Feature extraction and data compression system and method for distributed sensor networks

• US 8,478,319 B2
• Filed: 05/12/2010
• Issued: 07/02/2013
• Est. Priority Date: 05/12/2010
• Status: Expired due to Fees

• Current Assignee: Information Sports Technologies Incorporated
• Original Assignee: Information Sports Technologies Incorporated
• Inventors: Azimi-Sadjadi, Mahmood R., Srinivasan, SaravanaKumar, McCarron, Michael V.
• Primary Examiner(s): Wendell, Andrew
• Assistant Examiner(s): Tran, Paul P
• Application Number:
  US12/778,206
• Publication Number:
  US 20110281602A1
• Time in Patent Office: 1,147 Days
• Field of Search: 455/7, 455/9, 455/11.1, 455/18, 455/41.2, 455/66.1, 455/67.11, 455/88, 455/403, 455/405, 455/422, 455/446, 455/449, 455/450, 455/456.1, 455/456.3, 455/466, 455/507, 455/517, 455/550, 455/561, 340/10.1, 340/505, 340/521, 340/531, 340/539.01, 340/539.17, 340/539.19, 340/539.22, 340/541, 340/545.4, 340/545.5, 340/550, 340/552, 340/573.1, 367/118, 367/124, 367/127, 367/128, 367/129, 367/135, 367/136, 370/41.2, 370/310.1, 370/312, 370/315, 370/319, 370/320, 370/321, 370/328, 370/335, 370/338, 370/343, 370/346, 370/390, 379/265.01, 709/203, 709/216, 709/217, 709/219, 709/223, 709/224, 709/228, 709/229, 709/232, 709/247
• US Class Current: 455/507
• CPC Class Codes: H04B 17/27 for locating or positioning the transmitter
• CPC Class Codes: H04B 17/309 Measuring or estimating channel quality parameters
• CPC Class Codes: H04W 84/18 Self-organising networks, e.g. ad-hoc networks or sensor networks

Abstract

A distributed sensor network has a base station and clusters of sensor nodes. In a method of locating and classifying signal sources, at each node divides a received signal into blocks, performs Fourier-based transform on the blocks, selects peaks from the transformed blocks, selects subbands with features of interest based on the frequency of occurrence of the peaks across the blocks, collaborates with other nodes in the cluster to make a final selection of the subbands, encodes the subband features of the signal, and transmits the subband features to the base station. The base station processes the received subband features to locate and classify the signal sources.

Claims

1. A distributed sensor network for locating and classifying signal sources comprising:

a base station, and

at least one cluster having a plurality of sensor nodes with each sensor node including;

a sensor,

means, connected to receive a signal from said sensor, for dividing a selected time increment of said signal into blocks of time increments of said signal that are smaller than said selected time increment,

means for performing a first Fourier-based transform on each said block,

means for selecting a plurality of frequency peaks for each said block,

means for selecting a plurality of subbands of said signal based on the frequency of occurrence of said frequency peaks of said blocks,

means for collaborating with other sensor nodes in said cluster to select a plurality of common subbands from subbands selected by said nodes in said cluster,

means for performing a second Fourier-based transform on said selected time increment of said signal, and

means for transmitting said common subbands of said selected time increment of said signal to said base station,

whereby said common subbands include features relevant to a source of said signal and said base station locates and classifies said source by processing said common subbands received from said sensor nodes.

2. The network as set forth in claim 1 wherein said means for transmitting and said means for collaborating together include at least one wireless transceiver device.

3. The network as set forth in claim 1 wherein said sensor nodes each include an analog to digital converter for converting said signal into a digital signal.

4. The network as set forth in claim 1 wherein said first and second Fourier-based transforms are Discrete Cosine Transforms.

5. The network as set forth in claim 1 wherein each sensor node includes a means for encoding said common subbands before transmitting said common subbands.

6. The network as set forth in claim 1 wherein each sensor node includes a field programmable gate array and memory.

7. The network as set forth in claim 6 wherein said means for selecting a plurality of frequency peaks and said means for selecting a plurality of subbands include software instructions implemented in said field programmable gate array.

8. A distributed sensor network for locating and classifying signal sources comprising:

a base station, and

at least one cluster having a plurality of sensor nodes with each sensor node including;

a sensor for receiving a signal,

an analog to digital converter for converting said signal into a digital signal,

a field programmable gate array connected to said analog to digital converter, and programmed to divide a selected time increment of said digital signal into blocks of time increments of said signal that are smaller than said selected time increment, perform a first Discrete Cosine Transform on each said block, select a plurality of frequency peaks for each said block, select a plurality of subbands of said signal based on the frequency of occurrence of said frequency peaks in said blocks, perform a second Discrete Cosine Transform on said selected time increment of said digital signal, and

a wireless transceiver for collaborating, in cooperation with said field programmable gate array, with other sensor nodes in said cluster to select a plurality of common subbands from subbands selected by said nodes, and for transmitting said common subbands of said digital signal to said base station,

whereby said common subbands include features relevant to a source of said signal and said base station locates and classifies said source by processing said common subbands received from said sensor nodes.

9. A method of feature extraction and data reduction of a signal received by a sensor node in a cluster of sensor nodes in a sensor network with said sensor network including a base station, comprising the steps of:

dividing a selected time increment of said signal into blocks of time increments of said signal that are smaller than said selected time increment,

performing a first Fourier-based transform on each said block,

selecting a plurality of frequency peaks for each said block,

selecting a plurality of subbands of said signal based on the frequency of occurrence of said frequency peaks in said blocks,

collaborating with other sensor nodes in said cluster to select a plurality of common subbands from subbands selected by said nodes in said cluster, after said step of selecting a plurality of subbands,

performing a second Fourier-based transform on said selected time increment of said signal, and

transmitting said common subbands of said signal for said time increment to said base station,

whereby said common subbands include features relevant to a source of said signal and said common subbands reduce data relative to said signal for said selected time increment.

10. The method as set forth in claim 9 including the step of converting said signal for said selected time increment to a digital signal before said step of dividing.

11. The method as set forth in claim 9 wherein said first and second Fourier-based transforms are Discrete Cosine Transforms.

12. The method as set forth in claim 9 wherein said subbands include about ten percent of said signal for said selected time increment.

13. The method as set forth in claim 9 wherein in said step of selecting a plurality of frequency peaks, five frequency peaks are selected in each block.

14. The method as set forth in claim 9 wherein said step of selecting a plurality of subbands includes computing a histogram of said frequency peaks.

15. The method as set forth in claim 9 wherein in said step of selecting a plurality of subbands, three subbands are selected.

16. The method as set forth in claim 9 including the step of encoding said common subbands of said signal for said selected time increment before transmitting said common subbands of said signal for said selected time increment to said base station.

17. A method of feature extraction and data reduction of a signal received by a sensor node in a cluster of sensor nodes in a sensor network with said sensor network including a base station, comprising the steps of:

dividing a selected time increment of said signal into blocks of time increments of said signal that are smaller than said selected time increment,

performing a first Discrete Cosine Transform on each said block,

selecting five frequency peaks for each said block,

computing a histogram of said frequency peaks,

selecting three subbands of said signal based on the frequency of occurrence of said frequency peaks in said blocks,

collaborating with other sensor nodes in said cluster to select three of common subbands,

performing a second Discrete Cosine Transform on said selected time increment of said signal,

encoding said common subbands of said signal for said selected time increment, and

transmitting said common subbands of said signal for said selected time increment to said base station,

whereby said common subbands include features relevant to a source of said signal and said common subbands reduce data relative to said signal for said selected time increment.