Statistical Models and ML Positioning Using Received Signal Powers in Sensor Networks

Tanikawara, Makoto; Ohba, Kohei; Kubo, Yukihiro; Sugimoto, S.

doi:10.5687/sss.2011.184

Cited by 2 publications

(3 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Proposition 1: Consider system (7) and assume that (K(t) + H(t))1 = 1 (9) and that there exists 0 ≤ γ 0 < 1…”

Section: Convergence Of the Centralised Estimation Errormentioning

confidence: 99%

“…A sensor network (SN) is a network of autonomous devices that can sense their environment, make computations and communicate with neighbouring devices [1][2][3]. SNs and in particular wireless SNs (WSNs), have a growing domain of application in areas such as environmental monitoring, industrial automation, intelligent buildings, search and surveillance and automotive applications [4][5][6][7][8][9][10][11]. The characteristics of SNs motivate the development of new classes of distributed estimation and control algorithms which explore these systems limited power, computing and communication capabilities.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Improved distributed estimation method for environmental physical variables in static sensor networks

Mahmoud

Khalid

Sabih

2013

IET wirel. sens. syst.

View full text Add to dashboard Cite

In this study, an improved distributed estimation scheme for static sensor networks is developed. The scheme is developed for environmental time-variant physical variables. The main contribution of this work is that the algorithm in Saber and Murray (2005), Carli et al. (2009) and Xiao et al. (2004) has been extended, and a filter has been designed with weights, such that the variance of the estimation errors is minimised, thereby improving the filter design considerably and characterising the performance limit of the filter, and thereby tracking a time-varying signal. Moreover, certain parameter optimisation is alleviated with the application of a particular finite impulse response filter. Simulation results are showing the effectiveness of the developed estimation algorithm.

show abstract

“…Proposition 1: Consider system (7) and assume that (K(t) + H(t))1 = 1 (9) and that there exists 0 ≤ γ 0 < 1…”

Section: Convergence Of the Centralised Estimation Errormentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Improved distributed estimation method for environmental physical variables in static sensor networks

Mahmoud

Khalid

Sabih

2013

IET wirel. sens. syst.

View full text Add to dashboard Cite

show abstract

“…Significant attention have been paid to sensor networks in recent years owing to its huge potential applications in various areas such as, environmental monitoring, information collection, intelligent buildings, wireless networks, and industrial automation, see for example, [4], [5], [10], [15] and the references therein. As an important issue in the research area of sensor networks, the distributed filtering or estimation has received increasing interests recently.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy filter design for discrete-time delayed systems with distributed probabilistic sensor faults

Shi

et al. 2013

2013 American Control Conference

View full text Add to dashboard Cite

In this paper, the problem of distributed fuzzy filter design has been solved for T-S fuzzy systems with timevarying delays and multiple probabilistic packet losses. Our attention is paid to designing the distributed fuzzy filters to guarantee the filtering error dynamic system to be meansquare asymptotically stable with an average H∞ performance. Sufficient conditions for the obtained filtering error dynamic system are proposed by applying a comparison model and the scaled small gain theorem. Based on the measurements and estimates of the system states for each sensor and its neighbors, the solution of the parameters of the distributed fuzzy filters is characterized in terms of the feasibility of a convex optimization problem.

show abstract

Statistical Models and ML Positioning Using Received Signal Powers in Sensor Networks

Cited by 2 publications

References 5 publications

Improved distributed estimation method for environmental physical variables in static sensor networks

Improved distributed estimation method for environmental physical variables in static sensor networks

Fuzzy filter design for discrete-time delayed systems with distributed probabilistic sensor faults

Contact Info

Product

Resources

About