Derivation of an Approximate Location Estimate in Angle-of-Arrival Based Localization in the Presence of Angle-of-Arrival Estimate Error and Sensor Location Error

2018 ◽  
Author(s):  
Do-Jin An ◽  
Joon-Ho Lee
Keyword(s):  
Location Error ◽  
Sensor Location ◽  
Angle Of Arrival ◽  
Estimate Error ◽  
Approximate Location ◽  
Location Estimate

scholarly journals Performance Analysis of AOA-Based Localization Using the LS Approach: Explicit Expression of Mean-Squared Error

2020 ◽  
Vol 2020 ◽  
pp. 1-22
Author(s):  
Byung-Kwon Son ◽  
Do-Jin An ◽  
Joon-Ho Lee
Keyword(s):  
Explicit Expression ◽  
Mean Squared Error ◽  
Weighted Least Squares ◽  
Localization Algorithm ◽  
Angle Of Arrival ◽  
Squared Error ◽  
Distance Weighted ◽  
The Mean ◽  
Passive Localization ◽  
Location Estimate

In this paper, a passive localization of the emitter using noisy angle-of-arrival (AOA) measurements, called Brown DWLS (Distance Weighted Least Squares) algorithm, is considered. The accuracy of AOA-based localization is quantified by the mean-squared error. Various estimates of the AOA-localization algorithm have been derived (Doğançay and Hmam, 2008). Explicit expression of the location estimate of the previous study is used to get an analytic expression of the mean-squared error (MSE) of one of the various estimates. To validate the derived expression, we compare the MSE from the Monte Carlo simulation with the analytically derived MSE.

scholarly journals Vehicular Localization Enhancement via Consensus

Sensors ◽  
2020 ◽  
Vol 20 (22) ◽  
pp. 6506
Author(s):  
Hong Ki Kim ◽  
Minji Kim ◽  
Sang Hyun Lee
Keyword(s):  
Measurement Errors ◽  
Environmental Changes ◽  
Ground Truth ◽  
Localization Error ◽  
Local Data ◽  
Implementation Techniques ◽  
Estimate Error ◽  
Localization Performance ◽  
Update Rules ◽  
Location Estimate

This paper presents a strategy to cooperatively enhance the vehicular localization in vehicle-to-everything (V2X) networks by exchanges and updates of local data in a consensus-based manner. Where each vehicle in the network can obtain its location estimate despite its possible inaccuracy, the proposed strategy takes advantage of the abundance of the local estimates to improve the overall accuracy. During the execution of the strategy, vehicles exchange each other’s inter-vehicular relationship pertaining to measured distances and angles in order to update their own estimates. The iteration of the update rules leads to averaging out the measurement errors within the network, resulting in all vehicles’ localization error to retain similar magnitudes and orientations with respect to the ground truth locations. Furthermore, the estimate error of the anchor—the vehicle with the most reliable localization performance—is temporarily aggravated through the iteration. Such circumstances are exploited to simultaneously counteract the estimate errors and effectively improve the localization performance. Simulated experiments are conducted in order to observe the nature and its effects of the operations. The outcomes of the experiments and analysis of the protocol suggest that the presented technique successfully enhances the localization performances, while making additional insights regarding performance according to environmental changes and different implementation techniques.

scholarly journals Noniterative Three-Dimensional Location Estimation Using Azimuth and Elevation Measurements at Multiple Locations

2019 ◽  
Vol 2019 ◽  
pp. 1-13
Author(s):  
Ji Woong Paik ◽  
Joon-Ho Lee
Keyword(s):  
Closed Form ◽  
Three Dimensional ◽  
Location Estimation ◽  
Closed Form Expression ◽  
Sensor Location ◽  
Form Expression ◽  
Emitter Location ◽  
Initial Location ◽  
Computationally Intensive ◽  
Location Estimate

Closed-form expression of three-dimensional emitter location estimation using azimuth and elevation measurements at multiple locations is presented in this paper. The three-dimensional location estimate is obtained from three-dimensional sensor locations and the azimuth and elevation measurements at each sensor location. Since the formulation is not iterative, it is not computationally intensive and does not need initial location estimate. Numerical results are presented to show the validity of the proposed scheme.

scholarly journals SAGE-Based Algorithm for Direction-of-Arrival Estimation and Array Calibration

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Kunlai Xiong ◽  
Zheng Liu ◽  
Wenli Jiang
Keyword(s):  
Mutual Coupling ◽  
Direction Of Arrival ◽  
Parameters Estimation ◽  
Location Error ◽  
Sensor Location ◽  
Unified Framework ◽  
Good Convergence ◽  
Multidimensional Search ◽  
Array Calibration ◽  
Perturbation Parameters

Most existing array processing algorithms are very sensitive to model uncertainties caused by the mutual coupling and sensor location error. To mitigate this problem, a novel method for direction-of-arrival (DOA) estimation and array calibration in the case of deterministic signals with unknown waveforms is presented in this paper. The analysis begins with a comprehensive perturbed array output model, and it is effective for various kinds of perturbations, such as mutual coupling and sensor location error. Based on this model, the Space Alternating Generalized Expectation-Maximization (SAGE) algorithm is applied to jointly estimate the DOA and array perturbation parameters, which simplifies the multidimensional search procedure required for finding maximum likelihood (ML) estimates. The proposed method inherits the characteristics of good convergence and high estimation precision of the SAGE algorithm. At the same time, it forms a unified framework for DOA and array perturbation parameters estimation in the presence of mutual coupling and sensor location error. The simulation results demonstrate the effectiveness of the algorithm.

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