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A New Pseudolinear Filter for Bearings-Only Tracking without Requirement of Bias Compensation

Shizhe Bu, Aiqiang Meng, Gongjian Zhou
2021 Sensors  
The pseudolinear state-space model of bearings-only tracking is first developed. The correlation between the pseudomeasurement matrix and pseudolinear noise is thoroughly analyzed.  ...  In bearings-only tracking systems, the pseudolinear Kalman filter (PLKF) has advantages in stability and computational complexity, but suffers from correlation problems.  ...  The New Pseudolinear Filter In this section, we propose a new pseudolinear filter under the MMSE framework for bearings-only target tracking, which is referred as the pseudolinear-MMSE (PL-MMSE).  ... 
doi:10.3390/s21165444 pmid:34450886 pmcid:PMC8399602 fatcat:ejtkdpvpq5czvpk4n6twrilwle

Target Localization and Sensor Movement Trajectory Planning with Bearing-Only Measurements in Three Dimensional Space

Yiqun Zou, Bilu Gao, Xiafei Tang, Lingli Yu
2022 Applied Sciences  
Compared with traditional methods, the bias compensation method estimates the unknown variance of bearing noise consistently, which is utilized in pseudo-linear target localization to achieve higher precision  ...  In order to improve the accuracy of bearing-only localization in three dimensional (3D) space, this paper proposes a novel bias compensation method and a new single-sensor maneuvering trajectory algorithm  ...  Acknowledgments: The authors are also grateful to Professor Erwei Bai in the university of Iowa for shedding light on some initial research in this paper.  ... 
doi:10.3390/app12136739 fatcat:c4a6qsghsffqnfgnxdpad4cvhy

Bearing only Target Tracking using Single and Multisensor: A Review

B. Sindhu, J. Valarmathi, S. Christopher
2019 Journal of Engineering Science and Technology Review  
The brief review of methods used for estimating the target state in single and multi-sensor bearing only tracking (BOT) is presented in this paper.  ...  It deals with the target state estimation using bearing only measurements. BOT is difficult because of its poor observability in target state and nonlinearity in measurements.  ...  The authors have proposed a method to model the bearing only offset bias and the maximum likelihood estimators are used to estimate the biases.  ... 
doi:10.25103/jestr.121.07 fatcat:kt7frpjjj5gqbfxyxchg2r7pqi

Bias Compensation for AOA-Geolocation of Known Altitude Target Using Single Satellite

Chaoxin He, Min Zhang, Fucheng Guo
2019 IEEE Access  
To reduce the passive localization bias caused by the nonlinearity between the target position and measurements under high noise level and poor geometry conditions, the bias compensation method for known  ...  The basic idea of this method is to estimate the bias first, and then subtract it from the estimation of the target position.  ...  Reference [20] developed a reduced-bias pseudolinear estimator for bias compensation in bearings-only geolocation.  ... 
doi:10.1109/access.2019.2912864 fatcat:sbf37cdpwjgytfsup5fmh44p4y

A nonlinear tracking algorithm with range-rate measurements based on unbiased measurement conversion [article]

Lianmeng Jiao, Quan Pan, Yan Liang, Feng Yang
2014 arXiv   pre-print
The three-dimensional CMKF-U with only position measurements is extended to solve the nonlinear tracking problem with range-rate measurements in this paper.  ...  A pseudo measurement is constructed by the product of range and range-rate measurements to reduce the high nonlinearity of the range-rate measurements with respect to the target state; then the mean and  ...  z N ψ − = = ∑ (16) where, i z denotes the vector of converted measurement errors in realization i , compensated for the hypothesized bias, zz P is the hypothesized covariance of the errors, and N denotes  ... 
arXiv:1412.5524v1 fatcat:rxei54qhkzgvdnd3m4oq2h64cu

Optimal 3D Angle of Arrival Sensor Placement with Gaussian Priors

Rongyan Zhou, Jianfeng Chen, Weijie Tan, Qingli Yan, Chang Cai
2021 Entropy  
and 6, while the estimation bias remains very close to zero (smaller than 0.15 m).  ...  Based on this finding, an optimal sensor placement method using 3D rotation was created for when prior information exists as to the target location.  ...  Many estimators have been developed for AOA-based localization. A 3D one-step pseudolinear estimator (PLE) with a bias compensation strategy was proposed in [10] .  ... 
doi:10.3390/e23111379 pmid:34828076 pmcid:PMC8623848 fatcat:dxzc74rvqbafxa5ynv6mfelg7y

Novel passive localization algorithm based on double side matrix-restricted total least squares

Zheng Xu, Changwen Qu, Changhai Wang
2013 Chinese Journal of Aeronautics  
In order to solve the bearings-only passive localization problem in the presence of erroneous observer position, a novel algorithm based on double side matrix-restricted total least squares (DSMRTLS) is  ...  First, the aforementioned passive localization problem is transferred to the DSMRTLS problem by deriving a multiplicative structure for both the observation matrix and the observation vector.  ...  Acknowledgements The authors would like to thank the anonymous reviewers for their critical and constructive review of the manuscript.  ... 
doi:10.1016/j.cja.2013.06.009 fatcat:aa5rmdzaezdbpaz6dk4uw7kr6q

