Abstract:
A present novel and non-trivial system and methods for exacting terrain elevation data contained in a plurality of terrain elevation databases or data sets, combining the terrain elevation data contained in each terrain elevation data set to produce a single, combined terrain database of a common resolution. A system is disclosed that could provide a processor with terrain elevation data from a plurality of terrain elevations data sets having differing resolution and accuracy parameters, a per-point error metric data from a plurality of error metric data sets, and a common resolution parameter. A method is disclosed which for combining terrain elevation data sets having difference resolutions into a single, combined terrain elevation data set having a common resolution utilizing a per-point error metric data from a plurality of error metric data sets. A second method is disclosed for combining a plurality of terrain elevation data sets having different resolutions into terrain elevation data of a single, combined terrain elevation data set having a common resolution by transforming the plurality of terrain elevation data sets into a plurality of terrain frequency data sets and filtering the terrain frequency data sets utilizing a Kalman filter.

Description:
BACKGROUND OF THE INVENTION 
     1. Field of the Invention 
     This invention pertains to terrain elevation databases as applied in the field of avionics. The embodiments herein contain a system and methods for combining a plurality of terrain elevation databases having varying resolutions and parameters into a single database of a common resolution. 
     2. Description of the Related Art 
     Beginning in the early 1970&#39;s, a number of studies looked at the occurrence of “controlled flight into terrain” (CFIT)-type accidents, where a properly functioning airplane under the control of a fully qualified and certificated crew is flown into terrain (or water or obstacles) with no apparent awareness on the part of the crew. Findings from these studies indicated that many such accidents could have been avoided if a warning device called a ground proximity warning system (“GPWS”) was used. Thereafter, advances in terrain mapping technology permitted vendors and designers of avionics equipment to develop newer type of GPWS that provides greater situational awareness for flight crews. The U.S. Federal Aviation Administration (“FAA”) has classified such systems as Terrain Awareness and Warning Systems (“TAWS”). 
     The advancement of technologies—more precise navigation systems, increased computer memory storage, and better display technology—have allowed further development of in the common features of TAWS: (1) use of airplane position information from the aircraft&#39;s navigation system(s), (2) an onboard terrain database, and (3) a means of displaying the surrounding terrain. Aircraft position information from the aircraft&#39;s navigation system is fed to a TAWS computer. The TAWS computer compares the airplane&#39;s current position and flight path with the terrain database associated with the system. If there is a potential threat of collision with terrain, the TAWS computer sends warning alerts to the airplane&#39;s audio system. 
     Regarding onboard terrain databases, various vendors and designers of avionics equipment have developed databases that have been, for all intents and purposes, proprietary in nature. Past and recent advances have made significant efforts to obtain digital elevation models of the Earth, generating a high-resolution digital topographical database. Governing authorities have been a primary source of gathering and compiling terrain elevation data into terrain databases on a large scale. For instance, the U.S. Geological Society (“USGS”) developed GTOPO30, a global digital elevation model (“DEM”). GTOPO30 terrain elevation data was derived from eight sources of data including Digital Terrain Elevation Data (“DTED”), Digital Chart of the World (“DCW”), USGS Digital Elevations Models (“DEM”), Army Map Services (“AMS”) Maps, International Map of the World (“IMW”), Peru Map, New Zealand DEM, and Antarctic Digital Database (“ADD”). The release of GTOPO30 represented the completion of global coverage of low-resolution elevation data; the resolution of GTOPO30 is generally thirty arc seconds (or approximately one kilometer). While the whole coverage of the terrain elevation data is positive, the low-resolution elevation data is not advantageous for a system such as TAWS because changes in terrain are reported at a one kilometer interval. 
     In February 2000, the Space Shuttle Endeavour acquired elevation data on a near-global scale during mission STS-99. In an international research effort known as Shuttle Radar Topography Mission (“SRTM”), a DEM was generated producing the most complete high-resolution digital topological database of Earth to date; the resolution is generally one arc second (or approximately 30 meters), thereby providing much greater detail than that provided with GTOPO30 data set. While the resolution of the terrain elevation data set is positive, there are three significant drawbacks of SRTM. First, the coverage extended from 56° South latitude to 60° North latitude only. Second, the SRTM terrain elevation data set is affected by mountainous and desert areas. Mountain summits of the Alpine and Andes ranges, and many gorges and canyons contain voids in the data. Third, at the present time, the resolution of one arc second is available over the United States territory only; the rest of the world is available at three arc seconds resolution. 
