The Experts below are selected from a list of 26979 Experts worldwide ranked by ideXlab platform

Aaron B Wagner - One of the best experts on this subject based on the ideXlab platform.

  • on the optimality of binning for distributed Hypothesis Testing
    IEEE Transactions on Information Theory, 2012
    Co-Authors: Md Saifur Rahman, Aaron B Wagner
    Abstract:

    We study a Hypothesis Testing Problem in which data are compressed distributively and sent to a detector that seeks to decide between two possible distributions for the data. The aim is to characterize all achievable encoding rates and exponents of the type 2 error probability when the type 1 error probability is at most a fixed value. For related Problems in distributed source coding, schemes based on random binning perform well and are often optimal. For distributed Hypothesis Testing, however, the use of binning is hindered by the fact that the overall error probability may be dominated by errors in the binning process. We show that despite this complication, binning is optimal for a class of Problems in which the goal is to “test against conditional independence.” We then use this optimality result to give an outer bound for a more general class of instances of the Problem.

  • vector gaussian Hypothesis Testing and lossy one helper Problem
    International Symposium on Information Theory, 2009
    Co-Authors: Saifur Rahman, Aaron B Wagner
    Abstract:

    We study the vector Gaussian versions of two Problems: Hypothesis Testing under a communication constraint and the lossy one-helper Problem. In the Hypothesis Testing Problem, a test against independence is considered when a vector Gaussian source is available at the detector which receives a message about another vector Gaussian source at a specified rate. Two equivalent characterizations of the optimal type 2 error exponent are given when the type 1 error is at most a fixed constant. The first characterization is based on enhancement technique introduced by Weingarten et. al. and the other is transform-based. The transform-based characterization directly yields a water pouring interpretation, and establishes successive refinability. For the lossy one-helper Problem, we determine a portion of the boundary of the rate region.

Raj Rao Nadakuditi - One of the best experts on this subject based on the ideXlab platform.

  • passive radar detection with noisy reference signal using measured data
    IEEE Radar Conference, 2017
    Co-Authors: Sandeep Gogineni, Pawan Setlur, Muralidhar Rangaswamy, Raj Rao Nadakuditi
    Abstract:

    Traditional passive radar systems with a noisy reference signal use the cross-correlation (CC) statistic for detection. However, owing to the composite nature of this Hypothesis Testing Problem, no claims can be made about the optimality of this detector. Further, most modern day commercial digital illuminators and non-cooperative radar transmit signals which have an inherent low-rank structure. Therefore, exploiting this low-rank structure of most passive radar illuminators, we recently proposed singular value decomposition (SVD) based detector that outperforms the CC detector while offering “near CFAR” behavior with respect to varying signal strengths on the reference channel. In this paper, we compare these detectors using measured data collected from experiments in a controlled laboratory setting. We demonstrate the improved performance offered by the SVD detector when compared to the traditional CC detector while transmitting LFM as well as pseudo noise waveforms.

  • comparison of passive radar detectors with noisy reference signal
    IEEE Signal Processing Workshop on Statistical Signal Processing, 2016
    Co-Authors: Sandeep Gogineni, Pawan Setlur, Muralidhar Rangaswamy, Raj Rao Nadakuditi
    Abstract:

    Traditional passive radar systems with a noisy reference signal use the cross-correlation statistic for detection. However, owing to the composite nature of this Hypothesis Testing Problem, no claims can be made about the optimality of this detector. Therefore, exploiting the low-rank structure of most passive radar illuminators, we recently proposed singular value decomposition based detectors that outperform the CC detector. In this paper, we derive the generalized likelihood ratio tests for this signal model and compare with our proposed SVD based detectors. We demonstrate the near CFAR behavior (highly desirable) of our SVD detectors. We show that on the other hand, the GLRT detectors have a varying probability of false alarm with changing reference channel characteristics making it impractical to use them in a passive radar system.

