Method and system for identification and mitigation of errors in non-line-of-sight distance estimation

• US 20110177786A1
• Filed: 03/30/2009
• Published: 07/21/2011
• Est. Priority Date: 03/30/2009
• Status: Active Grant

• Current Assignee: Massachusetts Institute of Technology
• Original Assignee: Massachusetts Institute of Technology
• Inventors: Win, Moe Z., Wymeersch, Henk A.H.E., Gifford, Wesley M., Marano, Stefano
• Granted Patent: US 9,002,286 B2
• Time in Patent Office: Days
• US Class Current: 455/67.11
• CPC Class Codes: H04B 17/24 with feedback of measurements to the transmitter
• CPC Class Codes: H04B 17/27 for locating or positioning the transmitter
• CPC Class Codes: H04B 17/309 Measuring or estimating channel quality parameters
• CPC Class Codes: H04B 17/373 Predicting channel quality or other radio frequency [RF] parameters
• CPC Class Codes: H04B 17/391 Modelling the propagation channel
• CPC Class Codes: H04W 4/023 using mutual or relative location information between multiple location based services [LBS] targets or of distance thresholds

Abstract

Ultra-wide bandwidth (UWB) transmission is a promising technology for indoor localization due to its fine delay resolution and obstacle-penetration capabilities. However, the presence of walls and other obstacles present a significant challenge in terms of localization, as they result in positively biased distance estimates. Measurement campaigns with FCC-compliant UWB radios can quantify effects of non-line-of-sight (NLOS) propagation. Features of waveforms measured during a campaign can be extracted for use in distinguishing between NLOS and line-of-sight situations in embodiments of the present invention. Embodiments further include classification and regression methods based on machine learning that improve the localization performance while relying solely on the received signal. Applications for systems employing an example embodiment of the invention include indoor or outdoor search and recovery with high accuracy and low cost.

Claims

1. A method of characterizing a medium, the method comprising:

learning a function, the function being nonparametric with respect to features representing aspects of a waveform transmitted via a medium between a first wireless device and a second wireless device;

characterizing the medium by evaluating the function with a feature vector extracted from a waveform received at the first wireless device from the second wireless device to make a decision regarding a presence of an obstruction between the first and second wireless devices or to determine a likelihood of an obstruction between the first and second wireless devices; and

reporting the decision or likelihood.

2. The method as claimed in claim 1 wherein learning a function is performed using machine learning techniques including, but not limited to:

support vector machine techniques and Gaussian processes techniques.

3. The method as claimed in claim 1 wherein the function is chosen from a class of functions including, but not limited to:

polynomials and linear combinations of kernel functions.

4. The method as claimed in claim 1 wherein the learning includes accessing data in a training database assembled from waveforms transmitted between at least one pair of training wireless devices positioned in a model environment, the training database including at least line-of-sight/non-line-of-sight labels and feature vectors representing aspects of the waveforms transmitted between the training wireless devices.

5. The method as claimed in claim 1 wherein the first and second wireless devices are matched pairs of wireless devices employing compatible transceivers.

6. The method as claimed in claim 1 wherein the features representing aspects of a waveform include, but are not limited to:

estimated distance;

energy of a received signal;

maximum amplitudes;

rise time;

mean excess delay;

root-mean-square delay spread; and

kurtosis.

7. An apparatus for characterizing a medium, the apparatus comprising:

a learning unit configured to learn a function that is nonparametric with respect to features representing aspects of a waveform transmitted via a medium between a first wireless device and a second wireless device;

an evaluating unit configured to characterize the medium by evaluating the function with a feature vector extracted from a waveform received at the first wireless device from the second wireless device to make a decision regarding a presence of an obstruction between the first and second wireless devices or to determine a likelihood of an obstruction between the first and second wireless devices; and

a reporting unit configured to report the decision or likelihood.

8. The apparatus as claimed in claim 7 wherein the learning unit learns the function uses machine learning techniques including, but not limited to:

support vector machine techniques and Gaussian processes techniques.

9. The apparatus as claimed in claim 7 wherein the function is chosen from a class of functions including, but not limited to:

polynomials and linear combinations of kernel functions.

10. The apparatus as claimed in claim 7 further including a training database assembled from waveforms transmitted between at least one pair of wireless devices positioned in a model environment, the training database being configured to provide line-of-sight/non-line-of-sight labels and feature vectors representing aspects of waveforms transmitted between the training wireless devices to the learning unit.

11. The apparatus as claimed in claim 7 wherein the first and second wireless devices are matched pairs of wireless devices employing compatible transceivers.

12. The apparatus as claimed in claim 7 wherein the features representing aspects of a waveform include, but are not limited to:

estimated distance;

energy of a received signal;

maximum amplitudes;

rise time;

mean excess delay;

root-mean-square delay spread; and

kurtosis.

13. A method of estimating error in distance measurements, the method comprising:

learning a function based on features representing aspects of a waveform transmitted between training wireless devices and a true distance between the training wireless devices;

estimating error in a distance measurement by evaluating the function for a feature vector extracted from a waveform received at a first wireless device from a second wireless device to determine an estimate of distance estimation error or a distribution of distance estimation error of a distance estimate between the first and second wireless devices; and

reporting the estimate of distance estimation error or the distribution of the distance estimation error to a module making use of the estimate of distance estimation error or the distribution of the distance estimation error.

