Anti-aliasing filter

Résumé

An anti-aliasing filter (AAF) is a filter used before a signal sampler to restrict the bandwidth of a signal to satisfy the Nyquist–Shannon sampling theorem over the band of interest. Since the theorem states that unambiguous reconstruction of the signal from its samples is possible when the power of frequencies above the Nyquist frequency is zero, a brick wall filter is an idealized but impractical AAF. A practical AAF makes a trade off between reduced bandwidth and increased aliasing. A practical anti-aliasing filter will typically permit some aliasing to occur or attenuate or otherwise distort some in-band frequencies close to the Nyquist limit. For this reason, many practical systems sample higher than would be theoretically required by a perfect AAF in order to ensure that all frequencies of interest can be reconstructed, a practice called oversampling. Optical applications Spatial anti-aliasing In the case of optical image sampling, as by s in digital cameras, th

Source officielle

https://en.wikipedia.org/wiki/Anti-aliasing_filter

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A Novel Complex Gm-C IF Sub-Sampling Mixer

Christian Enz, Aravind Prasad Heragu Singaiyengar

In this paper, a novel circuit referred to as complex Gm-C sampler that can be used for sub-sampling IF signals is presented. The circuit employs a conventional cross-coupled Gm-C filter structure with a built-in sampler to combine the functions of complex anti-alias filtering and baseband downconversion by sampling in a single circuit block. The filter response of the individual Gm-C samplers can be tuned to a conventional lowpass response or a sinc type response depending on the circuit parameters thereby modifying the anti-alias filtering provided by the circuit. Input noise analysis and switch noise analysis of the complex Gm-C sampler is also performed and the results are presented. The Gm-C sampler and its complex version are designed using a 0.18 mum CMOS process and its operation are validated using Spectre RF. Simulation results of the designed complex Gm-C sampler are finally provided.

Ieee Service Center, 445 Hoes Lane, Po Box 1331, Piscataway, Nj 08855-1331 Usa2009

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Sampling and reconstructing spatial fields using mobile sensors

Jayakrishnan Unnikrishnan, Martin Vetterli

The classical approach to sampling time-invariant spatial fields uses static sensors distributed over space. We study a new approach involving mobile sensors that move through space measuring the field values along their paths. A single moving sensor can take measurements over a wide spatial area thus acting as a substitute for a potentially large number of static sensors. A moving sensor encounters the spatial field in its path in the form of a time-domain signal. Hence a time-domain anti-aliasing filter can be employed at the mobile sensor to limit the amount of out-of-band noise prior to sampling. We analytically quantify the advantage of mobile sensing over static sensing in rejecting out-of-band noise. We also demonstrate via simulations the improvement in reconstruction accuracy that can be obtained using mobile sensors and filtering in a temperature measurement problem.

Ieee2012

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Sampling and Reconstruction of Spatial Fields using Mobile Sensors

Jayakrishnan Unnikrishnan, Martin Vetterli

Spatial sampling is traditionally studied in a static setting where static sensors scattered around space take measurements of the spatial field at their locations. In this paper we study the emerging paradigm of sampling and reconstructing spatial fields using sensors that move through space. We show that mobile sensing offers some unique advantages over static sensing in sensing bandlimited spatial fields. Since a moving sensor encounters such a spatial field along its path as a time-domain signal, a time-domain anti-aliasing filter can be employed prior to sampling the signal received at the sensor. Such a filtering procedure, when used by a configuration of sensors moving at constant speeds along equispaced parallel lines, leads to a complete suppression of spatial aliasing in the direction of motion of the sensors. We analytically quantify the advantage of using such a sampling scheme over a static sampling scheme by computing the reduction in sampling noise due to the filter. We also analyze the effect of non-uniform sensor speeds on the reconstruction accuracy. Using simulation examples we demonstrate the advantages of mobile sampling over static sampling in practical problems. We extend our analysis to sampling and reconstruction schemes for monitoring time-varying bandlimited fields using mobile sensors. We demonstrate that in some situations we require a lower density of sensors when using a mobile sensing scheme instead of the conventional static sensing scheme. The exact advantage is quantified for a problem of sampling and reconstructing an audio field.

Institute of Electrical and Electronics Engineers2013

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