Conference Paper

Optimized Power Allocation in Nonlinear Sensor Networks via Semidefinite Programming

Sch. of Electr. Eng. & Telecom, Univ. of New South Wales, Sydney, NSW, Australia
DOI: 10.1109/VETECF.2010.5594512 Conference: Vehicular Technology Conference Fall (VTC 2010-Fall), 2010 IEEE 72nd
Source: IEEE Xplore


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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    • "This problem is subsequently cast as a standard semidefnite program (SDP) of tractable optimization which ensures global optimal solution. In our previous paper, we presented MMSE estimation for static measurement model in nonlinear sensor network [10]. In short, the key contributions of the paper are two-fold: • It applies optimized power allocation strategy for dynamic state estimation under a nonlinear measurement model. "
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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.
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