14. The method as claimed in claim 13 further comprising mitigating error in the distance estimate by subtracting the estimate of the distance estimation error from the distance estimate.

15. The method as claimed in claim 13 wherein the features representing aspects of a waveform or feature vector include, but are not limited to at least one of the following features:

estimated distance;

energy of a received signal;

maximum amplitude of the received signal;

rise time;

mean excess delay;

root-mean-square delay spread; and

kurtosis.

16. The method as claimed in claim 13 wherein learning a function is performed using machine learning techniques including, but not limited to:

support vector machine techniques and Gaussian processes techniques.

17. An apparatus for estimating error in distance measurements, the apparatus comprising:

a learning unit configured to learn a function based on features representing aspects of a waveform transmitted between training wireless devices and a true distance between the training wireless devices;

an evaluating unit configured to estimate error in distance measurements by evaluating the function for a feature vector extracted from a waveform received at a first wireless device from a second wireless device to determine an estimate of distance estimation error or a distribution of distance estimation error of a distance estimate between the first and second wireless devices; and

a reporting unit configured to report the estimate of distance estimation error or the distribution of the distance estimation error to a module making use of the distance estimation error or the distribution of the distance estimation error.

18. The apparatus as claimed in claim 17 further including a mitigation unit configured to mitigate error in the distance estimate by subtracting the estimate of the distance estimate error from the distance estimate.

19. The apparatus as claimed in claim 17 wherein the features representing aspects of a waveform or feature vector include, but are not limited to at least one of the following features:

energy of a received signal;

maximum amplitude of the received signal;

rise time;

mean excess delay;

root-mean-square delay spread; and

kurtosis.

20. The apparatus as claimed in claim 17 wherein the learning unit learns the function using machine learning techniques including, but not limited to:

support vector machine techniques and Gaussian processes techniques.

21. A method of supporting distance estimation between wireless devices, the method comprising:

evaluating an identification function to determine a presence or likelihood of an obstruction of a wireless waveform transmitted between wireless devices;

evaluating a mitigation function based on a result of evaluating the identification function; and

based upon a result of evaluating the mitigation function, providing an estimate of distance estimation error or a distribution of distance estimation error of an estimate of a distance between wireless devices to support distance estimation between wireless devices.

22. The method as claimed in claim 21 further comprising:

learning the identification function using line-of-sight/non-line-of-sight labels of waveforms received in a training environment; and

learning the mitigation function using true distances of waveforms received in a training environment.

23. The method as claimed in claim 21 further including:

correcting the estimate of the distance between wireless devices based on a result of evaluating the mitigation function.

24. The method as claimed in claim 21 further including:

localizing at least one of the wireless devices using the estimate of distance estimation error or the distribution of distance estimation error.

25. The method as claimed in claim 21 wherein evaluating the identification and mitigation functions occurs in one of plural layers in a communications network between wireless devices;

and

reporting the estimate of distance estimation error or the distribution of distance estimation error includes forwarding the distance estimation error or the distribution of distance estimation error to a different layer in the communications network.

26. The method as claimed in claim 21 wherein evaluating the identification function includes providing labels identifying a received waveform as being line-of-sight (LOS) or non-line-of-sight (NLOS) and evaluating the mitigation function is based on whether the label identifies the received waveform as being NLOS.

27. An apparatus of supporting distance estimation between wireless devices, the apparatus comprising:

an identification unit configured to evaluate an identification function to determine a presence or likelihood of an obstruction of a wireless waveform transmitted between wireless devices;

a mitigation unit configured to evaluate a mitigation function based on a result of evaluating the identification function; and

a reporting unit configured to provide, based upon a result of evaluating the mitigation function, an estimate of distance estimation error or a distribution of distance estimation error of an estimate of a distance between wireless devices to support distance estimation between wireless devices.

28. The apparatus as claimed in claim 27 further comprising:

an identification training database coupled to the identification unit, the identification training database including at least line-of-sight/non-line-of-sight labels of waveforms received in a training environment; and

a mitigation training database coupled to the mitigation unit, the mitigation training database including at least true distances of waveforms received in a training environment.

29. The apparatus as claimed in claim 27 further including:

a correction unit configured to correct the estimate of the distance between wireless devices based on a result of evaluating the mitigation function.

30. The apparatus as claimed in claim 27 further including:

a localization unit configured to localize at least one of the wireless devices using the estimate of distance estimation error or the distribution of distance estimation error.

31. The apparatus as claimed in claim 27 wherein the identification and mitigation units operate in one of plural layers in a communications network between wireless devices;

and

wherein the reporting unit forwards the estimate of the distance estimation error or the distribution of distance estimation error to a different layer in the communications network.

32. The apparatus as claimed in claim 27 wherein the identification unit is further configured to provide a label identifying a received waveform as being line-of-sight (LOS) or non-line-of-sight (NLOS) and the mitigation unit is further configured to evaluate the mitigation function based on whether the label identifies the received waveform as being NLOS.