Performance Analysis of a Robust Matched Subspace Detector

2011 ◽  
Vol 480-481 ◽  
pp. 775-780
Author(s):  
Ting Jun Li
Keyword(s):  
Performance Analysis ◽  
Matched Filter ◽  
Alternative Hypothesis ◽  
Additive White Gaussian Noise ◽  
Ratio Test ◽  
Noise Power ◽  
Test Statistic ◽  
Robust Detection ◽  
Low Snr ◽  
Useful Signal

The area of robust detection in the presence of partly unknown useful signal or interference is a widespread task in many signal processing applications. In this paper, we consider the robustness of a matched subspace detector in additive white Gaussian noise, under the condition that the noise power is known under null hypothesis, and unknown under alternative hypothesis when the useful signal triggers an variation of noise power, and we also consider the mismatch between the signal subspace and receiver matched filter. The test statistic of this detection problem is derived based on generalized likelihood ratio test, and the distribution of the test statistic is analysis. The computer simulation is used to validate the performance analysis and the robustness of this algorithm at low SNR, compared with other matched subspace detectors.

scholarly journals Monitoring Persistence Change in Heavy-Tailed Observations

Symmetry ◽  
2021 ◽  
Vol 13 (6) ◽  
pp. 936
Author(s):  
Dan Wang
Keyword(s):  
Kernel Method ◽  
Alternative Hypothesis ◽  
Null Distribution ◽  
Real Data ◽  
Ratio Test ◽  
Finite Sample ◽  
Test Statistic ◽  
Bootstrap Approximation ◽  
Heavy Tailed ◽  
Better Than

In this paper, a ratio test based on bootstrap approximation is proposed to detect the persistence change in heavy-tailed observations. This paper focuses on the symmetry testing problems of I(1)-to-I(0) and I(0)-to-I(1). On the basis of residual CUSUM, the test statistic is constructed in a ratio form. I prove the null distribution of the test statistic. The consistency under alternative hypothesis is also discussed. However, the null distribution of the test statistic contains an unknown tail index. To address this challenge, I present a bootstrap approximation method for determining the rejection region of this test. Simulation studies of artificial data are conducted to assess the finite sample performance, which shows that our method is better than the kernel method in all listed cases. The analysis of real data also demonstrates the excellent performance of this method.

scholarly journals Simplifying the Performance Analysis of the SPRT for GPS Acquisition

2011 ◽  
Vol 2011 ◽  
pp. 1-15 ◽  
Author(s):  
Niamh O'Mahony ◽  
Gérard Lachapelle ◽  
Colin C. Murphy
Keyword(s):  
Performance Analysis ◽  
Sequential Probability Ratio Test ◽  
Ratio Test ◽  
Noise Power ◽  
Receiver Design ◽  
Probability Ratio ◽  
Acquisition Strategies ◽  
Noncoherent Integration ◽  
Sequential Probability ◽  
Theoretical Performance

A new approximation for the distribution of the probability ratio in a sequential probability ratio test (SPRT) using noncoherent integration across a full code period is presented. The new approximation is valid for the carrier-to-noise power ratios (C/N0) typically encountered in GPS acquisition (20 dB-Hz ≤ C/N0 ≤ 50 dB-Hz), and it allows accurate theoretical performance analysis of the SPRT to be carried out for signals in this C/N0 range, eliminating the need for lengthy simulations for each scenario under investigation. Thus, the SPRT performance can be readily compared to that of other acquisition strategies for receiver design. Previous approximations in the literature are not valid in the range 20 dB-Hz ≤ C/N0 ≤ 50 dB-Hz.

Non-Standard Tests through a Composite Null and Alternative in Point-Identified Parameters

2015 ◽  
Vol 4 (1) ◽  
pp. 1-28 ◽  
Author(s):  
Jinyong Hahn ◽  
Geert Ridder
Keyword(s):  
Confidence Interval ◽  
Alternative Hypothesis ◽  
Tuning Parameter ◽  
Closed Form Expression ◽  
Ratio Test ◽  
Population Parameters ◽  
Test Statistic ◽  
Inference Problem ◽  
New Approach ◽  
Interval Censored

AbstractWe propose a new approach to statistical inference on parameters that depend on population parameters in a non-standard way. As examples we consider a parameter that is interval identified and a parameter that is the maximum (or minimum) of population parameters. In both examples we transform the inference problem into a test of a composite null against a composite alternative hypothesis involving point identified population parameters. We use standard tools in this testing problem. This setup substantially simplifies the conceptual basis of the inference problem. By inverting the Likelihood Ratio test statistic for the composite null and composite alternative inference problem, we obtain a closed form expression for the confidence interval that does not require any tuning parameter and is uniformly valid. We use our method to derive a confidence interval for a regression coefficient in a multiple linear regression with an interval censored dependent variable.

