56,823 research outputs found
Sensor Selection Based on Generalized Information Gain for Target Tracking in Large Sensor Networks
In this paper, sensor selection problems for target tracking in large sensor
networks with linear equality or inequality constraints are considered. First,
we derive an equivalent Kalman filter for sensor selection, i.e., generalized
information filter. Then, under a regularity condition, we prove that the
multistage look-ahead policy that minimizes either the final or the average
estimation error covariances of next multiple time steps is equivalent to a
myopic sensor selection policy that maximizes the trace of the generalized
information gain at each time step. Moreover, when the measurement noises are
uncorrelated between sensors, the optimal solution can be obtained analytically
for sensor selection when constraints are temporally separable. When
constraints are temporally inseparable, sensor selections can be obtained by
approximately solving a linear programming problem so that the sensor selection
problem for a large sensor network can be dealt with quickly. Although there is
no guarantee that the gap between the performance of the chosen subset and the
performance bound is always small, numerical examples suggest that the
algorithm is near-optimal in many cases. Finally, when the measurement noises
are correlated between sensors, the sensor selection problem with temporally
inseparable constraints can be relaxed to a Boolean quadratic programming
problem which can be efficiently solved by a Gaussian randomization procedure
along with solving a semi-definite programming problem. Numerical examples show
that the proposed method is much better than the method that ignores dependence
of noises.Comment: 38 pages, 14 figures, submitted to Journa
Estimation in Phase-Shift and Forward Wireless Sensor Networks
We consider a network of single-antenna sensors that observe an unknown
deterministic parameter. Each sensor applies a phase shift to the observation
and the sensors simultaneously transmit the result to a multi-antenna fusion
center (FC). Based on its knowledge of the wireless channel to the sensors, the
FC calculates values for the phase factors that minimize the variance of the
parameter estimate, and feeds this information back to the sensors. The use of
a phase-shift-only transmission scheme provides a simplified analog
implementation at the sensor, and also leads to a simpler algorithm design and
performance analysis. We propose two algorithms for this problem, a numerical
solution based on a relaxed semidefinite programming problem, and a closed-form
solution based on the analytic constant modulus algorithm. Both approaches are
shown to provide performance close to the theoretical bound. We derive
asymptotic performance analyses for cases involving large numbers of sensors or
large numbers of FC antennas, and we also study the impact of phase errors at
the sensor transmitters. Finally, we consider the sensor selection problem, in
which only a subset of the sensors is chosen to send their observations to the
FC.Comment: 28 pages, 5 figures, accepted by IEEE Transactions on Signal
Processing, Apr. 201
Non-linear minimum variance estimation for discrete-time multi-channel systems
A nonlinear operator approach to estimation in discrete-time systems is described. It involves inferential estimation of a signal which enters a communications channel involving both nonlinearities and transport delays. The measurements are assumed to be corrupted by a colored noise signal which is correlated with the signal to be estimated. The system model may also include a communications channel involving either static or dynamic nonlinearities. The signal channel is represented in a very general nonlinear operator form. The algorithm is relatively simple to derive and to implement
Localisation of mobile nodes in wireless networks with correlated in time measurement noise.
Wireless sensor networks are an inherent part of decision making, object tracking and location awareness systems. This work is focused on simultaneous localisation of mobile nodes based on received signal strength indicators (RSSIs) with correlated in time measurement noises. Two approaches to deal with the correlated measurement noises are proposed in the framework of auxiliary particle filtering: with a noise augmented state vector and the second approach implements noise decorrelation. The performance of the two proposed multi model auxiliary particle filters (MM AUX-PFs) is validated over simulated and real RSSIs and high localisation accuracy is demonstrated
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