Method for building robust algorithms that classify objects using high-resolution radar signals
A system and method are provided for classifying objects using high resolution radar signals. The method includes determining a probabilistic classifier of an object from a high resolution radar scan, determining a deterministic classifier of the object from the high resolution radar scan, and classifying the object based on the probabilistic classifier and the deterministic classifier.
1 . A method for classifying objects using high resolution radar signals, comprising:
determining a probabilistic classifier of an object from a high resolution radar scan;
determining a deterministic classifier of the object from the high resolution radar scan; and
classifying the object based on the probabilistic classifier and the deterministic classifier.
2 . The method of claim 1 , wherein the step of determining the probabilistic classifier includes:
selecting a feature-set consisting of features extracted from the high resolution radar scan;
selecting a probability density function (PDF) and corresponding parameter-values for each feature extracted from the high resolution radar scan; and
assembling the probabilistic classifier using the selected feature-set and the selected PDFs and their corresponding parameter-values.
3 . The method of claim 2 , wherein the corresponding parameters include an angular range for the extracted feature-values.
4 . The method of claim 2 , wherein the extracted feature-values from the high resolution radar scan correspond to a known classification class from a training data set and a known set of probabilistic classification features from the training data set.
5 . The method of claim 2 , wherein selecting the PDF and the corresponding parameter-values includes modeling a statistical distribution of each feature with a plurality of parametric PDFs.
6 . The method of claim 5 , further comprising:
estimating the corresponding parameter-values using Maximum Likelihood Parameter Estimation; and
computing a statistic ‘Q’ of the Chi-Squared Test of Goodness-of-Fit for seach parametric PDF.
7 . The method of claim 6 , wherein the parametric PDF with the lowest value of ‘Q’ and its corresponding parameter-values are selected.
8 . The method of claim 2 , wherein selecting the feature-set consisting of features extracted from the high resolution radar scan includes:
computing a probabilistic likelihood value from the extracted feature-values for each class using its joint PDF; and
classifying the extracted feature-values by selecting the class that produces the highest likelihood value.
9 . The method of claim 8 , further comprising determining the classification accuracy rate from the likelihood values.
10 . The method of claim 2 , wherein assembling the probabilistic classifier includes:
computing a probabilistic likelihood value from a joint PDF of each class; and
selecting the PDF that produces the highest likelihood value.
11 . The method of claim 10 , wherein the step of computing a probabilistic likelihood value further includes using an angular range for the extracted feature-values.
12 . The method of claim 10 , further comprising assigning a level of confidence to the selected PDF.
13 . The method of claim 12 , wherein the level of confidence is determined by an average of classification accuracy rates.
14 . The method of claim 1 , wherein determining the deterministic classifier of the object includes:
selecting a feature-set consisting of features extracted from the high resolution radar scan; and
assembling the deterministic classifier using the selected feature-set.
15 . The method of claim 14 , wherein selecting the features-set consisting of features extracted from the high resolution radar scan includes:
averaging the extracted feature-values; and
classifying the averaged value.
16 . The method of claim 14 , wherein assembling the deterministic classifier includes classifying the averaged value.
17 . The method of claim 16 , further comprising assigning a level of confidence to the classification decision.
18 . The method of claim 1 , wherein classifying the object includes outputting a classification type to a user.
19 . The method of claim 18 , wherein the classification types include a set of objects and unknown.
20 . The method of claim 19 , wherein the set of objects include a human and a vehicle.
21 . The method of claim 18 , wherein outputting the classification type is determined by assessing outputs of the probabilistic classifier and outputs of the deterministic classifier.
22 . The method of claim 21 , wherein the deterministic classifier takes precedence over the probabilistic classifier.
23 . The method of claim 1 , wherein the high resolution radar scan includes bistatic signals or multistatic signals.
24 . The method of claim 1 , wherein the high resolution radar scan includes a plurality of high resolution radar scans.
25 . A system for classifying objects using high resolution radar signals, comprising:
a high resolution radar signal module for producing a high resolution radar scan;
a probabilistic classifier module for determining an object from the high resolution radar scan;
a deterministic classifier module for determining the object from the high resolution radar scan; and
an object classification module for classifying the object based on the probabilistic classifier and the deterministic classifier.
