|Publication number||US7768442 B2|
|Application number||US 11/988,821|
|Publication date||Aug 3, 2010|
|Filing date||Jul 18, 2006|
|Priority date||Jul 18, 2005|
|Also published as||CA2615723A1, US7898461, US20090121921, US20100289693, WO2007009175A1|
|Publication number||11988821, 988821, PCT/2006/1013, PCT/AU/2006/001013, PCT/AU/2006/01013, PCT/AU/6/001013, PCT/AU/6/01013, PCT/AU2006/001013, PCT/AU2006/01013, PCT/AU2006001013, PCT/AU200601013, PCT/AU6/001013, PCT/AU6/01013, PCT/AU6001013, PCT/AU601013, US 7768442 B2, US 7768442B2, US-B2-7768442, US7768442 B2, US7768442B2|
|Inventors||Glen Stickley, Bryan Reeves|
|Original Assignee||Groundprobe Pty. Ltd.|
|Export Citation||BiBTeX, EndNote, RefMan|
|Patent Citations (22), Non-Patent Citations (13), Referenced by (6), Classifications (14), Legal Events (8)|
|External Links: USPTO, USPTO Assignment, Espacenet|
The Slope Stability Radar (SSR) is a ground based interferometric radar, designed to detect the precursor movements to slope failure in open cut mines. It achieves this by remotely monitoring the movement of the rock face, and using these movements to predict slope failure. The monitored region is scanned regularly, producing a close to real time assessment of the rock face and allows mine staff to react quickly to changes in the rock face. To achieve maximum safety benefit high accuracy and reliable measurements are required.
A mine using the SSR is reliant upon the measurement data from the system for safety warnings. Any disturbance to the signal could, unless identified, be incorrectly interpreted as a wall movement. Haul trucks and other mining vehicles can block the path between the SSR and target section of wall. Mine plant equipment, such as pumps and lighting plants may be parked in regions of the image. Grass and other vegetation may also be growing on the face in the field of view of the radar. Even the air itself between the SSR and the wall can appear to induce movements when there is a change in atmospheric pressure, humidity or temperature. This affects the refractive index of the air and thus the speed of the radar wave through the medium.
All these effects reduce the precision of the measurements. The regional disturbances of the haul trucks often disguise the true movements in those areas. Vegetation causes random fluctuations in the signal, which could be interpreted as movement. If the reduced measurement precision of some areas is left unidentified, the user's confidence for other more stable areas on the wall will be reduced. Finally atmospheric changes appear to produce global movements of the whole wall which, if left uncompensated, would appear as fluctuating movements of the wall, and not allow the true movements to be detected. An even worse phenomenon for atmospherics is that they can also produce a permanent change to the measured displacement due to signal ambiguity issues resulting from the measurement method. The result of these effects can be a lack of trust by the user in the measurements made by the SSR. To regain the trust of the user these effects need to be corrected or removed, or at least identified and displayed to the user.
To allow understanding and identification of these disturbances it is important to understand the method of operation of the SSR. This has been described previously in our granted U.S. Pat. No. 6,850,183. The SSR uses the phase of the returned signal to determine the movement of a wall slope. As explained in Reeves B. A. et al., “Developments in Monitoring Mine Slope Stability using Radar Interferometry”; IEEE Publication 0-7803-6359-0/00, pp. 2325-2327 and Bamler R. et al., “Synthetic aperture radar interferometry”, Inverse Problems. Vol 14, pp 1-54 1998, phase change can be converted to displacement using the following formula:
where Δd is the displacement, Δø is the measured change in phase, λ is the wavelength of the carrier frequency of the radar (32 mm) and n is an integer unknown.
The parameter “n” corresponds to the number of wavelength cycles the target has moved between scans. For small time intervals this is assumed to be 0. As the phase is a number between +/−π, the result is that for the measured distance change to be correct the actual distance change needs to be less than +/−λ/4 or +/−8 mm. As a result, processing methods need to be both robust for removal of disturbances to the phase change (and thus displacement) measurement, as well as being able to improve the accuracy of the estimation of the unknown “n” within the displacement calculation formula.