     The three-dimensional DEM data may also be categorized as a set of frequencies in two dimensions for the purposes of digital signal processing. One dimension may be North and the other may be East. Data characterized in this manner will have differences in frequency content. For example, GTOPO30 data set provides a very smooth surface with very few abrupt changes in elevations; it is low resolution, continuous, and virtually free of high-frequency noise. On the other hand, SRTM terrain elevation data set is high resolution and often contains high-frequency noise that may have been caused by radar artifacts of the Interferometric Synthetic Aperture Radar technique employed during the Shuttle mission to acquire the SRTM data. 
     Accordingly, each set of a plurality of terrain elevation data sets have advantages and disadvantages associated with it. 
     BRIEF SUMMARY OF THE INVENTION 
     The embodiments disclosed herein present novel and non-trivial system and methods for exacting terrain elevation data contained in a plurality of terrain elevation databases or data sets, combining the terrain elevation data contained in each terrain elevation data set to produce a single, combined terrain database of a common resolution. 
     In one embodiment herein, a system is disclosed that could provide a processor with terrain elevation data from a plurality of terrain elevations data sets having different resolutions and accuracy parameters, per-point error metric data from a plurality of error metric data sets, and a common resolution parameter. With such input, the processor combines the terrain elevation data from the plurality of terrain elevation data sets to form a single, combined terrain elevation data base set with having common resolution. 
     In another embodiment herein, a method is disclosed which for combining terrain elevation data sets into a combined terrain elevation data set having a common resolution by determining a weighted average of the plurality of terrain data sets. This method could provide the combined terrain elevation data set after receiving as input a plurality of terrain datasets having different resolutions and file formats, a common resolution parameter, and a plurality of error metric data sets for use as weights. 
     In another embodiment herein, a second method is disclosed for combining a plurality of terrain elevation data sets into a combined terrain elevation data set having a common resolution by utilizing a Kalman filter to filter out noise that may accompany terrain data sets. This method could provide the combined terrain elevation data set after receiving as input a plurality of terrain datasets having different resolutions and file formats, and a common resolution parameter. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  depicts a block diagram of a system for combining a plurality of terrain elevation databases or data sets into a single terrain elevation database having a common resolution. 
         FIG. 2  depicts a flowchart illustrating a per-point error metric method for combining terrain elevation data from a plurality of terrain elevation data sets into terrain elevation data of a single, combined terrain elevation data set having a common resolution. 
         FIG. 3  depicts a flowchart illustrating a Kalman filter method for combining terrain elevation data from a plurality of terrain elevation data sets into terrain elevation data of a single, combined terrain elevation data set having a common resolution. 
     
    
    
     DETAILED DESCRIPTION OF THE INVENTION 
     In the following description, several specific details are presented to provide a thorough understanding of embodiments of the invention. One skilled in the relevant art will recognize, however, that the invention can be practiced without one or more of the specific details, or in combination with other components, etc. In other instances, well-known implementations or operations are not shown or described in detail to avoid obscuring aspects of various embodiments of the invention. 
       FIG. 1  depicts a system  100  for combining a plurality of terrain databases into one database which is suitable for implementation of the techniques described herein. The system may be comprised of a plurality of terrain elevation data sets  110 - 1  to  110 -N (collectively, singular or plural, “terrain data sets  110 ”), a plurality of per-point error metric data  118 - 1  to  118 -N (collectively, “per-point error metric data  110 ”), a common resolution parameter  120 , a processor  122  and combined terrain elevation data set of database  124 . 
     Terrain elevation data sets  110  may include a plurality of terrain elevation databases containing terrain elevation data  112 - 1  to  112 -N sets (collectively, “terrain elevation data  112 ”), accuracy parameters  114 - 1  to  114 -N (collectively, “accuracy parameters  114 ”), and resolution parameters  116 - 1  to  116 -N (collectively, “resolution parameters  116 ”). Terrain elevation data sets may be stored in files having differing formats. Terrain elevation data sets  110 , terrain elevation data  112 , and all other data sets and data discussed herein may consist of software or source code stored, permanently or temporarily, in a digital memory storage device or computer-readable media including, but not limited to, RAM, ROM, CD, DVD, hard disk drive, diskette, solid-state memory, PCMCIA or PC Card, secure digital cards, and compact flash cards. 
