Abstract:
The invention pertains to the sensing and measurement of infrasound (pressure variations with frequency content under 10 Hertz). The invention measures the infrasound pressure variations and then attenuates undesired contributions due to, but not limited to, changes in ambient fluid speed and changes in altitude of the sensing device. The undesired contributions are attenuated using an adaptive filter with the measurements of the change in ambient fluid speed and the change in altitude. This approach differs from existing applications of adaptive filtering in that the invention uses measurements of speed and displacement to improve a pressure measurement. This approach differs from existing methods for attenuating the affects of the wind on the measurement of infrasound in that it does not require the use of a large spatial averaging filter.

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
   This application claims the benefit of Provisional Patent Application No. U.S. 60/656,623, filed 2005 Feb. 26, by the present inventor. 

   FEDERALLY SPONSORED RESEARCH 
   Not Applicable. 
   SEQUENCE LISTING OR PROGRAM 
   Not Applicable. 
   BACKGROUND OF THE INVENTION 
   1. Field of Endeavor 
   The invention pertains to the sensing of, and sensors for the detection of, infrasound (pressure variations with frequency content under 10 Hertz). The present invention concerns a method for removing the effects of undesirable disturbances from the measurement of infrasound. The discussion that follows focuses on air, but the invention is not limited to air as the medium for transmission of infrasound. 
   2. Infrasound Prior Art 
   Infrasound is typically classified as low frequency (under 10 Hertz) pressure waves with long wavelengths (greater than 30 meters in air). These signals are outside the hearing range of humans. These signals have been detected with a variety of devices, including highly sensitive barometers, differential pressure sensors, and fiber optic interferometers. The typical purpose for measuring infrasound is to detect the source of generation of the infrasound. Sources of infrasound range from man-made, such as bridge vibration, nuclear explosions (Comprehensive Test Ban Treaty) and industrial processes, to naturally occurring events, such as: volcanoes, meteorite impact, tidal action, tornadoes, tsunamis, avalanches, earthquakes, ground swell prior to earthquakes, . . . etc. The pressure variations that cause infrasound are small relative to the ambient pressure, thus the detectors must be very sensitive. This sensitivity allows the detector to pick-up pressure changes that are not caused by the source of interest, in particular wind. Much of the current research in infrasound sensing is on techniques for eliminating the wind disturbance from the pressure measurement. 
   The primary technique for reducing the winds contribution to infrasound signal is a spatial filter. A spatial filter involves a distributed measurement. Such a measurement is made over a length or area to average-out local pressure variations due to the localized affects of wind. For pneumatic differential pressure sensors a spatial filter typically involves a network of perforated pipes or permeable hoses that feed a single volume that is used as the source of the differential pressure measurement. Bedard et al. (2004) presented the results from the deployment of such a system. The sensor included a spatial filter for reducing wind-induced pressure fluctuations, which was composed of twelve porous hoses arranged in a diameter of about 50 feet,  FIG. 1 . Zumberge et al. (2003) presents the results of an optical fiber infrasound sensor (OFIS) that measures, interferometrically, pressure-induced strain in a sealed tube. The OFIS system has a length of 89 meters (left,  FIG. 2 ) and is compared to a pneumatic pipe array on the right in  FIG. 2 . The OFIS measurement is distributed over a length. The “line receiver”, implementation of OFIS has directional sensitivity and has a frequency roll-off for signals sources not at 90 degrees to the line (Zumberge et al., 2003). This directional sensitivity is by design. The OFIS is described in U.S. Pat. No. 6,788,417 B1 (May 2004). U.S. Pat. No. 6,788,417 also summarized the prior art on wind noise in infrasound measurements and includes a discussion of the prior air of noise reduction methods using perforated pipes and permeable hoses. In Noble and Tenney (2003), the U.S. Army documents the use of a sensor array with 20-meter spacing to detect impulsive signals in the frequency range of 3–8 Hz. More recently, Stubbs et al, (2005), “examines ways to enhance the effectiveness of infrasound monitoring”, including arranging the filter hoses to increase directional sensitivity. All of the above techniques discuss distributed measurements that achieve wind-borne noise reduction using some type of spatial filtering. 
   A disadvantage of the above approaches to reducing the contribution of the wind to the measurement of infrasound is that the implementation of spatial filtering is relatively large physically. The array in Noble and Tenney (2003) is 20 meters in diameter and the OFIS in  FIG. 2  is 89 meters long. The pneumatic array  FIG. 2  is on the order of 50 meters in diameter. The hardware used for the spatial filters is continuous, not discrete components. The measurement is an average over the length of pipe or hose. These implementations of spatial filtering require real estate proportional to the wavelength of interest, necessitating the location of the sensor in areas where land is available. The land must also be maintained and secured, which requires a recurring labor cost. 
