998 research outputs found
Sequential Sensing with Model Mismatch
We characterize the performance of sequential information guided sensing,
Info-Greedy Sensing, when there is a mismatch between the true signal model and
the assumed model, which may be a sample estimate. In particular, we consider a
setup where the signal is low-rank Gaussian and the measurements are taken in
the directions of eigenvectors of the covariance matrix in a decreasing order
of eigenvalues. We establish a set of performance bounds when a mismatched
covariance matrix is used, in terms of the gap of signal posterior entropy, as
well as the additional amount of power required to achieve the same signal
recovery precision. Based on this, we further study how to choose an
initialization for Info-Greedy Sensing using the sample covariance matrix, or
using an efficient covariance sketching scheme.Comment: Submitted to IEEE for publicatio
Optimized Data Rate Allocation for Dynamic Sensor Fusion over Resource Constrained Communication Networks
This paper presents a new method to solve a dynamic sensor fusion problem. We
consider a large number of remote sensors which measure a common Gauss-Markov
process and encoders that transmit the measurements to a data fusion center
through the resource restricted communication network. The proposed approach
heuristically minimizes a weighted sum of communication costs subject to a
constraint on the state estimation error at the fusion center. The
communication costs are quantified as the expected bitrates from the sensors to
the fusion center. We show that the problem as formulated is a
difference-of-convex program and apply the convex-concave procedure (CCP) to
obtain a heuristic solution. We consider a 1D heat transfer model and 2D target
tracking by a drone swarm model for numerical studies. Through these
simulations, we observe that our proposed approach has a tendency to assign
zero data rate to unnecessary sensors indicating that our approach is sparsity
promoting, and an effective sensor selection heuristic
Efficient Sensor Deployments for Spatio-Temporal Environmental Monitoring
IEEE This paper addresses the problem of efficiently deploying sensors in spatial environments, e.g., buildings, for the purposes of monitoring spatio-temporal environmental phenomena. By modeling the environmental fields using spatio-temporal Gaussian processes, a new and efficient optimality-cost function of minimizing prediction uncertainties is proposed to find the best sensor locations. Though the environmental processes spatially and temporally vary, the proposed approach of choosing sensor positions is proven not to be affected by time variations, which significantly reduces computational complexity of the optimization problem. The sensor deployment optimization problem is then solved by a practical and feasible polynomial algorithm, where its solutions are theoretically proven to be guaranteed. The proposed method is also theoretically and experimentally compared with the existing works. The effectiveness of the proposed algorithm is demonstrated by implementation in a real tested space in a university building, where the obtained results are highly promising
DNN-DANM: A High-Accuracy Two-Dimensional DOA Estimation Method Using Practical RIS
Reconfigurable intelligent surface (RIS) or intelligent reflecting surface
(IRS) has been an attractive technology for future wireless communication and
sensing systems. However, in the practical RIS, the mutual coupling effect
among RIS elements, the reflection phase shift, and amplitude errors will
degrade the RIS performance significantly. This paper investigates the
two-dimensional direction-of-arrival (DOA) estimation problem in the scenario
using a practical RIS. After formulating the system model with the mutual
coupling effect and the reflection phase/amplitude errors of the RIS, a novel
DNNDANM method is proposed for the DOA estimation by combining the deep neural
network (DNN) and the decoupling atomic norm minimization (DANM). The DNN step
reconstructs the received signal from the one with RIS impairments, and the
DANM step exploits the signal sparsity in the two-dimensional spatial domain.
Additionally, a semi-definite programming (SDP) method with low computational
complexity is proposed to solve the atomic minimization problem. Finally, both
simulation and prototype are carried out to show estimation performance, and
the proposed method outperforms the existing methods in the two-dimensional DOA
estimation with low complexity in the scenario with practical RIS.Comment: 11 pages, 12 figure
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