Browsing by Subject "Time-of-arrival"

Now showing 1 - 10 of 10

Open Access
A distributed positioning algorithm for cooperative active and passive sensors
(IEEE, 2010) Gholami, M.R.; Gezici, Sinan; Rydström, M.; Ström, E.G.
The problem of positioning a target node is studied for wireless sensor networks with cooperative active and passive sensors. Two-way time-of-arrival and time-difference-of-arrival measurements made by both active and passive nodes are used to estimate the position of the target node. A maximum likelihood estimator (MLE) can be employed to solve the problem. Due to the nonlinear nature of the cost function in the MLE, an iterative search might converge to local minima which often results in large estimation errors. To avoid this drawback, we instead formulate the problem of positioning as finding the intersection of a number of convex sets derived from measurements. To obtain this intersection, we apply the projection onto convex sets approach, which is robust and can be implemented in a distributed manner. Simulations are performed to compare the performance of the MLE and the proposed method. ©2010 IEEE.
Open Access
Enhanced position estimation via node cooperation
(IEEE, 2010) Sahinoglu, Z.; Gezici, Sinan
Two-way time-of-arrival (TW-ToA) is a widely used ranging protocol that can provide the distance between two devices without time synchronization. One drawback of the TW-ToA is poor positioning accuracy in the absence of a sufficient number of reference ranging devices. Also, for a self-positioning system with a limited battery life, it might be necessary to limit the number of transmissions while satisfying accuracy constraints. In this paper, a cooperative positioning protocol [1] is studied, which can improve positioning accuracy compared to the conventional TW-ToA based positioning systems and also facilitate positioning with fewer packet transmissions; hence, it can prolong battery life on average. The maximum likelihood estimator is obtained for the cooperative technique and the limits on the positioning accuracy are quantified in terms of the Cramer-Rao lower bound (CRLB). Simulation results are provided in order to show performance improvements. ©2010 IEEE.
Open Access
Fundamental limits and improved algorithms for linear least-squares wireless position estimation
(John Wiley & Sons, 2010-09-22) Guvenc, I.; Gezici, Sinan; Sahinoglu Z.
In this paper, theoretical lower bounds on performance of linear least-squares (LLS) position estimators are obtained, and performance differences between LLS and nonlinear least-squares (NLS) position estimators are quantified. In addition, two techniques are proposed in order to improve the performance of the LLS approach. First, a reference selection algorithm is proposed to optimally select the measurement that is used for linearizing the other measurements in an LLS estimator. Then, a maximum likelihood approach is proposed, which takes correlations between different measurements into account in order to reduce average position estimation errors. Simulations are performed to evaluate the theoretical limits and to compare performance of various LLS estimators.
Open Access
Hybrid TW-TOA/TDOA positioning algorithms for cooperative wireless networks
(IEEE, 2011) Gholami, M.R.; Gezici, Sinan; Ström, E.G.; Rydström, M.
The problem of positioning an unknown target is studied for a cooperative wireless sensor network using hybrid two-way time-of-arrival and time-difference-of-arrival measurements. A maximum likelihood estimator (MLE) can be employed to solve the problem. Due to the non-linear nature of the cost function in the MLE, a numerical method, e.g., an iterative search algorithm with a good initial point, should be taken to accurately estimate the target. To avoid drawbacks in a numerical method, we instead linearize the measurements and obtain a new two-step estimator that has a closed-form solution in each step. Simulation results confirm that the proposed linear estimator can attain Cramer-Rao lower bound for sufficiently high SNR. © 2011 IEEE.
Open Access
Improved position estimation using hybrid TW-TOA and TDOA in cooperative networks
(Institute of Electrical and Electronics Engineers, 2012-04-13) Gholami, M. R.; Gezici, Sinan; Ström, E. G.
This paper addresses the problem of positioning multiple target nodes in a cooperative wireless sensor network in the presence of unknown turn-around times. In this type of cooperative networks, two different reference sensors, namely, primary and secondary nodes, measure two-way time-of-arrival (TW-TOA) and time-difference-of-arrival (TDOA), respectively. Motivated by the role of secondary nodes, we extend the role of target nodes such that they can be considered as pseudo secondary nodes. By modeling turn-around times as nuisance parameters, we derive a maximum likelihood estimator (MLE) that poses a difficult global optimization problem due to its nonconvex objective function. To avoid drawbacks in solving the MLE, we linearize the measurements using two different techniques, namely, nonlinear processing and first-order Taylor series, and obtain linear models based on unknown parameters. The proposed linear estimator is implemented in three steps. In the first step, a coarse position estimate is obtained for each target node, and it is refined through steps two and three. To evaluate the performance of different methods, we derive the Cramér-Rao lower bound (CRLB). Simulation results show that the cooperation technique provides considerable improvements in positioning accuracy compared to the noncooperative scenario, especially for low signal-to-noise-ratios.
Open Access
Positioning algorithms for cooperative networks in the presence of an unknown turn-around time
(IEEE, 2011) Gholami, M.R.; Gezici, Sinan; Ström, E.G.; Rydström, M.
