

Choi

Sang

Park³

et al. 2012

Journal of Electrical Engineering and Technology

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“…필터와 거의 유사한 성능을 보장한다는 특징이 있다 [12,13]. 표 1에 정리된 강인 최소자승 추정기의 불확정 선형 상 태공간 모델과 식 (13)의 움직임 영상훼손 모델을 비교하 면, 다음 대응관계를 도출할 수 있다.…”

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Robust Least Squares Motion Deblurring Using Inertial Sensor for Strapdown Image IR Sensors

Kim

2012

Journal of Institute of Control, Robotics and Systems

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This paper proposes a new robust motion deblurring filter using the inertial sensor measurements for strapdown image IR applications. With taking the PSF measurement error into account, the motion blurred image is modeled by the linear uncertain state space equation with the noise corrupted measurement matrix and the stochastic parameter uncertainty. This motivates us to solve the motion deblurring problem based on the recently developed robust least squares estimation theory. In order to suppress the ringing effect on the deblurred image, the robust least squares estimator is slightly modified by adoping the ridge-regression concept. Through the computer simulations using the actual IR scenes, it is demonstrated that the proposed algorithm shows superior and reliable motion deblurring performance even in the presence of time-varying motion artifact. [7,11]. BD 기법은 점 확 산 함수가 시불변 특성을 갖는 HST (Hubble Space Telescope) 분야에서는 우수한 복원성능을 보이는 것으로 알려져 있다 [7]. 하지만, 지금까지의 연구결과에 따르면 움 직임에 의한 영상훼손이 다양한 시변 특성을 갖는 실제 상 황에서는 BD 기법에 의한 영상복원에 한계가 있음이 지적 되어 왔다 [11].

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Augmented instrumental variable method for position and heading estimation with RDOA measurements

Choi

Cheon

Park

et al. 2012

Int. J. Control Autom. Syst.

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Recursive robust least squares estimator for time-varying linear systems with a noise corrupted measurement matrix

Cited by 20 publications

References 15 publications

Performance Analysis of the Robust Least Squares Target Localization Scheme using RDOA Measurements

Performance Analysis of the Robust Least Squares Target Localization Scheme using RDOA Measurements

Robust Least Squares Motion Deblurring Using Inertial Sensor for Strapdown Image IR Sensors

Augmented instrumental variable method for position and heading estimation with RDOA measurements

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