System and method of time of flight detection
A position-determining apparatus, such as a GPS receiver, determines the position of the mobile device based on the time of flight of a transmitted probe signal using a method in which sections of the received signal is classified into two or more categories and accumulated according to categories before being used to compute the correlations familiar in the context of a matched filter. Using the method of the present invention to compute the correlations, and optionally applying additional time-saving techniques described herein, a position determination is achieved using arithmetic operations that are significantly reduced from that required in prior art methods to compute the correlations. The reduced number of arithmetic operations can reduce significantly the power consumption required of a device carrying out a method of the present invention, and thereby realizing a significant advantage.
1. A method for computing a distance from a signal source based on a probe signal transmitted from the signal source and received, comprising:
initializing a probability distribution for the distance; and
repeating the following steps until a predetermined condition is met:
receiving the probe signal for a time interval;
generating a replica of the probe signal;
updating the probability distribution based on the received probe signal and the replica of the probe signal during the time interval;
evaluating the updated probability distribution to determine whether or not the predetermined condition is met.
2. The method of claim 1 , wherein the probability distribution is performed according to the Bayes' Rule.
3. The method of claim 1 , wherein the probability distribution is updated using logarithms of the probability distribution.
4. The method of claim 1 , wherein the received probe signal used in updating the probability distribution comprises digital samples.
5. The method of claim 1 , wherein the received probe signal is modeled as a sum of an error-free signal and a zero-mean noise signal.
6. The method of claim 5 , wherein the error-free signal is further modeled by including a communication channel gain.
7. The method of claim 5 , wherein the zero-mean noise signal has a Gaussian distribution.
8. The method of claim 5 , wherein updating the probability distribution comprises, for each selected value of the distance, summing or integrating over the time interval a product of the received probe signal and the replica of the probe signal, wherein the received probe signal and the replica of the probe signal in the summing or integrating are offset in time by the distance.
9. The method of claim 1 , wherein the probe signal comprises a binary-value probe signal.
10. The method of claim 1 , wherein evaluating the probability distribution comprises finding a peak in the probability distribution.
11. The method of claim 1 , wherein evaluating the updated probability distribution comprises computing an expected value of the distance.
12. The method of claim 1 , further comprises normalizing the updated probability distribution.
13. The method of claim 1 , wherein the predetermined condition relates to a threshold value.