   Another disadvantage of the above approaches is that while in use, these sensors must be stationary. Vertical displacements as a function of time could be perceived as infrasound since atmospheric pressure is correlated with altitude. The distributed sensors with spatial filters assume that the ground, averaged over the sensor length, is fixed and not moving and thus not a contributing factor in the measurement of infrasound. This confines the sensor to a stationary position. 
   Another disadvantage of the above approaches is that because they are large, and in some cases buried underground, they are not easily relocated. Deployment of these sensors is typically permanent. It may be possible to transport the sensor to another location, but the sensor is not easily moved and reused at another location. This precludes the reuse of these sensors at alternative locations. 
   Another disadvantage of the above approaches is that the filtering technique is a function of hardware. These implementations of spatial filtering are a result of the distributed and continuous nature of the sensor hardware. Thus, the filtering technique is fixed and cannot be changed. In Bedard et al. (2004) the filter is a function of the geometry of the permeable hoses. In Zumberge et al. (2003) the filter is a function of the orientation and length of the OFIS. Spatial filtering is essentially an averaging filter, averaged over the length of the OFIS or pipe array. As a filtering technique, averaging is only optimal if the time domain properties are to be maintained, as in a sudden or step change as described in Smith, (1999). If it is desired to maintain frequency domain characteristics of the signal, then filtering techniques other than averaging will lead to improved results. If the signal source of interest is periodic (a tornado for example) then frequency separation of the measured signal is desired. An averaging filter cannot separate one band of frequencies from another because the averaging filter has poor stop-band attenuation properties. For periodic signals an averaging filter is not the right filter choice to get the best frequency separation and the above approaches are locked into the use of an averaging filter. 
   3. Adaptive Filtering Prior Art 
   Adaptive filters have been incorporated into numerous patents, commercial devices and software. The commercial computer program MATLAB® Signal Processing Toolbox from Mathworks, Inc. (Natick, Mass. USA) contains an implementation of a “Kalman Adaptive Filter”. The Masimo SET™ oximeter sensor uses an adaptive filter to remove undesired pulse components from the measure of blood oxygen, (U.S. Pat. No. 6,036,642 and RE38,492E). In Purdon et al (2001), an adaptive filter is used to remove an unwanted signal from an EEG measurement using a head motion sensor. U.S. Pat. No. 6,675,036B2, by Kreger et al (2004) describe a medical imaging devices that uses an adaptive filter to reduce the affects of bio-potential signals caused by a patient&#39;s respiration. In U.S. Pat. No. 5,943,641, Carme, et al (1999) describes a device for “recovering a wanted acoustic signal from a composite acoustic signal including interference components”. Carme et al claims acoustic signals for both the composite and the reference acoustic signals. All of the above adaptive filters use similar techniques. These adaptive filters all use at least one additional measurement. This additional measure is correlated with some undesired component. This additional measure is filtered, then used to reduce the contributions of the undesired component in the primary measurement of interest. In the above patents, it is the combination of signals used in the measurement that is unique, not the adaptive filter algorithm of which there are many. All of the adaptive filter algorithms work similarly to minimize a cost function, typically a mean squared error, such as the Least Mean Squared (LMS) algorithm. 
   BRIEF SUMMARY OF THE INVENTION 
   The present invention takes additional measurements that are correlated with disturbance components and actively attenuates these undesirable contributions to the primary measurement of interest. The present invention measures changes in ambient fluid speed and changes in the sensor altitude and uses these signals to attenuate unwanted signal components in a pressure measurement used to measure infrasound. Adaptive filtering is used to synthesize the correction to be made to the pressure measurement. Adaptive filtering is a known technique. It is the application of adaptive filtering to the measurement of infrasound using speed and displacement as additional measurements that is new and innovative in the present invention. 
   Infrasound is measured using a suitable sensor, typical, but not limited to, a differential pressure sensor. Wind speed is measured using a sensor with an adequate bandwidth, for example an ultrasonic wind sensor. Vertical displacement is measured using any suitable technique, for example twice integrating the signal from a vertical accelerometer. The change in air speed and the change in the vertical displacement signals are used with an adaptive filter to remove components correlated with each of these measurements from the change in pressure signal. The result is a filtered measure of infrasound. 
   The present invention makes measurements at a point. The invention does not make a distributed measurement nor does it require a spatial filter to remove the disturbances caused by the wind. Since the distributed measurement and spatial filter are not required, the physical size of the device is much smaller. The size of the invention is a function of the size of the housing to package the three sensors, a differential pressure sensor, an air speed sensor, and a sensor to detect the change in altitude. Because the device is relatively small, it can be installed in a variety of locations. Real estate is not a major considering for the installation. 
