6,275 research outputs found
Reconstruction of Bandlimited Functions from Unsigned Samples
We consider the recovery of real-valued bandlimited functions from the
absolute values of their samples, possibly spaced nonuniformly. We show that
such a reconstruction is always possible if the function is sampled at more
than twice its Nyquist rate, and may not necessarily be possible if the samples
are taken at less than twice the Nyquist rate. In the case of uniform samples,
we also describe an FFT-based algorithm to perform the reconstruction. We prove
that it converges exponentially rapidly in the number of samples used and
examine its numerical behavior on some test cases
Direction-of-Arrival Estimation Based on Sparse Recovery with Second-Order Statistics
Traditional direction-of-arrival (DOA) estimation techniques perform Nyquist-rate sampling of the received signals and as a result they require high storage. To reduce sampling ratio, we introduce level-crossing (LC) sampling which captures samples whenever the signal crosses predetermined reference levels, and the LC-based analog-to-digital converter (LC ADC) has been shown to efficiently sample certain classes of signals. In this paper, we focus on the DOA estimation problem by using second-order statistics based on the LC samplings recording on one sensor, along with the synchronous samplings of the another sensors, a sparse angle space scenario can be found by solving an minimization problem, giving the number of sources and their DOA's. The experimental results show that our proposed method, when compared with some existing norm-based constrained optimization compressive sensing (CS) algorithms, as well as subspace method, improves the DOA estimation performance, while using less samples when compared with Nyquist-rate sampling and reducing sensor activity especially for long time silence signal
Average sampling of band-limited stochastic processes
We consider the problem of reconstructing a wide sense stationary
band-limited process from its local averages taken either at the Nyquist rate
or above. As a result, we obtain a sufficient condition under which average
sampling expansions hold in mean square and for almost all sample functions.
Truncation and aliasing errors of the expansion are also discussed
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