On the Hybrid TOA/RSS Range Estimation in Wireless Sensor Networks

Coluccia, Angelo; Fascista, Alessio

doi:10.1109/twc.2017.2766628

Cited by 101 publications

(99 citation statements)

References 44 publications

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“…In the proposed multi-beam WFRFT-DM schemes, the locations of Bobs are assumed to be prior known for Alice. Practically, the locations of Bobs can be obtained by jointly using direction of arrival (DOA) estimation algorithm such as [46] and range estimation algorithm such as [47].…”

Section: Robustnessmentioning

confidence: 99%

WFRFT-Aided Power-Efficient Multi-Beam Directional Modulation Schemes Based on Frequency Diverse Array

Cheng

Fusco

Zhu

et al. 2019

IEEE Trans. Wireless Commun.

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The artificial noise (AN) aided multi-beam directional modulation (DM) technology is capable of wireless physical layer secure (PLS) transmissions for multiple desired receivers in free space. The application of AN, however, makes it less powerefficient for such a DM system. To address this problem, the weighted fractional Fourier transform (WFRFT) technology is employed in this paper to achieve power-efficient multi-beam DM transmissions. Specifically, a power-efficient multi-beam WFRFT-DM scheme with cooperative receivers and a power-efficient multi-beam WFRFT-DM scheme with independent receivers are proposed based on frequency diverse array (FDA), respectively. The bit error rate (BER), secrecy rate, and robustness of the proposed multi-beam WFRFT-DM schemes are analyzed. Simulations demonstrate that 1) the proposed multi-beam WFRFT-DM schemes are more power-efficient than the conventional multibeam AN-DM scheme; 2) the transmission security can also be guaranteed even if the eavesdroppers are located close to or the same as the desired receivers; and 3) the proposed multi-beam WFRFT-DM schemes are capable of independent transmissions for different desired receivers with different modulations.Index Terms-Physical layer security (PLS), directional modulation (DM), frequency diverse array (FDA), WFRFT, powerefficient, multi-beam.

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Section: Robustnessmentioning

confidence: 99%

WFRFT-Aided Power-Efficient Multi-Beam Directional Modulation Schemes Based on Frequency Diverse Array

Cheng

Fusco

Zhu

et al. 2019

IEEE Trans. Wireless Commun.

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“…Based on different type of measurements, the common localization methods estimate the source position using time of arrival (TOA), time difference of arrival (TDOA), angle of arrival (AOA), Doppler frequency, Fingerprint and received signal strength (RSS) [5][6][7][8][9][10][11][12][13]. To improving the accuracy further, a series of hybrid method is developed by combining several kinds of measurements [14][15][16]. Since TOA localization has the precision advantage in position estimate, particularly in indoor environments [17], it attracts much interest in the netted mono-static radar, multi-static radar and distributed multi-input multi-output (MIMO) system.…”

Section: Introductionmentioning

confidence: 99%

Refinement of TOA Localization with Sensor Position Uncertainty in Closed-Form

Gan¹,

Cong²,

Sun

2020

Sensors

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The subject of localization has received great deal attention in the past decades. Although it is perhaps a well-studied problem, there is still room for improvement. Traditional localization methods usually assume the number of sensors is sufficient for providing desired performance. However, this assumption is not always satisfied in practice. This paper studies the time of arrival (TOA)-based source positioning in the presence of sensor position errors. An error refined solution is developed for reducing the mean-squared-error (MSE) and bias in small sensor network (the number of sensors is fewer) when the noise or error level is relatively large. The MSE performance is analyzed theoretically and validated by simulations. Analytical and numerical results show the proposed method attains the Cramér-Rao lower bound (CRLB). It outperforms the existing closed-form methods with slightly raising computation complexity, especially in the larger noise/error case.

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“…The main objective of this paper is to investigate how a dynamic estimation of the TOA and RSS model parameters will impact the ultimate position estimation performance of joint TOA/RSS range-based localization. To this aim, we extended our previous work [44] by considering, in addition to the hybrid TOA/RSS range estimator proposed therein, a preliminary self-calibration stage and a final position estimation stage. In particular, differently from [44] where all the parameters are assumed to be known, in this paper, we consider the more realistic case where the model parameters are estimated "on the field" according to a self-calibration procedure (described in Section 3.1).…”

Section: Introductionmentioning

confidence: 99%

Hybrid TOA/RSS Range-Based Localization with Self-Calibration in Asynchronous Wireless Networks

Coluccia

Fascista

2019

JSAN

Self Cite

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The paper addresses the problem of localization based on hybrid received signal strength (RSS) and time of arrival (TOA) measurements, in the presence of synchronization errors among all the nodes in a wireless network, and assuming all parameters are unknown. In most existing schemes, in fact, knowledge of the model parameters is postulated to reduce the high dimensionality of the cost functions involved in the position estimation process. However, such parameters depend on the operational wireless context, and change over time due to the presence of dynamic obstacles and other modification of the environment. Therefore, they should be adaptively estimated “on the field”, with a procedure that must be as simple as possible in order to suit multiple real-time re-calibrations, even in low-cost applications, without requiring human intervention. Unfortunately, the joint maximum likelihood (ML) position estimator for this problem does not admit a closed-form solution, and numerical optimization is practically unfeasible due to the large number of nuisance parameters. To circumvent such issues, a novel two-step algorithm with reduced complexity is proposed: A first calibration phase exploits nodes in known positions to estimate the unknown RSS and TOA model parameters; then, in a second localization step, an hybrid TOA/RSS range estimator is combined with an iterative least-squares procedure to finally estimate the unknown target position. The results show that the proposed hybrid TOA/RSS localization approach outperformed state-of-the-art competitors and, remarkably, achieved almost the same accuracy of the joint ML benchmark but with a significantly lower computational cost.

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On the Hybrid TOA/RSS Range Estimation in Wireless Sensor Networks

Cited by 101 publications

References 44 publications

WFRFT-Aided Power-Efficient Multi-Beam Directional Modulation Schemes Based on Frequency Diverse Array

WFRFT-Aided Power-Efficient Multi-Beam Directional Modulation Schemes Based on Frequency Diverse Array

Refinement of TOA Localization with Sensor Position Uncertainty in Closed-Form

Hybrid TOA/RSS Range-Based Localization with Self-Calibration in Asynchronous Wireless Networks

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