IPC분류정보
국가/구분 |
United States(US) Patent
등록
|
국제특허분류(IPC7판) |
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출원번호 |
US-0086916
(2011-04-14)
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등록번호 |
US-8138976
(2012-03-20)
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발명자
/ 주소 |
- Boyer, Pete A.
- Mia, Rashidus S.
- Segall, Edward Joseph
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출원인 / 주소 |
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대리인 / 주소 |
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인용정보 |
피인용 횟수 :
8 인용 특허 :
3 |
초록
▼
A method for improving the results of radio location systems that incorporate weighted least squares optimization generalizes the weighted least squares method by using maximum a posteriori (MAP) probability metrics to incorporate characteristics of the specific positioning problem (e.g., UTDOA). We
A method for improving the results of radio location systems that incorporate weighted least squares optimization generalizes the weighted least squares method by using maximum a posteriori (MAP) probability metrics to incorporate characteristics of the specific positioning problem (e.g., UTDOA). Weighted least squares methods are typically used by TDOA and related location systems including TDOA/AOA and TDOA/GPS hybrid systems. The incorporated characteristics include empirical information about TDOA errors and the probability distribution of the mobile position relative to other network elements. A technique is provided for modeling the TDOA error distribution and the a priori mobile position. A method for computing a MAP decision metric is provided using the new probability distribution models. Testing with field data shows that this method yields significant improvement over existing weighted least squares methods.
대표청구항
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1. A method for use in a wireless location system, comprising: obtaining field data, wherein said field data have baseline or location dependent values to be used in a signal correlation model;analyzing said field data to obtain (1) said signal correlation model and associated measurement parameters
1. A method for use in a wireless location system, comprising: obtaining field data, wherein said field data have baseline or location dependent values to be used in a signal correlation model;analyzing said field data to obtain (1) said signal correlation model and associated measurement parameters, (2) correlation matrix rules, and (3) a model for a priori position;computing weights for the measurements based on an estimated variability of the measurement;using the weights along with the correlation matrix rules to generate a covariance matrix, and computing an inverse covariance matrix;performing an iterative search over a geographical region to find a location with a maximum a posteriori (MAP) metric;determining that a stopping condition has been reached; andreporting the geographic position with the largest MAP metric;wherein said iterative search includes a resolution loop in which a geographic search space resolution is reduced in each iteration and new test points are generated via interpolation, and wherein said iterative search includes a MAP metric computation that uses the covariance matrix, an error model and a measurement parameter table. 2. A method as recited in claim 1, further comprising generating a table providing a mapping between the measurement parameters for a skew and a correlation value for the measurement. 3. A method as recited in claim 1, further comprising generating a table providing a mapping between the measurement parameters for a skew and the number of baselines. 4. A method as recited in claim 3, wherein said performing an iterative search further comprises: determining measurement parameters for each baseline and using the measurement parameters to determine error model parameters; andupdating the error model parameters as a function of the skew. 5. A method as recited in claim 4, wherein the error model parameters are determined from a lookup table or using a parameter fitting model, wherein a summation of the MAP metric is computed using the covariance matrix when the errors have significant correlation, and wherein the summation of the MAP metric is computed using a fast matrix calculation when the errors do not have significant correlation. 6. A method as recited in claim 1, further comprising analyzing the correlation between different receiver ports linking the location receiver to an external antenna, and providing correlation values and rules for their application. 7. A method as recited in claim 1, wherein the search is re-centered at the previous iteration's minimum error point before proceeding, wherein test points within the current geographical region search space are individually searched and a MAP metric is computed for each test point. 8. A method as recited in claim 1, wherein the method is performed in a wireless location system (WLS) employing at least one of: a time of arrival (TOA) location algorithm, an uplink time difference of arrival (TDOA) location algorithm, a downlink TDOA location algorithm, an angle of arrival (AOA) location algorithm, and a hybrid location algorithm. 9. A method as recited in claim 8, wherein the downlink time difference of arrival location algorithm comprises the use of downlink satellite beacons from a global positioning system (GPS). 10. A method as recited in claim 9, wherein the method is performed in a WLS employing an assisted GPS location system. 11. A method as recited in claim 8, wherein the hybrid location algorithm employs uplink signals from a mobile device and downlink signals received at the mobile device. 12. A method as recited in claim 1, wherein the error model comprises an error distribution and the field data are analyzed to obtain the error distribution. 13. A method as recited in claim 12, wherein the measurement parameters are analyzed to identify which measurement parameters cause large changes in the error distribution and to determine ranges and bin sizes for the identified measurement parameters. 14. A method as recited in claim 13, further comprising: applying a coarse modeling step comprising computing values for error model parameters that match the field data; andapplying results of the coarse modeling step to update the error model. 15. A method as recited in claim 14, further comprising applying a fine modeling step and applying results of the fine modeling step to update the error model. 16. A method as recited in claim 15, wherein said fine modeling step comprises accumulating conditional statistics over the field data based on ranges and bin sizes. 17. A method as recited in claim 16, further comprising determining a conditional error by computing a skew ratio based on the mean and standard deviation of the error for each measurement parameter bin and selecting as the conditional error a center of the bin. 18. A method as recited in claim 1, wherein said performing an iterative search further comprises: approximating a common bias by computing a first bias point by assuming Gaussian statistics for the errors and a second bias point by a computation assuming double exponential statistics; anddetermining the common bias by combining the first and second bias points. 19. A method as recited in claim 18, further comprising: determining a conditional error contribution using the common bias, the covariance matrix, the error model, and the measurement parameter table. 20. A method as recited in claim 19, wherein said second bias point is computed by: populating and sorting weight and sample arrays;computing a threshold as ∑i=1NWi/2 to provide the stopping condition; accumulating the terms in order from terms with the smallest to the largest transition point; andat the point where the threshold is reached, returning ΔτK when there are an odd number of terms and otherwise averaging the kth term's transition point with the prior term's transition point.
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