     Terrain elevation data  112  could be that data which is contained in terrain data sets  110  as contained in digital elevation models. Digital elevation models have been produced by private sources and governmental authorities and have recently become widely available to the public as a whole without cost or for a nominal fee. Examples illustrative of digital elevation models containing terrain data sets  110  include, but are not limited to, SRTM, GTOPO30, DTED, DCW, DEM, AMS maps, IMW maps, Peru map, New Zealand DEM, and ADD. As embodied herein, terrain elevation data  112  could be provided to processor  122  for subsequent processing. 
     As embodied in  FIG. 1 , accuracy parameters  114  could be values that reflect the accuracy of the terrain elevation data  112  associated with digital elevation models. Accuracy parameters  114  may be expressed as a linear error at a confidence level. The range of accuracy parameters  114  for each digital elevation model varies widely. To illustrate, an accuracy parameter  114  of DCW has been stated as plus of minus 650 meters at 90 percent confidence level, and DTED and DEM have been stated as plus or minus 30 meters linear error at the 90 percent confidence level. Although each terrain elevation data set  110  is shown as having one accuracy parameter, the set may have a plurality of accuracy parameters within the set. As such, accuracy parameters  114  is not intended to be narrowly construed but to encompass all accuracy parameters that may be associated with a terrain elevation data. As embodied herein, accuracy parameters  114  could be provided to processor  122  for subsequent processing. 
     As embodied in  FIG. 1 , resolution parameters  116  could be values that reflect the resolution of the terrain elevation data  114  associated with digital elevation models. Resolution parameters may be expressed in distance or arc seconds (an arc second being approximately equal to 30 meters). The range of resolution parameters  116  for each digital elevation model varies widely. To illustrate, a resolution parameter  116  of GTOPO30 has been stated as 1 kilometer, DTED and DEM as 3 arc seconds, and SRTM as 1 arc second over United States territory and 3 arc seconds for the rest of the world. Although each terrain elevation data set  110  is shown as having one resolution parameter, the set may have a plurality of resolution parameters within the set. As such, resolution parameters  116  is not intended to be narrowly construed but to encompass all resolution parameters that may be associated with a terrain elevation data. Resolution parameters  116  could be provided to processor  122  for subsequent processing. 
     In an embodiment herein, error metric data sets (not depicted) include per-point error metric data  118 - 1  to  118 -N sets (collectively, “per-point error metric data  118 ”). Per-point error metric data  118  may include a plurality of values that correspond to the plurality of values comprising terrain elevation data  112 , and for each value found in terrain elevation data  112  there could be a corresponding per-point metric value. For example, terrain elevation data  112  that could make up a terrain data set  110  consisting of a 10×10 matrix may have a corresponding 10×10 matrix comprising an error metric data set of per-point error metric data  118 . 
     For the purposes of illustrating an embodiment of this invention, assume there are three terrain data sets  110 . The terrain elevation data  112 - 1  of a first terrain data set  110 - 1  could be low quality, and the per-point metric data  118 - 1  of a first error metric set may each be assigned a value of 0.10. Likewise, the terrain elevation data  112 - 2  of a second terrain data set  110 - 2  could have better quality than the first, and the per-point metric data  118 - 2  of a second error metric set may each be assigned a value of 0.30. The terrain elevation data  112 - 3  of a third terrain data set  110 - 3  could have the best quality of the three, and the per-point metric data  118 - 3  of a third error metric set would each be assigned a value of 0.60. It should be noted that each value of per-point metric data within an error metric data set may differ, but corresponding values across the plurality of error metric data sets  118  should be normalized to one. 
     As embodied in  FIG. 1 , a common resolution parameter  120  is received by the system  100 . A common resolution parameter  120  could be resolution that will be common to a combined terrain elevation database  124 . A processor  122  could receive as input terrain elevation data  112 , accuracy parameters  114 , resolution parameters  116 , per-point error metric data  118 , and a common resolution parameter  120 . In an embodiment herein, the processor  124  could conform resolution parameters  116  into the common resolution. In another embodiment, processor  124  could weight the terrain elevation data  112  of the terrain data sets  110  with the per-point metric data  118  of the error metric data set and combine the weighted terrain elevation data  112  to produce a combined terrain data set or database  124  of common resolution. In another embodiment herein, the processor  122  could transform the terrain elevation data  112  of the terrain data sets  110  into terrain frequency data (not shown) of terrain frequency sets (not shown) comprising a set of frequencies in two dimensions. In another embodiment herein, the processor  122  could filter the terrain frequency data of the terrain frequency sets utilizing a Kalman filter to produce a combined terrain data set or database  124  of a common resolution. 