  • random matrix theory inspired passive bistatic radar detection with noisy reference signal
    International Conference on Acoustics Speech and Signal Processing, 2015
    Co-Authors: Sandeep Gogineni, Pawan Setlur, Muralidhar Rangaswamy, Raj Rao Nadakuditi
    Abstract:

    Traditional passive radar systems with a noisy reference signal use the cross-correlation statistic for detection. However, owing to the composite nature of this Hypothesis Testing Problem, no claims can be made about the optimality of this detector. In this paper, we consider digital illuminators such that the transmitted signal in a processing interval is a weighted periodic summation of several identical pulses. The target reflectivity is assumed to change independently from one pulse to another within a processing interval. Inspired by random matrix theory, we propose a singular value decomposition (SVD) and Eigen detector for this model that significantly outperforms the conventional cross-correlation detector. We demonstrate this performance improvement through extensive numerical simulations across various surveillance and reference signal-to-noise ratio (SNR) regimes.

Yuval Kochman - One of the best experts on this subject based on the ideXlab platform.

  • On the Reliability Function of Distributed Hypothesis Testing Under Optimal Detection
    IEEE Transactions on Information Theory, 2019
    Co-Authors: Nir Weinberger, Yuval Kochman
    Abstract:

    The distributed Hypothesis Testing Problem with full side-information is studied. The trade-off (reliability function) between the two types of error exponents under limited rate is studied in the following way. First, the Problem is reduced to the Problem of determining the reliability function of channel codes designed for detection (in analogy to a similar result which connects the reliability function of distributed lossless compression and ordinary channel codes). Second, a single-letter random-coding bound based on a hierarchical ensemble, as well as a single-letter expurgated bound, are derived for the reliability of channel-detection codes. Both bounds are derived for a system which employs the optimal detection rule. We conjecture that the resulting random-coding bound is ensemble-tight, and consequently optimal within the class of quantization-and-binning schemes.

  • on the reliability function of distributed Hypothesis Testing under optimal detection
    International Symposium on Information Theory, 2018
    Co-Authors: Nir Weinberger, Yuval Kochman
    Abstract:

    The distributed Hypothesis-Testing Problem with full side-information is studied. The trade-off (reliability function) between the type 1 and type 2 error exponents under limited rate is studied in the following way. First, the Problem of determining the reliability function of distributed Hypothesis-Testing is reduced to the Problem of determining the reliability function of channel-detection codes (in analogy to a similar result which connects the reliability of distributed compression and ordinary channel codes). Second, a random-coding bound based on an hierarchical ensemble, as well as an expurgated bound, are derived for the reliability of channel-detection codes. The resulting bounds are the first to be derived for quantization-and-binning schemes under optimal detection.

  • On the Reliability Function of Distributed Hypothesis Testing Under Optimal Detection
    arXiv: Information Theory, 2018
    Co-Authors: Nir Weinberger, Yuval Kochman
    Abstract:

    The distributed Hypothesis-Testing Problem with full side-information is studied. The trade-off (reliability function) between the type 1 and type 2 error exponents under limited rate is studied in the following way. First, the Problem of determining the reliability function of distributed Hypothesis-Testing is reduced to the Problem of determining the reliability function of channel-detection codes (in analogy to a similar result which connects the reliability of distributed compression and ordinary channel codes). Second, a single-letter random-coding bound based on an hierarchical ensemble, as well as a single-letter expurgated bound, are derived for the reliability of channel-detection codes. Both bounds are derived for the optimal detection rule. We believe that the resulting bounds are ensemble-tight, and hence optimal within the class of quantization-and-binning schemes.

Andrew Xu - One of the best experts on this subject based on the ideXlab platform.

  • comparison of signal detectors for time domain radio seti
    arXiv: Signal Processing, 2018
    Co-Authors: Gregory Hellbourg, Andrew Xu
    Abstract:

    The radio Search for Extra Terrestrial Intelligence (SETI) aims at identifying intelligent and communicative civilizations in the Universe through the detection of engineered transmissions. In the absence of prior knowledge concerning the expected signal, SETI detection pipelines necessitate high sensitivity, versatility, and limited computational complexity to maximize the search parameter space and minimize the probability of misses. This paper addresses the SETI detection Problem as a binary Hypothesis Testing Problem, and compares four detection schemes exploiting artificial features of the data collected by a single receiver radio telescope. After a theoretical comparison, those detectors are applied to real data collected with the Green Bank Telescope in West Virginia (USA).

