Improved Least Squares Approaches for Differential Received Signal Strength-Based Localization with Unknown Transmit Power

Danaee, M. R.; Behnia, Fereidoon

doi:10.1007/s11277-019-06791-3

Cited by 5 publications

(4 citation statements)

References 41 publications

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“…Similar to (5), the received signal power from the threat source can be written as follows:

P_{i_{threat}} = P_{T_{threat}} + G_{T_{threat}} + G_{R_{UAV} - threat} - FSP L_{i_{threat}}

where

P_{T_{threat}}

G_{T_{threat}}

G_{R_{UAV} - threat}

and

FSP L_{i_{threat}}

are the threat transmit power, threat transmit antenna gain, UAV receiver antenna gain toward the threat source and the FSPL between UAV and threat source, respectively. The path loss between the UAV and an airborne threat source is similar to (6), while between the UAV and a ground‐based threat source can be described by the conventional log‐distance model as follows [36–39]:

{FSPL}^{(dB)} = L_{0} + 10 n \log (\frac{d}{d_{min}}) + L_{fad} + ρ H

where d is the distance between the transmitter and receiver,

L_{0}

is the constant related to the minimum valid distance

d_{\min}

, n is the path loss exponents (

n ≃ 2

) and

L_{fad}

is a parameter for accounting fading phenomenon [35].

ρ

and H are the parameters representing the flight path adjustment of the UAV, which are described in more details in Section 2.3.…”

Section: Background and Problem Descriptionmentioning

confidence: 99%

Low‐complexity framework for GNSS jamming and spoofing detection on moving platforms

Sharifi-Tehrani

Sabahi

Danaee

2020

IET Radar, Sonar & Navigation

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“…Similar to (5), the received signal power from the threat source can be written as follows:

P_{i_{threat}} = P_{T_{threat}} + G_{T_{threat}} + G_{R_{UAV} - threat} - FSP L_{i_{threat}}

where

P_{T_{threat}}

G_{T_{threat}}

G_{R_{UAV} - threat}

and

FSP L_{i_{threat}}

{FSPL}^{(dB)} = L_{0} + 10 n \log (\frac{d}{d_{min}}) + L_{fad} + ρ H

where d is the distance between the transmitter and receiver,

L_{0}

is the constant related to the minimum valid distance

d_{\min}

, n is the path loss exponents (

n ≃ 2

) and

L_{fad}

is a parameter for accounting fading phenomenon [35].

ρ

and H are the parameters representing the flight path adjustment of the UAV, which are described in more details in Section 2.3.…”

Section: Background and Problem Descriptionmentioning

confidence: 99%

Low‐complexity framework for GNSS jamming and spoofing detection on moving platforms

Sharifi-Tehrani

Sabahi

Danaee

2020

IET Radar, Sonar & Navigation

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“…Range-based localisation first performs two-point range and then uses three-sided, triangular and other geometric characteristics to locate nodes. Specific range methods include time of arrival (TOA) [8], time difference of arrival (TDOA) [9], received signal strength indication (RSSI) [10] and angle of arrival (AOA) [11]. Non-range-based localisation generally uses the received signal directly to determine the This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.…”

Section: Introductionmentioning

confidence: 99%

An improved underwater TDOA/AOA joint localisation algorithm

Jiang

Zhang

2021

IET Communications

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To solve the problem of sound source localisation in underwater sensor networks, this paper constructs a pseudo-linear equation system of time difference of arrival and angle of arrival (TDOA/AOA) and then uses the weighted least squares algorithm to estimate the target position. This paper proposes a two-stage weighted least squares algorithm that uses target position error. First, the adapted TDOA equation and the existing AOA equation are combined into the first-stage weighted least squares algorithm, which improves the estimation accuracy of the first stage compared with the traditional algorithm. Second, this paper uses the target position error in the second stage to derive a new TDOA/AOA equation. Finally, the target position calculated in the previous stage is adjusted by the solved target position error. The performance of the proposed algorithm is verified by comparison with the Cramer-Rao lower bound. Simulation results show that the proposed algorithm still has good localisation performance even under high-angle noise.

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“…Another approach to avoid getting stuck in local solutions is to use LS methods which are very popular in localization problem, [19,20,[22][23][24]. The LS methods have closed-form solutions and involve only light computation, compared to the SDP or SOCP methods.…”

Section: Introductionmentioning

confidence: 99%

“…One of the possible solutions which has been offered yet for recovering such an information loss is to apply two successive LS filters where the first one obtains a coarse position coordinates and the second exploits the known relation between the parameter estimates for improving the position estimate [19,20,24].…”

Section: Introductionmentioning

confidence: 99%

One‐to‐one non‐linear transformation for RSS‐based localization with unknown transmit power

Danaee

2021

IET Communications

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For most of the positioning algorithms, based on the received signal strength, the objective function of the estimator is linearized so that the conventional least square algorithm can be executed. This would necessitate using a data model containing many noisy elements which implicitly violates the least squares assumptions. To avoid this violation, an optimized data modelling would have to be set up as shown in this paper. The authors then trivially provide the solution of this new least square problem which is still a primary coarse position estimation because of ignoring the non‐linear relationships existing among parameters of the estimation. To extract quantitative information from these non‐linear relationships, the authors also propose two new signal space points for using Taylor‐series expansion as effectively as possible in accordance of the new data model. The proposed algorithms work without any knowledge about the transmit power. The performance evaluation of the proposed idea indicates that the new suggested estimators perform significantly better than the other existing linear position estimators for a large range of noise variance and path‐loss exponent values.

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Improved Least Squares Approaches for Differential Received Signal Strength-Based Localization with Unknown Transmit Power

Cited by 5 publications

References 41 publications

Low‐complexity framework for GNSS jamming and spoofing detection on moving platforms

Low‐complexity framework for GNSS jamming and spoofing detection on moving platforms

An improved underwater TDOA/AOA joint localisation algorithm

One‐to‐one non‐linear transformation for RSS‐based localization with unknown transmit power

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