   The present invention removes the contribution caused by changes in altitude from the measurement of infrasound. Therefore, the invention does not need to be fixed to the ground nor does the invention have to assume that the ground is stationary. This allows the invention to be mounted on platforms that move, such as a buoy, a floating platform, or a building. 
   The present invention does not require a spatial filter and is physically relatively small. Thus, the installation of the invention does not have to be permanent. The present invention is transportable and can be moved from site to site. Since the invention is easily deployed, it can be reused at numerous locations. This reduces the total cost for sites that only need the sensor for a short period of time. 
   The present invention makes measurements at a point. The present invention is not a distributed measurement and does not use an averaging filter. However, multiple versions of the present invention can be used in a network or sensor web. The advantage of this sensor grid comprised of discrete nodes is that a variety of grid geometries can be selected. The sensor web would require the relative location of each sensor and would need to have a uniform measurement of time for each sensor. This geometry and time information can be obtain at each node in the grid from the Geosynchronous Position System (GPS) signals. Such a sensor web comprised of multiple, discrete point-sensors has the advantage that the spatial filtering technique is not defined by the hardware. Thus, filters other than an averaging filter could be used. A variety of spatial filters could be used. Directional sensitivity would be a matter of software selection instead of hardware installation. Signal processing techniques suitable for the network geometry, signal type and source, and the type of information to be extracted can be selected. 
   The present invention has the advantage that it allows for a variety of grid geometries in a sensor network. A network of point sensors enables new information to be extracted from the data that was not previously available. For example, wave-front timing and triangulation can be used to estimate direction and distance from the source of the infrasound. 
   The present invention attenuates the affect of changing wind speed and altitude on the measurement of infrasound. With sufficient attenuation, the invention can be vehicle mounted. When the invention is combined with accurate GPS location and timing signals it is possible for the sensor to account for Doppler effects caused by being in motion. 

   
     BRIEF DESCRIPTION OF THE DRAWING 
       FIG. 1  is prior art showing an array of spatial filters used for reducing wind-induced pressure fluctuations in the infrasonic frequency range from Bedard et al. (2004). This figure demonstrates the physical size of pneumatic infrasound sensors. A typical sensor is composed of twelve radial arms with ports at one-foot intervals with a diameter of fifty feet. A typical array of four such sensors would cover an area of 100 meters by 100 meters. 
       FIG. 2  is prior art that compares the size and geometry of the optical fiber infrasound sensor (OFIS) to an infrasonic pipe array sensor in Zumberge et al (2003). The OFIS is 89 feet in length. The pipe array covers an area of about 80 meters by 80 meters. 
       FIG. 3  is a signal model showing how the true infrasound signal is corrupted by the changes in changes in fluid speed and changes in altitude. 
       FIG. 4  is a schematic diagram showing the elements that make up the overall system architecture of the device according to the invention. 
       FIG. 5  is a schematic diagram showing the elements that make up the housing used for the co-located sensors. 
       FIG. 6  is a schematic diagram showing the elements that make up the pneumatic bandpass filter. 
   

   DETAILED DESCRIPTION OF THE INVENTION 
   The invention views the infrasound pressure measurement as having been corrupted by changes in fluid speed and changes in altitude as shown in  FIG. 3 . The invention adds the measurement of the change in fluid speed and change in altitude to the infrasound pressure measurement. The invention uses these additional measurements with an adaptive filter to remove those terms from the infrasound pressure measurement that are correlated to the change in fluid speed and changes in altitude. 
   A system block diagram of the invention is shown in  FIG. 4 . The invention includes a housing  10  that provides a common point of measurement for three sensors. A representation of this housing  10  is shown in  FIG. 5 . The top  11  of the housing is secured to the base  15  via several supports  13 . The housing contains the common measurement points for the change in altitude sensor  12 , the fluid speed sensor  14 , and the pressure port  16  for the differential pressure measurement. The pressure port  16  is connected to a rigid wall pipe or tubing  17 . Structure  18  is used to support the housing from below as shown or from above if the housing is inverted and suspended from  18 . The sensors are protected from debris and wildlife by a suitable cover  19 , which does not have a significant impact on the measurements. The cover  19 , slides over the top to surround the circumference of the housing. The rigid wall pipe or tubing  17  feeds the pneumatic filters shown in  FIG. 6  via the inlet port  21 . 
   In  FIG. 6 , the filter inlet port  21  leads to a “Tee” fitting  22 . The “Tee” splits the pressure path into two filter halves, side A and side B. The filters are shown as simple “RC”, first order, low pass filters with flow resistance and flow capacitance, but could be other types of pneumatic filters. Side A has a flow resistance  23  and flow capacitance volume  25 . Side B has flow resistance  24  and flow capacitance volume  26 . The two filters, A and B, have different time constants represented by showing volume B larger than volume A, but could also be obtain with different flow resistances  23  and  24 . The different in time constants is such that the pressure at the two output ports, Port A  27  and Port B  28 , have the same DC characteristics at 0 Hertz, but the two filters roll-off at different frequencies. 
