GPS navigation system using neural networks

• US 6,351,711 B1
• Filed: 09/14/2000
• Issued: 02/26/2002
• Est. Priority Date: 09/14/2000
• Status: Expired due to Term

• Current Assignee: CSR Technology Incorporated (Qualcomm, Inc.)
• Original Assignee: SIRF Technology Incorporated (Qualcomm, Inc.)
• Inventors: Chansarkar, Mangesh M.
• Primary Examiner(s): Cuchlinski, Jr., William A.
• Assistant Examiner(s): Hernandez, Olga
• Application Number:
  US09/662,412
• Time in Patent Office: 530 Days
• Field of Search: 701/213, 701/200, 701/214, 701/209, 731/78.R, 342/457, 342/463
• US Class Current: 701/213
• CPC Class Codes: G01S 19/42 Determining position

Abstract

A GPS receiver includes a satellite receiver/processor having an input that receives input signals from at least one GPS satellite. The output of the receiver/processor provides satellite-related navigation information. A neural network receives the satellite-related information to obtain an output signal representative of receiver-related navigation information. The neural network includes a first node layer connected to a second node layer through a first connection layer and a third node layer connected to the second node layer through a second connection layer. Each of the node layers comprises a plurality of neurons.

Claims

1. A method of determining navigation data for a GPS receiver comprising:

receiving input signals via the GPS receiver from at least one GPS satellite, the input signals comprising satellite-related navigation information; and

applying the input signals to a neural network to obtain an output signal representative of receiver-related navigation information, wherein the neural network comprises one of either a discrete-time Hopfield neural network, a continuous-time Hopfield neural network, a cellular neural network, a multilayer perception networks, a self-organizing system, a radial basis function network or a high-order neural network.

2. A method of determining navigation data for a GPS receiver using a neural network comprising a first node layer connected to a second node layer through a first connection layer and a third node layer connected to the second node layer through a second connection layer, said method comprising:

receiving input signals via the GPS receiver from at least one GPS satellite, the input signals comprising satellite-related navigation information;

connecting the input signals to the second node layer through the first node layer and the first connection layer;

connecting the outputs of the second node layer to the third node layer through the second connection layer; and

combining the outputs of the second node layer to provide receiver position data.

3. The method of claim 2 further comprising weighting the second connection layer.

4. The method of claim 3 wherein weighting the second connection layer comprises:

comparing the output signals to a desired signal to produce an error signal; and

applying the error signal to a training algorithm to determine a weight.

5. A GPS receiver comprising:

a satellite receiver/processor having an input receiving input signals from at least one GPS satellite and an output providing satellite-related navigation information; and

a neural network having an input receiving the satellite-related information to obtain an output signal representative of receiver-related navigation information;

wherein the neural network comprises one of either a discrete-time Hopfield neural network, a continuous-time Hopfield neural network, a cellular neural network, a multilayer perception networks, a self-organizing system, a radial basis function network or a high-order neural network.

6. A GPS receiver comprising:

a satellite receiver/processor having an input receiving input signals from at least one GPS satellite and an output providing satellite-related navigation information; and

a neural network having an input receiving the satellite-related information to obtain an output signal representative of receiver-related navigation information;

wherein the neural network comprises an first node layer connected to a second node layer through a first connection layer and a third node layer connected to the second node layer through a second connection layer.

7. The receiver of claim 6 wherein:

the first node layer comprises a plurality of input neurons receiving the input signals;

the second node layer comprises a plurality of hidden neurons connected to the plurality of input neurons through the first connection layer; and

the third node layer comprises a plurality of output neurons connected to the hidden neurons through the second connection layer.

8. The receiver of claim 6 wherein the second connection layer is weighted.

9. The receiver of claim 6 wherein the output neurons comprise combining devices for combining the outputs of the hidden neurons to provide receiver position data.

10. A navigation system for tracking the position of an object within a geographic area, said system comprising:

a satellite receiver/processor having an input receiving input signals from at least one GPS satellite and an output providing satellite-related navigation information and a neural network having an input receiving the satellite-related information to obtain an output signal representative of receiver-related navigation information;

a map data base storing information related to the geographic area;

a map matching processor receiving as input the receiver-related navigation information and combining the geographic-area information therewith to provide display information; and

a display responsive to the display information for providing an indication of the position of the object.