Method and apparatus for rapid acoustic analysis

Patent metadata

Field	Value
Publication number	US-10539655-B1
Application number	US-201815941536-A
Country	US
Kind code	B1
Filing date	Mar 30, 2018
Priority date	Aug 28, 2014
Publication date	Jan 21, 2020
Grant date	Jan 21, 2020

Abstract

A computer-implemented method of identifying a target includes receiving at least one data input related to the target from at least one data source. At least one acoustic parameter is calculated from the at least one data input. A target identification algorithm is applied to at least one acoustic data parameter. An identification of the target is produced from at least one acoustic parameter when the target identification algorithm is applied thereto. The identification of the target is displayed.

First claim

What is claimed is:

1. A computer-implemented method of identifying a target, the method comprising:
receiving an acoustic spectrogram video associated with the target;
calculating at least one acoustic parameter from the acoustic spectrogram video;
determining a time averaging factor for the acoustic spectrogram video;
scaling the acoustic spectrogram video with the time averaging factor to produce an averaged spectrogram image;
applying a noise reduction algorithm to the at least one acoustic parameter, comprising:
filtering the averaged spectrogram image; and
producing a noise reduced spectrogram image associated with the target from the filtered spectrogram image;

applying a target identification algorithm to the at least one acoustic data parameter, comprising:
extracting and storing at least one processed image frequencies of interest from the noise reduced spectrogram image;
accessing a reference electronic database to find at least one standardized target frequency and at least one standardized frequency tolerance;
matching a processed image frequency of interest of a target image as being within a standardized frequency tolerance of a standardized target frequency, wherein pixel values for the target image are determined based on a comparison of the processed image frequency and the standardized frequency tolerance; and
building a target identification that comprises the target image and the pixel values, wherein each of the pixel values reflects an intensity of a feature in the corresponding pixel; and

displaying the target identification of the target.

2. The method of claim 1 , wherein building the target identification further comprises:
comparing the image and target frequencies of interest;
comparing the image and target frequencies tolerances; and
determining a plurality of proposed target identifications based on the comparisons of the frequencies of interest and the frequencies tolerances.

3. The method of claim 2 , further comprising:
sorting at least one calculated acoustic parameter and at least one associated harmonic associated with the target;
ranking the plurality of proposed target identifications according to the sorted parameter and harmonic to determine at least one most probable target identification; and
displaying the at least one most probable target identification.

4. A system for identifying a target, the system comprising:
an acoustic parameter database configured to receive an acoustic spectrogram video associated with the target;
a calculation algorithm programmed to calculate at least one acoustic parameter from the acoustic spectrogram video;
an auto-analysis algorithm programmed to:
apply a target identification algorithm to the at least one acoustic data parameter; and
determine a time averaging factor for the acoustic spectrogram video; and
scale the acoustic spectrogram video with the time averaging factor to produce an averaged spectrogram image;
apply a noise reduction algorithm to the at least one acoustic parameter, comprising:
filtering the averaged spectrogram image;
producing a noise reduced spectrogram image associated with the target from the filtered spectrogram image;

apply a target identification algorithm to the at least one acoustic data parameter, comprising:
extracting and storing at least one processed image frequencies of interest from the noise reduced spectrogram image;
accessing a reference electronic database to find at least one standardized target frequency and at least one standardized frequency tolerance;
matching a processed image frequency of interest of a target image as being within a standardized frequency tolerance of a standardized target frequency, wherein pixel threshold values for the target image are determined based on a comparison of the processed image frequency and the standardized frequency tolerance; and
building a target identification that comprises the target image and the pixel values, wherein each of the pixel values reflects an intensity of a feature in the corresponding pixel; and

a graphical user interface configured to display the target identification of the target.

5. The system of claim 4 , wherein the auto-analysis algorithm is programmed to build the target identification by:
comparing the image and target frequencies of interest;
comparing the image and target frequencies tolerances; and
determining a plurality of proposed target identifications based on the comparisons of the frequencies of interest and the frequencies tolerances.

6. The system of claim 5 , wherein the auto-analysis algorithm is programmed to:
sort at least one calculated acoustic parameter and at least one associated harmonic associated with the target;
rank the plurality of proposed target identifications according to the sorted parameter and harmonic to determine at least one most probable target identification; and
transmit the at least one most probable target identification to the graphical user interface.

7. A system for identifying a target, the system comprising:
an acoustic parameter database configured to receive an acoustic spectrogram video associated with the target;
a calculation algorithm programmed to calculate at least one acoustic parameter from the acoustic spectrogram video;
an auto-analysis algorithm programmed to:
determine a time averaging factor for the video;
scale the acoustic spectrogram video with the time averaging factor to produce an averaged spectrogram image;
filter the averaged spectrogram image;
produce a noise reduced spectrogram image associated with the target from the filtered spectrogram image; and
apply a target identification algorithm to the at least one acoustic data parameter, comprising:
extracting and storing at least one processed image frequencies of interest from the noise reduced spectrogram image;
accessing a reference electronic database to find at least one standardized target frequency and at least one standardized frequency tolerance;
matching a processed image frequency of interest of a target image as being within a standardized frequency tolerance of a standardized target frequency, wherein pixel threshold values for the target image are determined based on a comparison of the processed image frequency and the standardized frequency tolerance; and
building a target identification that comprises the target image and the pixel values, wherein each of the pixel values reflects an intensity of a feature in the corresponding pixel; and

a graphical user interface configured to display the target identification of the target.

8. The system of claim 7 , wherein the auto-analysis algorithm is further programmed to:
build at least one target identification by:
comparing the image and target frequencies of interest;
comparing the image and target frequencies tolerances; and
determining a plurality of proposed target identifications based on the comparisons of the frequencies of interest and the frequencies tolerances,

and further wherein the auto-analysis algorithm is programmed to:
sort at least one calculated acoustic parameter and at least one associated harmonic associated with the target;
rank the at least one proposed target identifications according to the sorted parameter and harmonic to determine at least one most probable target identification; and
transmit the at least one most probable target identification to the graphical user interface.

Assignees

• Us Navy

Inventors

• Hernandez John Christopher
• Morris Dustin Wayne

Classifications

• G01S3/801 Primary

  Details {(G01S3/82, G01S3/84, G01S3/86 take precedence)} · CPC title

• G01S7/003 Primary

  Transmission of data between radar, sonar or lidar systems and remote stations · CPC title

• G01H3/04 Primary

  Frequency · CPC title