  • comparison of signal detectors for time domain radio seti
    2018 2nd URSI Atlantic Radio Science Meeting (AT-RASC), 2018
    Co-Authors: Gregory Hellbourg, Andrew Xu
    Abstract:

    Thellbourgdetectorspaper. In the absence of prior knowledge concerning the expected signal, SETI detection pipelines necessitate high sensitivity, versatility, and limited computational complexity to maximize the search parameter space and minimize the probability of misses. This paper addresses the SETI detection Problem as a binary Hypothesis Testing Problem, and compares four detection schemes exploiting artificial features of the data collected by a single receiver radio telescope. After a theoretical comparison, those detectors are applied to real data collected with the Green Bank Telescope in West Virginia (USA).

Sandeep Gogineni - One of the best experts on this subject based on the ideXlab platform.

  • passive radar detection with noisy reference signal using measured data
    IEEE Radar Conference, 2017
    Co-Authors: Sandeep Gogineni, Pawan Setlur, Muralidhar Rangaswamy, Raj Rao Nadakuditi
    Abstract:

    Traditional passive radar systems with a noisy reference signal use the cross-correlation (CC) statistic for detection. However, owing to the composite nature of this Hypothesis Testing Problem, no claims can be made about the optimality of this detector. Further, most modern day commercial digital illuminators and non-cooperative radar transmit signals which have an inherent low-rank structure. Therefore, exploiting this low-rank structure of most passive radar illuminators, we recently proposed singular value decomposition (SVD) based detector that outperforms the CC detector while offering “near CFAR” behavior with respect to varying signal strengths on the reference channel. In this paper, we compare these detectors using measured data collected from experiments in a controlled laboratory setting. We demonstrate the improved performance offered by the SVD detector when compared to the traditional CC detector while transmitting LFM as well as pseudo noise waveforms.

  • comparison of passive radar detectors with noisy reference signal
    IEEE Signal Processing Workshop on Statistical Signal Processing, 2016
    Co-Authors: Sandeep Gogineni, Pawan Setlur, Muralidhar Rangaswamy, Raj Rao Nadakuditi
    Abstract:

    Traditional passive radar systems with a noisy reference signal use the cross-correlation statistic for detection. However, owing to the composite nature of this Hypothesis Testing Problem, no claims can be made about the optimality of this detector. Therefore, exploiting the low-rank structure of most passive radar illuminators, we recently proposed singular value decomposition based detectors that outperform the CC detector. In this paper, we derive the generalized likelihood ratio tests for this signal model and compare with our proposed SVD based detectors. We demonstrate the near CFAR behavior (highly desirable) of our SVD detectors. We show that on the other hand, the GLRT detectors have a varying probability of false alarm with changing reference channel characteristics making it impractical to use them in a passive radar system.

  • random matrix theory inspired passive bistatic radar detection with noisy reference signal
    International Conference on Acoustics Speech and Signal Processing, 2015
    Co-Authors: Sandeep Gogineni, Pawan Setlur, Muralidhar Rangaswamy, Raj Rao Nadakuditi
    Abstract:

    Traditional passive radar systems with a noisy reference signal use the cross-correlation statistic for detection. However, owing to the composite nature of this Hypothesis Testing Problem, no claims can be made about the optimality of this detector. In this paper, we consider digital illuminators such that the transmitted signal in a processing interval is a weighted periodic summation of several identical pulses. The target reflectivity is assumed to change independently from one pulse to another within a processing interval. Inspired by random matrix theory, we propose a singular value decomposition (SVD) and Eigen detector for this model that significantly outperforms the conventional cross-correlation detector. We demonstrate this performance improvement through extensive numerical simulations across various surveillance and reference signal-to-noise ratio (SNR) regimes.