36,197 research outputs found
Maximum-likelihood estimation of delta-domain model parameters from noisy output signals
Fast sampling is desirable to describe signal transmission
through wide-bandwidth systems. The delta-operator provides an ideal discrete-time modeling description for such fast-sampled systems. However, the estimation of delta-domain model parameters is usually biased by directly applying the delta-transformations to a sampled signal corrupted by additive measurement noise. This problem is solved here by expectation-maximization, where the delta-transformations of the true signal are estimated and then used to obtain the model parameters. The method is
demonstrated on a numerical example to improve on the accuracy of using a shift operator approach when the sample rate is fast
Simultaneous Sparse Approximation Using an Iterative Method with Adaptive Thresholding
This paper studies the problem of Simultaneous Sparse Approximation (SSA).
This problem arises in many applications which work with multiple signals
maintaining some degree of dependency such as radar and sensor networks. In
this paper, we introduce a new method towards joint recovery of several
independent sparse signals with the same support. We provide an analytical
discussion on the convergence of our method called Simultaneous Iterative
Method with Adaptive Thresholding (SIMAT). Additionally, we compare our method
with other group-sparse reconstruction techniques, i.e., Simultaneous
Orthogonal Matching Pursuit (SOMP), and Block Iterative Method with Adaptive
Thresholding (BIMAT) through numerical experiments. The simulation results
demonstrate that SIMAT outperforms these algorithms in terms of the metrics
Signal to Noise Ratio (SNR) and Success Rate (SR). Moreover, SIMAT is
considerably less complicated than BIMAT, which makes it feasible for practical
applications such as implementation in MIMO radar systems
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