   In FIG.  6 ., output ports, Port A  27  and Port B  28  are connected to the two ports (A and B) on the differential pressure sensor  110  in  FIG. 4 . The combined effect of the two different low pass filters with different time constants and the differential pressure sensor is a bandpass pneumatic filter of the pressure at inlet  16   FIG. 5 , in the housing. The output from the differential pressure signal is typically an analog electrical signal. In this case the analog signal can be further filtered  210 , if required, to prevent antialiasing prior to digital sampling. Also, the signal can be capacitively coupled to the analog to digital converter to remove any DC bias signal. After filtering, the differential pressure signal is digitally sampled by an analog to digital converter (ADC)  310  in  FIG. 4 . Note that the differential pressure sensor and the analog-to-digital converter must have sufficient dynamic range such that they do not reach operating limits under envisioned altitude and wind speed conditions. Saturation of these signals will prevent the adaptive filter from identifying the proper correlation. At the same time, the differential pressure sensor must have sufficient resolution to be able to measure the signals of interest. Typically this requires a 24-bit analog to digital converter and a differential pressure sensor with a resolution on the order of 0.01 Pascal and a range on the order of 1000 Pascal. The exact sensing requirements are dependent upon the construction of the housing  10 , the rate of changes of fluid speed and altitude, and the infrasound source of interest. 
     FIG. 4  shows a sensor  120  for measuring the change of altitude. A variety of sensors are possible for this measurement. In  FIG. 5  this change of altitude sensor  12  is envisioned as an accelerometer mounted in a vertical orientation. This accelerometer signal could be twice integrated in the analog domain within the bandpass filter  220  in  FIG. 4 . Alternatively, the acceleration signal is twice integrated digitally in the preprocessing section  420 . This acceleration signal is first filtered  220  for antialiasing and to remove DC components and then sampled by the ADC  320 . The net effect of either approach is that a bandpass, filtered, digital measurement of the change in altitude of the sensor housing is available after preprocessing  420 . Note that this displacement measurement must have an accuracy that matches the accuracy of the pressure measurement. As a figure of merit, consider that at sea level a pressure change of 1.0 Pascal is roughly equivalent to an altitude change of 9 centimeters. So if you need to resolve 0.01 Pa, then you need vertical displacement accuracy on the order of 1 millimeter. 
     FIG. 4  contains a block representing a fluid velocity sensor  130 . This sensor is shown as four nodes  14  in  FIG. 5 . that make up two ultrasonic transmitter/receiver pairs. The fluid speed sensor must have sufficient bandwidth and dynamic range. Other examples of fluid speed sensors with sufficient dynamic range are 3-node ultrasonic sensors, hot wire and hot film anemometers. An air mass flow sensor could also be used. Once the air speed signal is converted to an analog value, it is appropriately filtered  230  to remove DC contributions and for antialiasing prior to digitization by the analog to digital converter ADC  330 . After preprocessing  430 , the result is a measure of the contribution to the pressure in the housing at port  16   FIG. 5  due to the change in fluid speed. The details of the preprocessing  430  depend on the housing  10  and need to be determined experimentally. For example for a housing that blocks the movement of air, the speed squared might be used instead of speed to be representative of the dynamic pressure. 
   In  FIG. 4 , the shaded block  100  represents the region where the sensed physical values are converted to electrical signals. Blocks  310 ,  320 ,  330  show the conversion of analog signal to digital signals. The shaded block  400  represents a processing unit and contains the digital signals after the analog to digital conversion processes. Operations within the processing unit  400  are performed via computer software. The preprocessing in  410 ,  420 ,  430  may be just a matter of scaling. In the case of air speed  430 , preprocessing involves the calibration of the relationship between air speed and the differential pressure sensor as mounted in the housing. Blocks  420  and  430  also contain data buffers to store the past history of the terms to be used in the adaptive filter. Digital filters require past terms to calculate the filter output. The buffers provide the past terms. Block  500  represents the adaptive filter algorithm running in software within the processing unit. The adaptive filter attempts to minimize the correlation between the “error” and the filtered speed and altitude terms from Blocks  420  and  430 . The “error” term come from the filtered infrasound. An example of one type of adaptive scheme that can be used is the Kalman adaptive filter. In this case the filter calculations are just the calculation for a linear finite impulse response (FIR) filter, whose filter weights are updated by the adaptation scheme. One possible adaptation scheme is the least mean square error (LMS) algorithm where the algorithm attempts to minimize the mean square error. A wide variety of filters and adaptive algorithms are possible. The final output after processing is a filtered infrasound signal  800 .