Optimized Power Allocation in Nonlinear Sensor Networks via Semidefinite Programming
ABSTRACT This paper presents an efficient technique for power allocation to the sensor nodes in a nonlinear sensor network (NSN). We minimize mean square error of the estimation of a random scalar parameter subject to a constraint on total amount of power consumed by the sensor nodes. This estimation is carried out at fusion center (FC) which receives the local observations from the sensors located at different positions. We convert the optimization problem into a convex one, and then use semidefinite programming to find the global optimal solution. The simulation results show that our approach outperforms the previous work both for the channel with white noise and the one with colored noise. The proposed strategy also gives better results in case of nonlinear model when compared to the strategy of assigning equal power to sensor nodes.
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ABSTRACT: This paper discusses dynamic state estimation for nonlinear measurement model through distributed multisensor network under power constraints. For this scenario, we propose an optimized power allocation strategy based on semidefinite programming, that achieves minimum mean-squared error for the estimate subject to constraints on total transmit power. System nonlinearity is handled effectively with the help of distributed unscented Kalman filtering and linear fractional transformation. Furthermore, advantage of using multiple sensors over a single independent sensor is established through simulation results for tracking a maneuvering target.Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2011, May 22-27, 2011, Prague Congress Center, Prague, Czech Republic; 01/2011
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ABSTRACT: This paper presents a novel technique of allocating optimized power to wireless sensor nodes in a nonlinear measurement model. We consider the problem of distributed estimation of a random vector-valued parameter in an energy-constrained sensor network. Noise-corrupted local nonlinear observations are transmitted by spatially distributed sensor nodes towards fusion center where estimation of the vector parameter is carried out. In order to guarantee reliable communication, we minimize mean square error of this estimate subject to a constraint on total power consumed by the network. This optimization problem is then recast into a semi-definite program (SDP) which guarantees globally optimized values of the required power gains at sensor nodes. Estimation performance of this novel technique is demonstrated through examples of nonlinear models. Furthermore, for linear models the proposed strategy provides better performance when compared with the previous sub-optimial techniques.Signal Processing and Communication Systems (ICSPCS), 2010 4th International Conference on; 01/2011
Conference Paper: Multisensor data fusion in nonlinear Bayesian filtering[Show abstract] [Hide abstract]
ABSTRACT: In this paper, an optimal multisensor data fusion method is proposed to estimate the state of a highly nonlinear dynamic model. Data fusion from spatially distributed sensors is expressed as a semi definite program (SDP) that aims at minimizing mean-squared error (MSE) of the state estimate under total transmit power constraints. Furthermore, a Bayesian filtering technique, based on unscented transformations and linear fractional transformations (LFT), is presented under multisensor framework to implement the SDP. Extensive simulations are performed to justify effectiveness of the proposed multisensor scheme over a single sensor supplied with the same power budget as that of the entire sensor network.Communications and Electronics (ICCE), 2012 Fourth International Conference on; 01/2012