One of the processing techniques described in U.S. Pat. No. 6,850,183 to improve signal quality was atmospheric correction. In this technique, a single reference section of wall was used to determine the atmospheric effect on the signal. This calculated correction was then applied to the remainder of the wall. Simple processing based on changes in amplitude and phase for detection of vegetation and other spurious signals, such as trucks, was also discussed.
The problems with the known technique for disturbance rejection include:
Identifying and rejecting these disturbances will significantly improve the precision of the SSR, increase confidence in the measurement accuracy, and reduce the number of false positive alarms. This will in turn improve the acceptance of the SSR technology leading to improved mine safety.
In one form, although it need not be the only or indeed the broadest form, the invention resides in an anomaly detection and correction module for a slope monitoring system comprising:
an atmospheric correction module that corrects slope movement measurements for anomalies caused by atmospheric changes; and
a disturbance detection module that identifies disturbances that cause errors in the slope movement measurements.
The disturbance detection module suitably masks regions affected by the errors.
In another form the invention resides in a method of error handling in interferometric signal processing for a slope monitoring system including the steps of:
extracting uncorrected movement data from interferometric radar measurements;
correcting the movement data for changes in atmospheric conditions;
identifying disturbances in the corrected movement data; and
displaying the corrected movement data and regions affected by the disturbances.
Suitably the regions affected by the disturbances are masked.
Disturbances are suitably blocking disturbances caused by short-term blockage of the radar signal, for example by trucks and other equipment, and random disturbances caused by long-term interference, for example by vegetation.
Atmospheric correction is suitably achieved by estimating the change in the signal speed due to changes in the refractive index of the air and the offset induced at zero range.
Identifying disturbances in the movement data is suitably achieved by detecting variations in short-term and long-term signal coherence.
In a further form the invention resides in a method of atmospheric correction of movement data comprising multiple data points in a slope monitoring system including the steps of:
selecting a plurality of atmospheric correction regions at different ranges;
deducing displacement data within the selected regions;
determining a search space;
calculating a cost function for a grid of points within the search space;
using a minimisation algorithm to determine a correction slope and offset; and
applying the correction slope and offset to the movement data.
Suitably the step of a using a minimisation algorithm includes calculating local minima within the search space and using the local minima to seed a multidimensional minimisation algorithm to find true minima.
In a yet further form the invention resides in a method of identifying disturbances of movement data comprising multiple data points in a slope monitoring system including the steps of:
determining short-term coherence for each data point;
averaging the short-term coherence to determine long-term coherence;
comparing the long-term coherence to a first threshold and masking the data point if the long-term coherence is less than the first threshold; and
comparing a ratio of the short-term coherence to the long-term coherence with a second threshold and masking the data point if the ratio is less than the second threshold.
The method may further include the step of comparing signal amplitude of the data point with a sky threshold and classifying the data point as sky if the amplitude is less than the sky threshold.
Signals from the radar module 102 are processed in the peak detection and deformation module 106 to convert them from radar range profile to phase and amplitude changes for the strongest signals for that footprint. From this the displacement data can be extracted. This data is then passed to the anomaly detection and correction module 112. The purpose of this module is to either eliminate, reduce or at least detect anomalies in the data caused by external effects on the system. These disturbances include changes in the atmosphere, mine vehicles such as trucks as well as vegetation on the wall face. Without this module significant false movement can appear in the deformation images produced by the SSR unit, leading to a false assumption that the wall is moving or the obscuration of actual wall movements. Both are failures of the system in the eyes of the user. Module 112 uses the displacement data and signal amplitudes to determine if the deformation is a true measure of the wall movement. It does this via two steps; firstly the effect of the atmosphere is identified and corrected in the atmospheric correction module 107. Without atmospheric correction, fluctuations of up to 30 mm can occur in the deformation images. Considering a typical system measurement accuracy is less than 1 mm, this error is completely unacceptable. Secondly, more localised disturbances are identified and eliminated from the image in the disturbance detection module 108. Without this trucks can temporarily disturb the signal, inducing jumps in the deformation data, which are difficult to identify in comparison to stepped wall movements. The result of module 112 is a clean signal, free from external disturbances allowing the user to see when true wall movements occur.
