4 research outputs found
Feedback Acquisition and Reconstruction of Spectrum-Sparse Signals by Predictive Level Comparisons
In this letter, we propose a sparsity promoting feedback acquisition and
reconstruction scheme for sensing, encoding and subsequent reconstruction of
spectrally sparse signals. In the proposed scheme, the spectral components are
estimated utilizing a sparsity-promoting, sliding-window algorithm in a
feedback loop. Utilizing the estimated spectral components, a level signal is
predicted and sign measurements of the prediction error are acquired. The
sparsity promoting algorithm can then estimate the spectral components
iteratively from the sign measurements. Unlike many batch-based Compressive
Sensing (CS) algorithms, our proposed algorithm gradually estimates and follows
slow changes in the sparse components utilizing a sliding-window technique. We
also consider the scenario in which possible flipping errors in the sign bits
propagate along iterations (due to the feedback loop) during reconstruction. We
propose an iterative error correction algorithm to cope with this error
propagation phenomenon considering a binary-sparse occurrence model on the
error sequence. Simulation results show effective performance of the proposed
scheme in comparison with the literature
Robust Target Localization Based on Squared Range Iterative Reweighted Least Squares
In this paper, the problem of target localization in the presence of outlying
sensors is tackled. This problem is important in practice because in many
real-world applications the sensors might report irrelevant data
unintentionally or maliciously. The problem is formulated by applying robust
statistics techniques on squared range measurements and two different
approaches to solve the problem are proposed. The first approach is
computationally efficient; however, only the objective convergence is
guaranteed theoretically. On the other hand, the whole-sequence convergence of
the second approach is established. To enjoy the benefit of both approaches,
they are integrated to develop a hybrid algorithm that offers computational
efficiency and theoretical guarantees. The algorithms are evaluated for
different simulated and real-world scenarios. The numerical results show that
the proposed methods meet the Cr'amer-Rao lower bound (CRLB) for a sufficiently
large number of measurements. When the number of the measurements is small, the
proposed position estimator does not achieve CRLB though it still outperforms
several existing localization methods.Comment: 2017 IEEE 14th International Conference on Mobile Ad Hoc and Sensor
Systems (MASS): http://ieeexplore.ieee.org/document/8108770