This paper addresses the problem of single node positioning in cooperative network using hybrid two-way time-of-arrival and time-difference-of-arrival where, the turn-around time at the target node is unknown. Considering the turn-around time as a nuisance parameter, the derived maximum likelihood estimator (MLE) brings a difficult global optimization problem due to local minima in the cost function of the MLE. To avoid drawbacks in solving the MLE, we obtain a linear two-step estimator using non-linear pre-processing which is algebraic and closed-form in each step. To compare different methods, Cramér-Rao lower bound (CRLB) is derived. Simulation results confirm that the proposed linear estimator attains the CRLB for sufficiently high signal-to-noise ratios. © 2011 IEEE.
Open Access
Statistics of the MLE and Approximate Upper and Lower Bounds-Part 1: Application to TOA Estimation
(IEEE, 2014-08) Mallat, A.; Gezici, Sinan; Dardari, D.; Craeye, C.; Vandendorpe, L.
In nonlinear deterministic parameter estimation, the maximum likelihood estimator (MLE) is unable to attain the Cramer-Rao lower bound at low and medium signal-to-noise ratios (SNR) due the threshold and ambiguity phenomena. In order to evaluate the achieved mean-squared-error (MSE) at those SNR levels, we propose new MSE approximations (MSEA) and an approximate upper bound by using the method of interva l estimation (MIE). The mean and the distribution of the MLE ar e approximated as well. The MIE consists in splitting the a priori domain of the unknown parameter into intervals and computin g the statistics of the estimator in each interval. Also, we derive an approximate lower bound (ALB) based on the Taylor series expansion of noise and an ALB family by employing the binary detection principle. The accurateness of the proposed MSEAs and the tightness of the derived approximate bounds 1 are validated by considering the example of time-of-arrival estimation.
Open Access
Statistics of the MLE and Approximate Upper and Lower Bounds-Part 2: Threshold Computation and Optimal Signal Design
(2014) Mallat, A.; Gezici, Sinan; Dardari, D.; Vandendorpe, L.
Threshold and ambiguity phenomena are studied in Part I of this paper where approximations for the mean-squared error (MSE) of the maximum-likelihood estimator are proposed using the method of interval estimation (MIE), and where approximate upper and lower bounds are derived. In this part, we consider time-of-arrival estimation and we employ the MIE to derive closed-form expressions of the begin-ambiguity, end-ambiguity and asymptotic signal-to-noise ratio (SNR) thresholds with respect to some features of the transmitted signal. Both baseband and passband pulses are considered. We prove that the begin-ambiguity threshold depends only on the shape of the envelope of the ACR, whereas the end-ambiguity and asymptotic thresholds only on the shape of the ACR. We exploit the results on the begin-ambiguity and asymptotic thresholds to optimize, with respect to the available SNR, the pulse that achieves the minimum attainable MSE. The results of this paper are valid for various estimation problems.
Unknown
Statistics of the MLE and approximate upper and lower bounds-Part I: Application to TOA estimation
(Institute of Electrical and Electronics Engineers Inc., 2014) Mallat, A.; Gezici, Sinan; Dardari, D.; Craeye, C.; Vandendorpe, L.
In nonlinear deterministic parameter estimation, the maximum likelihood estimator (MLE) is unable to attain the Cramér-Rao lower bound at low and medium signal-to-noise ratios (SNRs) due the threshold and ambiguity phenomena. In order to evaluate the achieved mean-squared error (MSE) at those SNR levels, we propose new MSE approximations (MSEA) and an approximate upper bound by using the method of interval estimation (MIE). The mean and the distribution of the MLE are approximated as well. The MIE consists in splitting the a priori domain of the unknown parameter into intervals and computing the statistics of the estimator in each interval. Also, we derive an approximate lower bound (ALB) based on the Taylor series expansion of noise and an ALB family by employing the binary detection principle. The accuracy of the proposed MSEAs and the tightness of the derived approximate bounds are validated by considering the example of time-of-arrival estimation.
Unknown
Statistics of the MLE and approximate upper and lower bounds-part II: Threshold computation and optimal pulse design for TOA estimation
(Institute of Electrical and Electronics Engineers Inc., 2014) Mallat, A.; Gezici, Sinan; Dardari, D.; Vandendorpe, L.
Threshold and ambiguity phenomena are studied in Part I of this paper where approximations for the mean-squared error (MSE) of the maximum-likelihood estimator are proposed using the method of interval estimation (MIE), and where approximate upper and lower bounds are derived. In this part, we consider time-of-arrival estimation and we employ the MIE to derive closed-form expressions of the begin-ambiguity, end-ambiguity and asymptotic signal-to-noise ratio (SNR) thresholds with respect to some features of the transmitted signal. Both baseband and passband pulses are considered. We prove that the begin-ambiguity threshold depends only on the shape of the envelope of the ACR, whereas the end-ambiguity and asymptotic thresholds only on the shape of the ACR. We exploit the results on the begin-ambiguity and asymptotic thresholds to optimize, with respect to the available SNR, the pulse that achieves the minimum attainable MSE. The results of this paper are valid for various estimation problems.