The inventors have found that in many cases the effect of offset is less important so the offset can be constrained to zero. Another approach is to determine the offset separately, for example by measuring the radar reflection from the feedhorn, thereby eliminating influences other than the radar electronics.
Firstly, the displacements within the selected atmospheric regions (see
The requirement to have an indication of the atmospheric region at a number of different ranges is to allow the slope and offset to be estimated at a number of ranges. This is because an estimate at close range will be less accurate than an estimate at a far range, but is also less likely to have the wrapping issue discussed below with reference to
One process for estimating a line which best fits the given data points can be understood by reference to
Another issue that must to be managed within the algorithm is to ensure that the slope and offset estimate stays within given limits. If there is no limit placed on the slope it is possible that the wrapping could increase to a point where there may be a solution where a highly wrapped line will fit through almost all of the points. This slope however is not a correct estimate of the ppm change due to atmospherics. To limit the offset and slope, an initial estimate 501 of the offset and slope is used. This can be determined via the previous offset and slope, or could be calculated from atmospheric measurements from external weather sensing module 103. This estimate is firstly used to limit the search space. Secondly, it is used to calculate a cost function weighting 502. Atmospheric measurements can also be used to correct long term errors due to movement of the atmospheric region.
As well as changes in the atmosphere between the radar and the target, other external disturbances can occur. These can be classified into two types, blocking disturbances and random disturbances. Objects that block the radar beam for a short period of time cause blocking anomalies. In the mining context, these include haul trucks, other mining vehicles and mine personnel. Objects that induce a continuous disturbance to the beam cause random disturbance. They include vegetation on the face such as grass and trees, as well as permanent vibrating equipment such as water pumps and lighting plants. A random disturbance is also a very rapidly moving wall, where the rock face is throwing rocks or material as it accelerates to failure.
A measure which is key to the identification and separation of these different anomalies is the interferometric coherence (or complex correlation coefficient). This is defined by Bamler (referenced above) as:
γ=E[u 1 u 2*]/√(E[|u 1|2 ]E[|u 2|2]) 
where E is the expected value, u1 is the complex radar signal at time 1, u2 is the complex radar signal at time 2 and γ is the resultant interferometric coherence. The interferometric coherence is between 0 and 1 where 1 is high coherence and 0 is low coherence.
This is calculated for a zone around the signal peak for each returned echo from the wall. Identification of regions that contain blocking disturbances and random disturbances allows these regions to be highlighted as unreliable and disregarded for alarm purposes. As will be evident by reference to
It is convenient to use hysteresis in the threshold calculation for all of these decisions, ensuring a point close to the threshold point will not flicker between masked and not masked. This improves the user's confidence in the masking decision.
The methods of interferometric signal processing correct for anomalies and disturbances in the measured displacement data caused by atmospheric variation, blocking disturbances such as heavy equipment and random disturbances such as vegetation. These corrections enhance the precision of the displacement data leading to more reliable alarms and hence greater user confidence. Throughout the specification the aim has been to describe the preferred embodiments of the invention without limiting the invention to any one embodiment or specific collection of features.
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|U.S. Classification||342/28, 342/156, 342/179, 342/114|
|International Classification||G01S13/89, G01S19/44, G01S13/62, G01S19/46|
|Cooperative Classification||G01S13/88, G01S7/414, G01S13/89|
|European Classification||G01S13/88, G01S7/41B, G01S13/89|
|Feb 5, 2008||AS||Assignment|
Owner name: GROUNDPROBE PTY LTD (ACN 095 991 549), AUSTRALIA
Free format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNORS:STICKLEY, GLEN;REEVES, BRYAN;REEL/FRAME:020466/0278
Effective date: 20060710
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|Aug 3, 2014||REIN||Reinstatement after maintenance fee payment confirmed|
|Sep 1, 2014||PRDP||Patent reinstated due to the acceptance of a late maintenance fee|
Effective date: 20140902
|Sep 2, 2014||FPAY||Fee payment|
Year of fee payment: 4
|Sep 2, 2014||SULP||Surcharge for late payment|
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Effective date: 20140803