26 . The system of claim 25 , wherein the probabilistic classifier module includes:
a feature-set module for selecting a feature-set consisting of features extracted from the high resolution radar scan;
a probability density finction (PDF) module for selecting a PDF and corresponding parameter-values for each feature extracted from the high resolution radar scan; and
an assembly module for assembling the probabilistic classifier using the selected feature-set and the selected PDFs and their corresponding parameter-values.
27 . The system of claim 26 , wherein the corresponding parameters include an angular range for the extracted feature-values.
28 . The system of claim 26 , wherein the extracted feature-values from the high resolution radar scan correspond to a known classification class from a training data set and a known set of probabilistic classification features from the training data set.
29 . The system of claim 26 , wherein the PDF module models a statistical distribution of each feature with a plurality of parametric PDFs.
30 . The system of claim 29 , further comprising:
an estimation module for estimating the corresponding parameter-values using Maximum Likelihood Parameter Estimation; and
a computation module for computing a statistic ‘Q’ of the Chi-Squared Test of Goodness-of-Fit for each parametric PDF.
31 . The system of claim 30 , wherein the parametric PDF with the lowest value of ‘Q’ and its corresponding parameter-values are selected.
32 . The system of claim 26 , wherein the feature-set module:
a likelihood module for computing a probabilistic likelihood value from the extracted feature-values for each class using its joint PDF; and
a classifying module for classifying the extracted feature-values by selecting the class that produces the highest likelihood value.
33 . The system of claim 32 , further comprising a determination module for determining the classification accuracy rate from the likelihood values.
34 . The system of claim 26 , wherein the assembly module:
a likelihood value module for computing a probabilistic likelihood value from a joint PDF of each class; and
a PDF selection module for selecting the PDF that produces the highest likelihood value.
35 . The system of claim 34 , wherein the likelihood value module further includes using an angular range for the extracted feature-values.
36 . The system of claim 34 , further comprising a confidence module for assigning a level of confidence to the selected PDF.
37 . The system of claim 36 , wherein the level of confidence is determined by an average of classification accuracy rates.
38 . The system of claim 25 , wherein the deterministic classifier module includes:
a feature-set selection module for selecting a feature-set consisting of features extracted from the high resolution radar scan; and
a deterministic classifier assembly module for assembling the deterministic classifier using the selected feature-set.
39 . The system of claim 38 , wherein the features-set selection module includes:
an averaging module for averaging the extracted feature-values; and
a classification module for classifying the averaged value.
40 . The system of claim 38 , wherein the deterministic classifier assembly module includes classifying the averaged value.
41 . The system of claim 40 , further comprising a deterministic confidence module for assigning a level of confidence to the classification decision.
42 . The system of claim 25 , wherein the object classification module includes an output module for outputting a classification type to a user.
43 . The system of claim 42 , wherein the classification types include a set of objects and unknown.
44 . The system of claim 43 , wherein the set of objects include a human and a vehicle.
45 . The system of claim 42 , wherein outputting the classification type is determined by assessing outputs of the probabilistic classifier and outputs of the deterministic classifier.
46 . The system of claim 45 , wherein the deterministic classifier takes precedence over the probabilistic classifier.
47 . The system of claim 25 , wherein the high resolution radar scan includes bistatic signals or multistatic signals.
48 . The system of claim 25 , wherein the high resolution radar scan includes a plurality of high resolution radar scans.
49 . A computer readable medium whose contents cause a computer system to classifying objects using high resolution radar signals, the computer system performing the steps of: determining a probabilistic classifier of an object from a high resolution radar scan; determining a deterministic classifier of the object from the high resolution radar scan; and classifying the object based on the probabilistic classifier and the deterministic classifier.
50 . A method for classifying objects using high resolution radar signals, comprising:
means for determining a probabilistic classifier of an object from a high resolution radar scan;
means for determining a deterministic classifier of the object from the high resolution radar scan; and
means for classifying the object based on the probabilistic classifier and the deterministic classifier.