Signal Detection Based on Cyclic Autocorrelation under Noise Uncertainty

2013 ◽  
Vol 336-338 ◽  
pp. 1733-1737
Author(s):  
Chao Wang ◽  
Li Qiang Tian
Keyword(s):  
Cognitive Radio ◽  
Signal Detection ◽  
Autocorrelation Function ◽  
Computational Cost ◽  
Noise Power ◽  
Feature Detector ◽  
Robust Detection ◽  
Low Snr ◽  
Uncertainty Model ◽  
Snr Wall

Signal detection is a key enabler of cognitive radio. This paper considers the detection signals in uncertain low SNR environments. We propose a feature detector based on cyclic autocorrelation function of signal. Compared with other feature detector based on cyclic spectral, the proposed detector need lower computational cost than computational cyclic spectrum. Similar radiometer detector,SNR wall also exists in noise power uncertainty model. Beyond this SNR wall robust detection is impossible.Detection performance including the SNR wall is proved.

ERROR DETECTION IN GENETIC LINKAGE DATA FOR HUMAN PEDIGREES USING LIKELIHOOD RATIO METHODS

1995 ◽  
Vol 03 (01) ◽  
pp. 13-25 ◽  
Author(s):  
MARGARET GELDER EHM ◽  
MAREK KIMMEL ◽  
ROBERT W. COTTINGHAM
Keyword(s):  
Likelihood Ratio ◽  
Error Detection ◽  
Null Hypothesis ◽  
Alternative Hypothesis ◽  
Recombination Fraction ◽  
Linkage Data ◽  
Ratio Test ◽  
Pedigree Data ◽  
Test Statistic ◽  
Typing Error

The occurrence of laboratory typing error in pedigree data collected for use in linkage analysis cannot be ignored. In maps where recombinations between nearby markers rarely occur, each erroneous recombinations (result of typing error) is given substantial weight thereby increasing the estimate of θ, the recombination fraction. As the maps being developed become more dense, θ approaches the error rate and most of all observed crossovers will be erroneous. We present a method for detecting errors in pedigree data. The index is a variant of the likelihood ratio test statistic and is used to test the null hypothesis of no error for an individual at a locus versus the alternative hypothesis of error. High values of the index correspond to unlikely genotypes. The method has been shown to detect errors introduced into CEPH pedigrees and an error in a larger experimental pedigree (retinitis pigmentosa). While the method was designed to detect typing error, it is sufficiently general to detect any relatively unlikely genotype and therefore can also be used to detect pedigree error.

Object Detection Using an Ideal Observer Model

2020 ◽  
Vol 2020 (16) ◽  
pp. 41-1-41-7
Author(s):  
Orit Skorka ◽  
Paul J. Kane
Keyword(s):  
Object Detection ◽  
Matched Filter ◽  
Image Sensors ◽  
Ideal Observer ◽  
Noise Power ◽  
Relative Evaluation ◽  
Detection And Identification ◽  
Cross Correlation Analysis ◽  
Observer Model ◽  
The Ideal

Many of the metrics developed for informational imaging are useful in automotive imaging, since many of the tasks – for example, object detection and identification – are similar. This work discusses sensor characterization parameters for the Ideal Observer SNR model, and elaborates on the noise power spectrum. It presents cross-correlation analysis results for matched-filter detection of a tribar pattern in sets of resolution target images that were captured with three image sensors over a range of illumination levels. Lastly, the work compares the crosscorrelation data to predictions made by the Ideal Observer Model and demonstrates good agreement between the two methods on relative evaluation of detection capabilities.

An Effective Spectrum Sensing Method Based on Correlation Coefficient and Energy Detection

2014 ◽  
Vol 1023 ◽  
pp. 210-213
Author(s):  
Fu Lai Liu ◽  
Shou Ming Guo ◽  
Rui Yan Du
Keyword(s):  
Correlation Coefficient ◽  
Spectrum Sensing ◽  
Dynamic Spectrum Access ◽  
Energy Detection ◽  
Low Complexity ◽  
Noise Power ◽  
Decision Threshold ◽  
Test Statistic ◽  
Receiving Antenna ◽  
Influence Of Noise

Spectrum sensing is the key functionality for dynamic spectrum access in cognitive radio networks. Energy detection is one of the most popular spectrum sensing methods due to its low complexity and easy implementation. However, performance of the energy detector is susceptible to uncertainty in noise power. To overcome this problem, this paper proposes an effective spectrum sensing method based on correlation coefficient. The proposed method utilizes a single receiving antenna with a delay device to acquire the original received signal and the delayed signal. Then the correlation coefficient of the two signals is computed and the result is used as the test statistic. Theoretical analysis shows that the decision threshold is unrelated to noise power, thus the proposed approach can effectively overcome the influence of noise power uncertainty. Simulation results testify the effectiveness of the proposed method even in low signal-to-noise (SNR) conditions.

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