Intelligence-Aware Batch Processing for TMA with Bearings-Only Measurements

Gabriele Oliva, Alfonso Farina, Roberto Setola
2021 Sensors  
This paper develops a framework to track the trajectory of a target in 2D by considering a moving ownship able to measure bearing measurements.  ...  The approach is formally characterized by providing a constrained maximum likelihood estimation (MLE) formulation and by extending the definition of the Cramér–Rao lower bound (CRLB) matrix to the case  ...  Acknowledgments: The authors would like to express their sincere gratitude for the useful comments and suggestions by the guest editors and the anonymous reviewers during the review process.  ... 
doi:10.3390/s21217211 pmid:34770519 pmcid:PMC8587890 fatcat:lfkfqjjagfcarmjmb2jejdrwfe

Linear Robust Target Tracking Filter Using the Range Differences Measured By Formation Flying Multiple UAVs
다중 UAV에서 측정된 거리차 정보를 이용한 선형 강인 표적추적 필터 설계

Hye-Kyung Lee, Seul-Ki Han, Won-Sang Ra
2012 The Transactions of The Korean Institute of Electrical Engineers  
In order to solve the range difference based passive target tracking problem within the framework of linear robust state estimation, the uncertain linear measurement model which contains the stochastic  ...  For the cruciform formation flying UAVs, the relationship between the target tracking performance and the measurement errors is quantitatively analyzed.  ...  Bullock, "A stochastic perturbation analysis of bias in the extended Kalman filter as applied to bearing-only estimation," IEEE Conf.Decision and Contr., pp. 3778-3783, 1992. [11] K.  ... 
doi:10.5370/kiee.2012.61.2.284 fatcat:ijfbfuuhfba5dp5emtgohatqly

Decentralized Geolocation and Bias Estimation for Uninhabited Aerial Vehicles with Articulating Cameras

William W. Whitacre, Mark E. Campbell
2011 Journal of Guidance Control and Dynamics  
., “Bias Compensation for Bearings-Only Pseudolinear Target Track Estimator,” JEEE Transactions on Signal Processing, Vol. 54, No. 1, Jan. 2006, pp. 59-68. doi:10.1109/TSP.2005.861088 Durrant-Whyte, H.  ...  Campbell and Wheeler [7] compensate for these biases with an additional algorithm that fuses a conservative uniform density model for the bias.  ... 
doi:10.2514/1.49059 fatcat:7ihdti43gjc5ze37vmo75bd2yi

Gaussian Target Tracking With Direction-of-Arrival von Mises–Fisher Measurements

Angel F. Garcia-Fernandez, Filip Tronarp, Simo Sarkka
2019 IEEE Transactions on Signal Processing  
UKF (AUKF), and the filters described in [20] : the pseudolinear KF (PLKF), the bias compensated pseudolinear KF (BC-PLKF), and instrumental variable-based KF (IVKF).  ...  The scenario in Section VI-A considers 2-D target tracking with bearings only measurements. The scenario in Section VI-B considers 2-D target tracking with range-bearings measurements.  ... 
doi:10.1109/tsp.2019.2911258 fatcat:upd2bno2uffq3k4fg3wfnbo3cu

2020 Index IEEE Transactions on Signal Processing Vol. 68

2020 IEEE Transactions on Signal Processing  
., One-Step Prediction for Discrete Time-Varying Nonlinear Systems With Unknown Inputs and Correlated Noises; TSP  ...  Hsue, W., TSP 2020 1776-1791 Target tracking A Closed-Form Estimator for Bearings-Only Fusion of Heterogeneous Passive Sensors.  ...  ., +, TSP 2020 3644-3659 Machine bearings A Closed-Form Estimator for Bearings-Only Fusion of Heterogeneous Passive Sensors.  ... 
doi:10.1109/tsp.2021.3055469 fatcat:6uswtuxm5ba6zahdwh5atxhcsy

Table of Contents

2020 IEEE Transactions on Signal Processing  
Vallet 4656 An Algebraic Closed-Form Solution for Bearings-Only Maneuvering Target Motion Analysis From a Nonmaneuvering Platform .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  ...  Eldar 4627 Diffusion Average-Estimate Bias-Compensated LMS Algorithms Over Adaptive Networks Using Noisy Measurements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  ... 
doi:10.1109/tsp.2020.3042287 fatcat:nh7viihaozhd7li3txtadnx5ui

Page 573 of Journal of Guidance, Control, and Dynamics Vol. 34, Issue 2 [page]

Journal of Guidance, Control, and Dynamics  
., “Bias Compensation for Bearings-Only Pseudolinear Target Track Estimator,” JEEE Transactions on Signal Processing, Vol. 54, No. 1, Jan. 2006, pp. 59-68. doi:10.1109/TSP.2005.861088 Durrant-Whyte, H.  ...  ., “Mobile Radar Bias Estimation Using Unknown Location Targets,” /nternational Conference on Information Fusion, Vol. 1, Paris, July 2000.  ... 

2021 Index IEEE Transactions on Instrumentation and Measurement Vol. 70

2021 IEEE Transactions on Instrumentation and Measurement  
Note that the item title is found only under the primary entry in the Author Index.  ...  The Author Index contains the primary entry for each item, listed under the first author's name.  ...  ., +, TIM 2021 2005912 A Recursive Estimator for Pseudolinear Target Motion Analysis Using Mul-tiple Hybrid Sensors.  ... 
doi:10.1109/tim.2022.3156705 fatcat:dmqderzenrcopoyipv3v4vh4ry
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