     It should be noted that the processor  122  may comprise of any electronic data processing unit which executes software or source code stored, permanently or temporarily, in a digital memory storage device or computer-readable. A common example of an electronic data processing unit is a microprocessor; however, for the embodiments herein, the term processor is not limited to the microprocessor and its meaning is not intended to be construed narrowly. For instance, a processor could also consist of more than one electronic data processing units. 
       FIG. 2  depicts flowchart  200  of an example of a method for combining a plurality of terrain databases into one terrain database. The flowchart begins with module  202  with the receiving of a plurality of terrain data sets  110 . In an embodiment, each of the plurality of terrain data sets  110  may be stored in different file formats. The flowchart continues to module  204  with the receiving of a common resolution parameter  120 , a parameter which could be the resolution of a combined terrain data set or database  124 . The flowchart continues to module  206  with the conforming of the plurality of terrain data sets  110  to a common resolution  120 . In an embodiment, if any of the plurality of terrain data sets  110  is stored in different file formats, the conforming step of the method could include the conversion of the differing file formats into one. In addition, this method could determine those databases that do not have the same resolution of the common resolution  120  and modify their respective resolutions either upward or downward so that they may match the resolution that will be common to the combined database. 
     The flowchart continues to module  208  with the receiving of a plurality error metric sets  118 . Each error metric set  118  may correspond to a terrain data set  110  and include per-point metric data, and each error metric set  118  may correspond to one of the plurality of terrain data sets  110 . The flowchart continues to module  210  with the application of each error metric set  118  as a weighting factor to its corresponding one of the plurality of conformed terrain data sets  110 . The flowchart continues to module  212  with the combining via summation of the plurality of weighted, conformed terrain data sets into one weighted terrain data set. The flowchart continues to module  214  with the normalizing of the weighted terrain set if the sum of the plurality of error metric data sets is greater than one; if so, then normalization could include dividing the weighted data set by the sum of the error metric data sets. Then, the flowchart proceeds to the end. 
       FIG. 3  depicts flowchart  300  of an example of a method for combining a plurality of terrain databases into one terrain database. The flowchart begins with modules  302 ,  304 , and  306  which may perform the same steps as modules  202 ,  204 , and  206 , respectively. The flowchart continues to module  308  with the application of a Kalman filter. A Kalman filter may be regarded as an optimal solution to many tracking and data prediction tasks. The Kalman filter is an efficient recursive filter that estimates the state of a dynamic system from a series of incomplete and noisy measurements. Terrain elevation data sets may be viewed as a set containing noisy measurements. The GTOPO30 terrain data set provides a very smooth surface with very few abrupt changes in elevations; it is low resolution, continuous, and virtually free of high-frequency noise. On the other hand, SRTM terrain elevation data set is high resolution and often contains high-frequency noise caused by radar artifacts of the Interferometric Synthetic Aperture Radar technique employed during the Shuttle mission to acquire the SRTM data. For instance, application of a Kalman filter could provide a better estimate of the series of incomplete noisy measurements of the SRTM data when applied against GTOPO30 data. The utilization of a Kaman filter is known to those skilled in the art. The application of the Kalman filter results with the production of a combined terrain data set having a common resolution. Then, the flowchart proceeds to the end. 
     It should be noted that the method steps described above are embodied in computer-readable media as computer instruction code. It shall be appreciated to those skilled in the art that not all method steps described must be performed, nor must they be performed in the order stated. 
     As used herein, the term “embodiment” means an embodiment that serves to illustrate by way of example but not limitation. 
     It will be appreciated to those skilled in the art that the preceding examples and embodiments are exemplary and not limiting to the scope of the present invention. It is intended that all permutations, enhancements, equivalents, and improvements thereto that are apparent to those skilled in the art upon a reading of the specification and a study of the drawings are included within the true spirit and scope of the present invention. It is therefore intended that the following appended claims include all such modifications, permutations and equivalents as fall within the true spirit and scope of the present invention.