Variance estimation and ranking of Gaussian mixture distributions in target tracking applications

Trailovic, L.; Pao, Lucy Y.

doi:10.1109/cdc.2002.1184857

Cited by 17 publications

(14 citation statements)

References 11 publications

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“…Then we measured the variance of the measured observations V meas , corresponding to the variance of the gaussian mixture. Knowing the different gaussian means E i 's from the previous experiments, we extracted v from [23]:…”

Section: Fpga Resultsmentioning

confidence: 99%

Improved Higher-Order Side-Channel Attacks with FPGA Experiments

Peeters

Standaert

Donckers

et al. 2005

Cryptographic Hardware and Embedded Systems – CHES 2005

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Abstract. We demonstrate that masking a block cipher implementation does not sufficiently improve its security against side-channel attacks. Under exactly the same hypotheses as in a Differential Power Analysis (DPA), we describe an improvement of the previously introduced higherorder techniques allowing us to defeat masked implementations in a low (i.e. practically tractable) number of measurements. The proposed technique is based on the efficient use of the statistical distributions of the power consumption in an actual design. It is confirmed both by theoretical predictions and practical experiments against FPGA devices.

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Section: Fpga Resultsmentioning

confidence: 99%

Improved Higher-Order Side-Channel Attacks with FPGA Experiments

Peeters

Standaert

Donckers

et al. 2005

Cryptographic Hardware and Embedded Systems – CHES 2005

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show abstract

“…For data originating from k-component zeromean Gaussian mixture distributions similar tools were developed and reported in [16,17]. It was shown that for large n the distribution of the variance estimate based on n samples of data from a zero-mean Gaussian mixture distribution has the same properties as the distribution of the variance estimate based on n * =γ n samples from a zero-mean single Gaussian distribution, where the reduction factorγ can be found as a function of the mixture parameters as [16,17]:…”

Section: Variance Rankingmentioning

confidence: 99%

“…The variance ranking tools [16,17] reviewed in Section 4 were used in the optimization algorithm of [15] to yield the results in Fig. 5 and Table 1.…”

Section: Sequential Msjpda Tracking Algorithmmentioning

confidence: 99%

“…It has been shown [16,17] that a variance estimate based on data samples from a zero-mean Gaussian mixture distribution has the same properties as the distribuThis work was supported in part by the Office of Naval Research (Grant N00014-02-1-0136) and a University of Colorado Faculty Fellowship.…”

Section: Introductionmentioning

confidence: 99%

“…As a result, the tools for variance ranking based on properties of the F -distribution can still be applied to data originating from a zero-mean Gaussian mixture distribution simply by taking into account the reduced number of samples. The reduction factor has been found as a function of the Gaussian mixture parameters [16,17].…”

Section: Introductionmentioning

confidence: 99%

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Position error modeling using gaussian mixture distributions with application to comparison of tracking algorithms

Trailovic

Pao

Proceedings of the 2003 American Control Conference, 2003.

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In this paper Gaussian mixtures are used to model the distribution of position error in tracking algorithms. An expectation maximization algorithm is constructed to estimate parameters of a k-component Gaussian mixture based on a sample set obtained from a tracking simulator. The modeling and parameter estimation approach is applied to position error data generated by several tracking algorithms including multi-target multi-sensor joint probabilistic data association and particle filters. The Gaussian mixture model yields significantly better likelihood of position error compared to the single Gaussian distribution. It is further shown how the mixture models can be used to efficiently compare tracking algorithms in terms of the root mean squared position error.

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A Novel Information Theoretic Measure Based Sensor Network Design Approach for Steady State Linear Data Reconciliation

Prakash

Bhushan

2020

IFAC-PapersOnLine

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Variance estimation and ranking of Gaussian mixture distributions in target tracking applications

Cited by 17 publications

References 11 publications

Improved Higher-Order Side-Channel Attacks with FPGA Experiments

Improved Higher-Order Side-Channel Attacks with FPGA Experiments

Position error modeling using gaussian mixture distributions with application to comparison of tracking algorithms

A Novel Information Theoretic Measure Based Sensor Network Design Approach for Steady State Linear Data Reconciliation

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