Dr. Andrew J. Noga Profile

Dr. Andrew J. Noga

at Air Force Research Lab

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Proceedings Article | 25 May 2005 Paper

Phase cycle-slip replacement via data fitting

James LaRue, Andrew Noga

Proceedings Volume 5809, (2005) https://doi.org/10.1117/12.606017

KEYWORDS: Signal to noise ratio, Modulation, Fermium, Frequency modulation, Electronic filtering, Demodulation, Linear filtering, Phase shift keying, Signal processing, Signal detection

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This paper addresses the problem of phase cycle-slip (FM click noise) elimination. For analyses and application demonstration, the signal of interest is a commercial FM transmission, received and sampled for subsequent demodulation as typical in software defined radios. There are two parts to this paper. The first part will investigate the advantages of using data fitting to repair the time series in the neighbourhood of a detected click. Previous papers have considered time series which have neighbourhoods in which only one point was considered as a click, and hence, only one point needed to be repaired. We will consider the more difficult and practical case where there is a frequency modulated signal passed through a band-pass filter and a (software/digital) FM limiter discriminator used for demodulation. This receive system has the effect of causing click distortion over multiple samples, making the repairing that much more difficult. The methods of forward- backward linear prediction (FBLP or Wiener filtering), least squares polynomial fitting (LSPOLY), and twin Tukey window (TTW) filtering are discussed. The results are shown empirically, and will show that the TTW technique outperforms the FBLP and LSPOLY techniques for the presented application. The second part of this paper will discuss potential techniques to discern samples which are clicks, from samples which are normal yet click-like. We will consider the combination of autocorrelation, kurtosis, 4th order moment, and spectral characteristics, to form a threshold detection level to identify clicks.

Proceedings Article | 25 August 2003 Paper

Performance aspects of the adjustable bandwidth concept (ABC) predetection processor

Andrew Noga

Proceedings Volume 5096, (2003) https://doi.org/10.1117/12.488343

KEYWORDS: Signal detection, Signal processing, Signal to noise ratio, Optical filters, Sensors, Electronic filtering, Convolution, Bessel functions, Statistical analysis, Radar signal processing

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There are a variety of domains in which signal channelization has proven to be useful, including the time, frequency, spatial and polarization domains. These partitioning techniques are necessary for the proper management and effective utilization of the overall channel resource. The term "multi-channel" is used to describe this partitioning of these domains. However, there are other "domains" in which channelization techniques can be employed. These include the coding domain (as in code-division multiple-access) and the less obvious steganographic domain. One can argue that these latter examples of domains lack the physical interpretation of their counterparts, or that they are each in fact a clever use of the standard domains. But from the view of the overall channel resource, very effective utilization and management tools can be developed, operated and described in these domains. In this paper, a technique is studied which is based upon a novel utilization of the signal bandwidth domain, for pre-processing prior to detection and parameter estimation. Experimental and theoretical results will be given for assessment of device performance. The studied technique is referred to as the Adjustable Bandwidth Concept (ABC) signal energy detector. When implemented digitally, this device is essentially a cepstral-based pre-processor for generating multiple channels for the analysis and detection of signal components of distinguishable bandwidths. The ABC device processes an input log-magnitude spectrogram and results in a multi-channel output. Each output channel contains information regarding the input spectrogram which is sorted or partitioned based on the bandwidth of signal components within the spectrogram. A primary application of such a device is as a pre-processing step prior to detection and estimation, for automated spectral survey and characterization.

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