Two-Dimensional Subspace-Based Model Order Selection Methods for FMCW Automotive Radar Systems

2018 ◽  
Author(s):  
Yuliang Sun ◽  
Tai Fei ◽  
Nils Pohl
Keyword(s):  
Dimensional Subspace ◽  
Order Selection ◽  
Two Dimensional ◽  
Selection Methods ◽  
Automotive Radar ◽  
Model Order Selection ◽  
Model Order ◽  
Radar Systems

scholarly journals Online LTI system identification and time delay estimation

2021 ◽  
Author(s):  
Seyed Hossein Rahnamaee
Keyword(s):  
Time Delay ◽  
System Identification ◽  
Information Criterion ◽  
Order Selection ◽  
Selection Methods ◽  
Model Order Selection ◽  
Data Set ◽  
Model Order ◽  
Lti System ◽  
Selection For

Model order selection for linear time-invariant (LTI) systems is an important system modeling concern and has been widely investigated through past decades. Different approaches of order selection such as Akaike information criterion (AIC), Bayesian information criterion (BIC), minimum description length (MDL) and reconstruction error LTI system identification (RE-LTI) propose different criteria to select the optimum order of a system. In many real life applications of model order selection the size of an observed data set is increasing. Thus, order selection methods need to adopt the best fit of a model as the data set size is increasing. This is our motivation to extend RE-LTI order selection for online application of order selection with lower computational cost and complexity. It has been shown previously that AIC, BIC, two-stage MDL and many existing order selection criteria are special cases of RE-LTI method. Our online order selection approach reduces the computational complexity of the offline approach from O(N3) to O(N2). It should be noted that RE-LTI and MNDL order selection methods have same fundamentals and consequently extending RE-LTI to online RE-LTI also extends MNDL to online MNDL. Another crucial issue in system identification and modeling is estimating the time delay of a system’s impulse response (or determining the start of its non-zero part). This problem is addressed in various areas including radar, sonar, acoustic source tracking, multipath channel identification, as well as many automatic control applications. Utilizing fundamentals of RE-LTI approach, here we introduce a new time-delay estimator. Simulation results show advantages of the proposed method and its superiority to existing approaches in accuracy and robustness in terms of the FIT index.

APPLICATION OF THE TWO-DIMENSIONAL FREQUENCY DOMAIN LEAST SQUARES ALGORITHM TO AIRBORNE SURVEILLANCE RADAR DETECTION

2000 ◽  
Vol 10 (05n06) ◽  
pp. 293-303
Author(s):  
QINGWEN ZHANG ◽  
WASFY B. MIKHAEL
Keyword(s):  
Least Squares ◽  
Frequency Domain ◽  
Airborne Radar ◽  
Order Selection ◽  
Two Dimensional ◽  
Radar Detection ◽  
Test Statistic ◽  
Model Order Selection ◽  
Model Order ◽  
Simulation Based

The application of the two-dimensional frequency domain least squares (2D–FD–LS) algorithm to parametric-based airborne radar detection is discussed herein. Several issues such as model order selection and model stabilization are considered. The frequency-domain version of the test statistic is also derived here. Simulation-based results demonstrate that good performance can be obtained.

scholarly journals Online LTI system identification and time delay estimation

2021 ◽  
Author(s):  
Seyed Hossein Rahnamaee
Keyword(s):  
Time Delay ◽  
System Identification ◽  
Information Criterion ◽  
Order Selection ◽  
Selection Methods ◽  
Model Order Selection ◽  
Data Set ◽  
Model Order ◽  
Lti System ◽  
Selection For

Model order selection for linear time-invariant (LTI) systems is an important system modeling concern and has been widely investigated through past decades. Different approaches of order selection such as Akaike information criterion (AIC), Bayesian information criterion (BIC), minimum description length (MDL) and reconstruction error LTI system identification (RE-LTI) propose different criteria to select the optimum order of a system. In many real life applications of model order selection the size of an observed data set is increasing. Thus, order selection methods need to adopt the best fit of a model as the data set size is increasing. This is our motivation to extend RE-LTI order selection for online application of order selection with lower computational cost and complexity. It has been shown previously that AIC, BIC, two-stage MDL and many existing order selection criteria are special cases of RE-LTI method. Our online order selection approach reduces the computational complexity of the offline approach from O(N3) to O(N2). It should be noted that RE-LTI and MNDL order selection methods have same fundamentals and consequently extending RE-LTI to online RE-LTI also extends MNDL to online MNDL. Another crucial issue in system identification and modeling is estimating the time delay of a system’s impulse response (or determining the start of its non-zero part). This problem is addressed in various areas including radar, sonar, acoustic source tracking, multipath channel identification, as well as many automatic control applications. Utilizing fundamentals of RE-LTI approach, here we introduce a new time-delay estimator. Simulation results show advantages of the proposed method and its superiority to existing approaches in accuracy and robustness in terms of the FIT index.

scholarly journals Analysis of Residual Dependencies of Independent Components Extracted from fMRI Data

2016 ◽  
Vol 2016 ◽  
pp. 1-15
Author(s):  
N. Vanello ◽  
E. Ricciardi ◽  
L. Landini
Keyword(s):  
Clustering Algorithm ◽  
A Priori ◽  
Similarity Measures ◽  
Real Data ◽  
Fmri Data ◽  
Order Selection ◽  
Functional Magnetic Resonance ◽  
Model Order Selection ◽  
Model Order ◽  
Independent Components

Independent component analysis (ICA) of functional magnetic resonance imaging (fMRI) data can be employed as an exploratory method. The lack in the ICA model of strong a priori assumptions about the signal or about the noise leads to difficult interpretations of the results. Moreover, the statistical independence of the components is only approximated. Residual dependencies among the components can reveal informative structure in the data. A major problem is related to model order selection, that is, the number of components to be extracted. Specifically, overestimation may lead to component splitting. In this work, a method based on hierarchical clustering of ICA applied to fMRI datasets is investigated. The clustering algorithm uses a metric based on the mutual information between the ICs. To estimate the similarity measure, a histogram-based technique and one based on kernel density estimation are tested on simulated datasets. Simulations results indicate that the method could be used to cluster components related to the same task and resulting from a splitting process occurring at different model orders. Different performances of the similarity measures were found and discussed. Preliminary results on real data are reported and show that the method can group task related and transiently task related components.

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