[특허]Wavelet transform implemented classification of sensor data

Wavelet transform implemented classification of sensor data 원문보기

IPC분류정보
국가/구분	United States(US) Patent 등록
국제특허분류(IPC7판)	G01S-007/41
출원번호	US-0327548 (1994-10-24)
발명자 / 주소	Peele Larry (Orlando FL) Stirman Charles (Maitland FL)
출원인 / 주소	Martin Marietta Corporation (Bethesda MD 02)
인용정보	피인용 횟수 : 42 인용 특허 : 0

초록 ▼

An improved classifier estimates a classification of a sensed object. Data representing the sensed object are received by a sensor, such as a radar, and transformed into wavelet transform coefficients. A subset of the wavelet transform coefficients are selected, the number of coefficients in the subset being fewer in number than the size of the original data. The subset of wavelet transform coefficients is then used in place of the original data by a classifier that generates the classification of the sensed object. The classifier may be a correlation (profile matching) classifier or a quadratic classifier. In another embodiment of the invention, a wavelet fusion classifier further improves classifier performance by taking data from a single sensor, and transforming it into a reduced subset of wavelet transform coefficients for use with a correlation classifier, and also transforming the data into a reduced subset of wavelet transform coefficients for use with a quadratic classifier. These respective reduced subsets of wavelet transform coefficients are supplied to corresponding correlation and quadratic classifiers. A confidence level is determined for the output of each of the correlation and quadratic classifiers. The output of the classifier having the highest confidence level is then selected as the estimated classification of the sensed object.

대표청구항 ▼

A classifier comprising: input means for receiving sensor data signals representing a sensed object; first transform means for transforming said sensor data signals into a plurality of first wavelet transform coefficients suitable for use in a first classification processing means; first subselection means for generating a first subset of wavelet transform coefficients, said first subset comprising at least one of said plurality of first wavelet transform coefficients; first classification processing means for generating, from said first subset of wavelet transform coefficients, a first classification signal indicative of a first classification of said sensed object, wherein said first classification processing means comprises: means for comparing said first subset of wavelet transform coefficients to each of a plurality of stored known subsets of wavelet transform coefficients, each stored known subset corresponding to one of a plurality of known classifications; and means, coupled to said comparing means, for generating said first classification signal by selecting a most closely matching one of said plurality of known classifications that corresponds to a most closely matching known subset of wavelet transform coefficients; first confidence level means, coupled to said first classification processing means, for generating a first confidence level signal corresponding to said first classification signal; second transform means for transforming said sensor data signals into a plurality of second wavelet transform coefficients suitable for use in a second classification processing means; second subselection means for generating a second subset of wavelet transform coefficients, said second subset comprising at least one of said plurality of second wavelet transform coefficients; second classification processing means for generating, from said second subset of wavelet transform coefficients, a second classification signal indicative of a second classification of said sensed object, wherein said second classification processing means is a quadratic classifier; second confidence level means, coupled to said second classification processing means, for generating a second confidence level signal corresponding to said second classification signal; and means, coupled to said first and second classification processing means and to said first and second confidence level means, for determining which of said first and second confidence level signals indicates a higher confidence level, and for providing as an output of said classifier a selected one of said first and second classification signals corresponding to said determined higher confidence level.

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Wavelet transform implemented classification of sensor data 원문보기

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