Method and apparatus for navigating unmanned vehicle using sensor fusion

• US 20070233336A1
• Filed: 09/29/2004
• Published: 10/04/2007
• Est. Priority Date: 09/30/2003
• Status: Abandoned Application

• Current Assignee: Samsung Electronics Co. Ltd.
• Original Assignee: Samsung Electronics Co. Ltd.
• Inventors: Kwon, Woong, Roh, Kyung-shik, Han, Woo-sup, Shim, Jun-seok, Serguei, Boldyrev
• Application Number:
  US10/951,908
• Publication Number:
  US 20070233336A1
• Time in Patent Office: Days
• US Class Current: 701/23
• CPC Class Codes: G01C 21/04 by terrestrial means G01C21/24, G01C21/26 take precedence

Abstract

A method and apparatus for navigating an unmanned vehicle using sensor fusion are provided. This method includes: measuring a plurality of parameters using at least two sensors that sense a result of a position estimation of the unmanned vehicle; selectively combining the measured parameters; detecting changes of the parameters within expected ranges; and estimating a position of the unmanned vehicle represented by an unknown state of sensor data and a desired inference, using estimation and error distribution. The apparatus is scalable, so it can be easily expanded or compressed under any environmental conditions. The apparatus is also survivable, so if a sensor source is lost or malfunctions, it is not a disaster for the whole system, but it just decreases exponential-related error estimation. The apparatus is also modular, so the apparatus can easily determine what kind of sensor is responsible for what kind of sensing.

Claims

1. A method of navigating an unmanned vehicle, comprising:

measuring a plurality of parameters using at least two sensors that sense a result of a position estimation of the unmanned vehicle;

selectively combining the measured parameters;

detecting changes of the parameters within expected ranges; and

estimating a position of the unmanned vehicle represented by sensor data and desired data deviation, using estimation and error distribution.

2. The method of claim 1, wherein the measuring of the parameters comprises:

receiving a source signal;

transforming the source signal into a frequency-domain signal using fast Fourier transformation and calculating a spectrum density function; and

fitting a polynomial to a spectrum- and signal-dependent representation and calculating a corresponding correlation function and corresponding coefficients.

7. A computer-readable recording medium in which a computer program for executing the method of claim 1 is recorded.

3. An apparatus navigating an unmanned vehicle using sensor fusion, the apparatus comprising:

a sensor channel unit including sensors and control signal sequences, extracting raw data from the sensors, and transmitting the raw data to a pre-processing layer;

a cross-channel model calculation/feedback support unit calculating cross-products including cross- and auto-correlation channels to perform a fusion algorithm, supporting error feedback for channel parameters, and obtaining error estimation for signal processing representation;

an estimation decomposition unit generating a linear combination of orthogonal weight functions, generating a set of weight functions for estimation signal representation corresponding to signal key features, and obtaining rules for error compensation in consideration of an error estimation equation;

an estimation superimposing unit that superimpose the weight function generated by the estimation decomposition unit on a set of decomposition weight coefficients and a corresponding set of estimations of distributed random values on measured signal values; and

a final product calculation unit extracting necessary information related to a final product calculation, extracting key features related to localization according to a position and a current state of the unmanned vehicle, correlating a final product with an environment state, and obtaining unscaled and uncalibrated information about the position of the unmanned vehicle.

4. The apparatus of claim 3, wherein the sensor channel unit analyzes a signal in a spectrum domain by processing signal data using a fast Fourier transform.

5. The apparatus of claim 4, wherein the sensor channel unit tracks a state of a spectrum function, predicts and analyzes a state of a sensor channel, fits a polynomial to the spectrum function using an auto-regression method and a least mean-squared error method, obtains key parameters of the sensor channel using abstract models of the sensor channel, and tunes the sensor channel during some time according to the environmental conditions.

6. The apparatus of claim 3, wherein the cross-channel model calculation/feedback support unit uses raw signal transformation via integral convolution, spectrum functions, and power spectrum functions to calculate a correlation function, determines a cross-noise weight in signal channels using the spectrum functions and the power spectrum functions, analyzes a signal spectrum function, extracts information about the environment at early stages, and obtains cross-related products, error minimization feedback support, and key frequencies of sensor channels.