System and Methods for Non-Parametric Technique Based Geolocation and Cognitive Sensor Activation

• US 20150009072A1
• Filed: 08/30/2013
• Published: 01/08/2015
• Est. Priority Date: 07/08/2013
• Status: Active Grant

• Current Assignee: Rockwell Collins, Inc. (Raytheon Technologies Corporation d/b/a RTX)
• Original Assignee: Rockwell Collins, Inc. (Raytheon Technologies Corporation d/b/a RTX)
• Inventors: Nijsure, Yogesh Anil
• Granted Patent: US 9,804,253 B2
• Time in Patent Office: Days
• US Class Current: 342/417
• CPC Class Codes: G01S 5/0218 Multipath in signal reception
• CPC Class Codes: G01S 5/0278 involving statistical or probabilistic considerations G01S5/0252, G01S5/0294 take precedence
• CPC Class Codes: G01S 5/28 by co-ordinating position lines of different shape, e.g. hyperbolic, circular, elliptical or radial

Abstract

The present invention relates to a geolocation system and method for a multi-path environment. The geolocation system comprises one or more emitters (201a . . . 201n), one or more sensors (202a . . . 202n) comprising at least one processor. A first processor (204) estimates angle of arrival (AOA) and time of arrival (TOA) from the signals received from said one or more emitters (201a . . . 201n). A second processor (205) determines clusters based on the (AOA) and (TOA) data. The system also comprises a central node (207) in communication with at least one sensor (202a . . . 202n) and configured to estimate geolocation of one or more emitters (201a . . . 201n) wherein, said second processor (205) clusters data for the one or more emitters (201a . . . 201n) by executing a non-parametric Bayesian technique and said central node (207) utilizes hybrid angle of arrival-time difference of arrival (AOA-TDOA) technique to determine geolocation of each of the emitters (201a . . . 201n).

Claims

1. A geolocation system for a multi-path environment comprising:

one or more emitters;

one or more sensors comprising at least one processor;

a first processor (204) for estimating angle of arrival (AOA) and time of arrival (TOA) from the signals received from the one or more emitters;

a second processor for determining clusters based on the angle of arrival (AOA) and time of arrival (TOA) data received from said one or more emitters;

a central node in communication with at least one sensor and configured to estimate geolocation of one or more emitters;

wherein said second processor clusters data for the one or more emitters in said multi-path environment by executing a non-parametric Bayesian technique; and

said central node utilizes hybrid angle of arrival-time difference of arrival (AOA-TDOA) technique to determine geolocation of each of the one or more emitters.

2. The geolocation system as claimed in claim 1, further comprising a cognitive sensor activation unit (208) in communication with said central node wherein said unit is configured to activate one or more sensor based on geolocation parameters estimated by the central node.

3. The geolocation system as claimed in claim 1, wherein said first processor and second processor is a digital signal processors (DSP) or general purpose processor (GPP).

4. The geolocation system as claimed in claim 1, wherein each of said sensors comprises an antenna array for receiving signals in a multi-path environment.

5. The geolocation system as claimed in claim 1, wherein each of said sensor comprises a transceiver for transmitting the clustering data to the central node.

6. The geolocation system as claimed in claims 1, wherein said central node and said cognitive sensor activation unit are connected to a graphical user interface.

7. The geolocation system as claimed in claim 1, wherein said second processor utilizes a Dirichlet process mixture model (DPMM) technique for clustering and determining contributions from each of the emitters.

8. A method for geolocation in a multi-path environment comprising the steps of:

receiving signals from one or more emitters at one or more sensors in said multi-path environment;

determining the angle of arrival (AOA) and time of arrival (TOA) from each of the received signals in said multi-path environment;

clustering the mixture of received signals arising from one or more emitters utilizing a non-parametric Bayesian technique;

transmitting by the one or more sensors said clusters to a central node for further processing;

estimating by the central node the time difference of arrival (TDOA) profile for each of the one or more emitters and extracting the AOA data for said emitters from within the distinct clusters; and

processing by the central node the AOA-TDOA information for each of the one or more emitters to perform a hybrid AOA-TDOA technique for geolocation.

9. The method as claimed in claim 8, comprising displaying geolocation data on a graphical user interface.

12. The method as claimed in claim 8, wherein clustering the mixture of received signals arising from one or more emitters is performed by a Dirichlet Process Mixture Model (DPMM) technique.

10. A method for selectively activating one or more sensors for tracking one or more emitters in a multi-path environment, comprising the steps of:

receiving signals from one or more emitters at one or more sensors in said multi-path environment;

determining the angle of arrival (AOA) and time of arrival (TOA) from each of the received signals in said multi-path environment;

clustering the mixture of received signals arising from one or more emitters utilizing a non-parametric Bayesian technique;

transmitting by the one or more sensors said clusters to a central node for further processing;

estimating by the central node the time difference of arrival (TDOA) profile for each of the one or more emitters and extracting the AOA data for said emitters from within the distinct clusters;

processing by the central node the AOA-TDOA information for each of the one or more emitters to perform a hybrid AOA-TDOA technique for geolocation;

tracking the position of each of the emitters based on said geolocation parameters; and

selectively activating the one or more sensors in proximity to one or more emitters.

11. The method as claimed in claim 10, wherein the one or more sensors are located in the multi-path environment discretized by Voronoi regions.

13. The method as claimed in claim 10, wherein selectively activating the one or more sensors in proximity to a targeted emitter reduces the sensor transmission power and noise contributions.

14. The method as claimed in claim 10, wherein clustering the mixture of received signals arising from one or more emitters is performed by a Dirichlet Process Mixture Model (DPMM) technique.