Received signal strength based positioning for multiple nodes in wireless sensor networks

Lin, Lanxin; So, Hing Cheung; Chan, Y.T.

doi:10.1016/j.dsp.2013.10.017

Cited by 27 publications

(17 citation statements)

References 19 publications

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“…In addition, reference anchor optimization using a technique based on the minimization of the theoretical mean square error is also proposed to further improve performance of WLS algorithm. Twostep weighted least squares (TSWLS) localization algorithm is proposed in [17], and the mean square error of the proposed algorithm is analyzed under the zero mean Gauss white noise interference. TSWLS algorithm considerably improves localization system performance.…”

Section: Introductionmentioning

confidence: 99%

“…The solution from the equation in(17) for the estimated location correspond to the condition of α = 0 is given byp i = L. Case 2: Under the conditions of α = 0, = 0, β ≤ 0, or equivalent as γ = 0.5, the solution of the equation in(17) for the estimated location is then given byp i = L + ϕθ . Case 3: When the conditions are α = 0, > 0, β ≤ 0, or equivalently written as γ < 0.5, then we would obtain two solutions of the estimated location.…”

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confidence: 99%

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A Computationally Efficient Received Signal Strength Based Localization Algorithm in Closed-Form for Wireless Sensor Network

Zhang

Xiong

Bao-guo

2017

Neural Process Lett

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Ranging error is known to degrade significantly the target node localization accuracy. This paper investigates the use of computationally efficient positioning solution of least square (LS) in closed-form, to reduce localization accuracy loss caused by ranging error. For range-based node localization, the LS solution based on least square criterion has been confirmed to exhibit capability of optimum estimation but extensively achieve at a very complex calculation. In this paper we consider the problem how to acquire such LS solution provided with estimation performance at low complex calculation. In this paper, we use the Gauss noise model and use the weighted least squares criterion and the effective calculation method to solve the linearized equation derived from the RSS measurement, and put forward a new approach to estimate the performance of the target node location estimation. Based on the Fisher information matrix, the Cramér-Rao lower bound of target position estimation is derived based on received signal strength. We obviously indicate that the proposed algorithm can approximately achieve the LS solution in estimation performance at a markedly low complex calculation. Simulations are performed to show the improvement of the proposed algorithm.

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Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

A Computationally Efficient Received Signal Strength Based Localization Algorithm in Closed-Form for Wireless Sensor Network

Zhang

Xiong

Bao-guo

2017

Neural Process Lett

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“…A localization scheme tries to localize the source node using the ranging information extracted from the signaling between anchor node and source node [9], [10]. Most of the accurate localization techniques are based on the ranging information by using the techniques such as, time of arrival (TOA) [11], [12], time difference of The associate editor coordinating the review of this manuscript and approving it for publication was Ying Li. arrival (TDOA) [13], [14], received signal strength (RSS) [15]- [17], and angle of arrival (AOA) [18], [19].…”

Section: Introductionmentioning

confidence: 99%

SDP Relaxation Methods for RSS/AOA-Based Localization in Sensor Networks

2020

IEEE Access

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With the fast development of new array technology and intelligent antenna, it is easier to obtain angle of arrival (AOA) measurements. Hybrid received signal strength (RSS) and AOA measurement techniques are proposed for the position computing in sensor networks. By converting the measurement equations and relaxing the optimization function, range-based square semidefinite programming (RLS-SDP) and squared range-based square semidefinite programming (SRLS-SDP) algorithms are put forward to obtain the source position estimate by considering the transmit power to be known or unknown. The proposed RLS-SDP and SRLS-SDP algorithms provide accurate solution to the source position estimate and avoid the initialization process of numerical calculation. The simulations show that the proposed RLS-SDP and SRLS-SDP algorithms perform better than the linear estimator and provide the accuracy performance which is very close to the Cramér-Rao Lower Bound (CRLB) of position estimation. The proposed SRLS-SDP algorithm shows its advantages in the computational complexity compared with the RLS-SDP, since the complexity of SRLS-SDP is independent of the number of anchor nodes. INDEX TERMS Localization, received signal strength, angle of arrival, semidefinite programming.

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“…As a special case of transmit power estimation, node positioning based on received signal strength observations has been extensively studied in [6][7][8], where the transmit power itself is assumed known. In [9], transmit power estimation was studied by using an ad hoc optimization method.…”

Section: Introductionmentioning

confidence: 99%

Performance Analysis of Maximum Likelihood Estimation for Transmit Power Based on Signal Strength Model

Peng

2018

JSAN

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We study theoretical performance of Maximum Likelihood (ML) estimation for transmit power of a primary node in a wireless network with cooperative receiver nodes. The condition that the consistence of an ML estimation via cooperative sensing can be guaranteed is firstly defined. Theoretical analysis is conducted on the feasibility of the consistence condition regarding an ML function generated by independent yet not identically distributed random variables. Numerical experiments justify our theoretical discoveries.

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Received signal strength based positioning for multiple nodes in wireless sensor networks

Cited by 27 publications

References 19 publications

A Computationally Efficient Received Signal Strength Based Localization Algorithm in Closed-Form for Wireless Sensor Network

A Computationally Efficient Received Signal Strength Based Localization Algorithm in Closed-Form for Wireless Sensor Network

SDP Relaxation Methods for RSS/AOA-Based Localization in Sensor Networks

Performance Analysis of Maximum Likelihood Estimation for Transmit Power Based on Signal Strength Model

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