Abstract
The distribution of ranging errors of time of arrival techniques fails to satisfy zero means and equal variances. It is one of the major causations of position error of least square–based localization algorithm. The optimization of time of arrival ranging is defined as a nonlinear programming problem. Then, time of arrival ranging error model and geometric constraints are used to define the initial values, objective functions, and constraints of nonlinear programming, as well as to detect line of sight and nonline of sight. A three-dimensional localization algorithm of an indoor time of arrival–based positioning is proposed based on least square and the optimization algorithm. The performance of the ranging and localization accuracies is evaluated by simulation and field testing. Results show that the optimized ranging error successfully satisfies zero mean value and equal variances. Furthermore, the ranging and localization accuracies are significantly improved.
Highlights
Location information is one of the most important attributes of an object
D^ subÀmean is used as the initial value of the nonlinear programming to solve the problem on the ranging error that does not satisfy zero expectation and equal variances. mLOS and mNLOS, which are used as the parameters of the algorithm, can be obtained by statistical line of sight (LOS)/nonline of sight (NLOS) ranging experiment based on a large number of time of arrival (TOA)
In the realization of localization algorithm, LOS/NLOS recognition algorithm based on received signal strength (RSS), which is proposed in Wann and Chin,[4] and the sequential quadratic programming (SQP) are used to implement the LOS/NLOS detection (LND) and nonlinear programming, respectively.[26]
Summary
Location information is one of the most important attributes of an object. Many important areas of human civilization, such as transportation, military, logistics, security, resource exploration, and natural environment protection, have a strong demand for the exact location information of the target. TOA localization.[20] The assumptions of the mean value and equal variances must be satisfied to apply LS.[21] in the realistic indoor environment, the distribution of distance measurement error cannot satisfy zero mean value and equal variances because of the effect of multipath and NLOS.[22] the LS algorithm cannot obtain the optimal estimation result. An optimization algorithm based on nonlinear programming (NLP) is presented to optimize the distance estimation and tune the ranging error distribution to satisfy zero mean value and equal variance, thereby solving the mismatch problem. The analysis in section ‘‘LS and its premise hypothesis’’ shows that the LS localization algorithm is the best unbiased estimation only if the TOA ranging errors are zero mean and equal variance. D^ subÀmean is used as the initial value of the nonlinear programming to solve the problem on the ranging error that does not satisfy zero expectation and equal variances. mLOS and mNLOS, which are used as the parameters of the algorithm, can be obtained by statistical LOS/NLOS ranging experiment based on a large number of TOA measurement in the field
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: International Journal of Distributed Sensor Networks
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
