Bayesian Cramer-Rao Bound for Mobile Terminal Tracking in Mixed LOS/NLOS Environments

Fritsche, Carsten; Klein, Anja; Gustafsson, Fredrik

doi:10.1109/wcl.2013.032013.130073

Cited by 9 publications

(3 citation statements)

References 11 publications

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“…However, this bound can only be practical if there are enough LOS measurements for unambiguous localization, hence, for low number of LOS, i.e., less than two it can not be useful. Even though the posterior Cramer-Rao bound (PCRB) on positioning RMSE has been derived approximately in [44], [45], these derivations are based on the assumption that the NLOS bias has a Gaussian distribution with known mean and variance. Evaluating this PCRB for other NLOS distributions such as exponential is even more challenging.…”

Section: Simulation Resultsmentioning

confidence: 99%

Mobile Localization in Non-Line-of-Sight Using Constrained Square-Root Unscented Kalman Filter

Yousefi

Chang

Champagne

2015

IEEE Trans. Veh. Technol.

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Abstract-Localization and tracking of a mobile node (MN) in non-line-of-sight (NLOS) scenarios, based on time of arrival (TOA) measurements, is considered in this work. To this end, we develop a constrained form of square root unscented Kalman filter (SRUKF), where the sigma points of the unscented transformation are projected onto the feasible region by solving constrained optimization problems. The feasible region is the intersection of several discs formed by the NLOS measurements. We show how we can reduce the size of the optimization problem and formulate it as a convex quadratically constrained quadratic program (QCQP), which depends on the Cholesky factor of the a posteriori error covariance matrix of SRUKF. As a result of these modifications, the proposed constrained SRUKF (CSRUKF) is more efficient and has better numerical stability compared to the constrained UKF. Through simulations, we also show that the CSRUKF achieves a smaller localization error compared to other techniques and that its performance is robust under different NLOS conditions.

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Section: Simulation Resultsmentioning

confidence: 99%

Mobile Localization in Non-Line-of-Sight Using Constrained Square-Root Unscented Kalman Filter

Yousefi

Chang

Champagne

2015

IEEE Trans. Veh. Technol.

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“…The received signal is oversampled by 4. The simulation results are compared with MCRB [18][19][20], with N g = 3 and K = 30. σ versus the signal-to-noise ratio (SNR) in different iterations are shown in Figs. 2and 3, respectively.…”

Section: Simulation Performance Of the Algorithmmentioning

confidence: 99%

Joint Blind Parameter Estimation of Non-cooperative High-Order Modulated PCMA Signals

2018

KSII TIIS

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A joint blind parameter estimation algorithm based on minimum channel stability function aimed at the non-cooperative high-order modulated paired carrier multiple access (PCMA) signals is proposed. The method, which uses hierarchical search to estimate time delay, amplitude and frequency offset and the estimation of phase offset, including finite ambiguity, is presented simultaneously based on the derivation of the channel stability function. In this work, the structure of hierarchical iterative processing is used to enhance the performance of the algorithm, and the improved algorithm is used to reduce complexity. Compared with existing data-aided algorithms, this algorithm does not require a priori information. Therefore, it has significant advantage in solving the problem of blind parameter estimation of non-cooperative high-order modulated PCMA signals. Simulation results show the performance of the proposed algorithm is similar to the modified Cramer-Rao bound (MCRB) when the signal-to-noise ratio is larger than 16 dB. The simulation results also verify the practicality of the proposed algorithm.

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“…Under the timing synchronisation accomplished by the signal model, many achievements have been obtained for the CRB of frequency offset and carrier phase estimations, as shown in [14–18]. Moreover, CRB of joint time delay and frequency estimation of sinusoidal signals is provided in [19], and Bayesian CRBs for parameter estimation of single‐source signals are derived as well in [20–22].…”

Section: Introductionmentioning

confidence: 99%

Modified Cramer–Rao bound of parameter estimation for single‐channel mixtures of adjacent‐frequency signals

Yong

Peng

Zhang

2016

IET signal process.

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Bayesian Cramer-Rao Bound for Mobile Terminal Tracking in Mixed LOS/NLOS Environments

Cited by 9 publications

References 11 publications

Mobile Localization in Non-Line-of-Sight Using Constrained Square-Root Unscented Kalman Filter

Mobile Localization in Non-Line-of-Sight Using Constrained Square-Root Unscented Kalman Filter

Joint Blind Parameter Estimation of Non-cooperative High-Order Modulated PCMA Signals

Modified Cramer–Rao bound of parameter estimation for single‐channel mixtures of adjacent‐frequency signals

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