Communications – electrical: acoustic wave systems and devices – Distance or direction finding – By combining or comparing signals
Reexamination Certificate
2003-07-25
2004-06-29
Pihulic, Daniel (Department: 3662)
Communications, electrical: acoustic wave systems and devices
Distance or direction finding
By combining or comparing signals
Reexamination Certificate
active
06757219
ABSTRACT:
FIELD OF THE INVENTION
This invention, referred to as “Raster Based Manual Adaptive Target Motion Analysis Evaluation” (Raster MATE) relates to a method or process for target motion analysis using raw acoustic raster data. More specifically, the invention relates to determination of the solution (relative track position and track motion) of a radiating source, and possibly to determination of solution quality, from acoustic raster.
BACKGROUND OF THE INVENTION
Determination of certain position and motion parameters, such as location, range, direction and speed, of a target or radiating source, from information items received from the target or radiating source, is a general problem of considerable importance to many types of surveillance systems. For example, a determined location, direction and speed can be used to track a target and anticipate its future location.
Specific terms associated with various systems referred to in this patent application are defined so as to insure common understanding of the terms.
“Ascan” refers to a single line of acoustic raster energy received at a sensor represented in an amplitude format. The scanning from one reference observation to the next is sometimes referred to as amplitude scanning or Ascan. Displaying the received energy in an amplitude level peak connected line for each observation results in a line of amplitude observations or Ascan at that moment in time.
“Bscan” refers to a single line of acoustic raster energy received at a sensor represented in an intensity format. The scanning from one reference observation to the next is sometimes referred to as bit scanning or Bscan. Displaying the received energy in a color intensity level for each observation results in a line of observations or Bscan at that moment in time. The use of Bscan raster is referred to in many of the descriptions herein.
“Target Position” is the location of the target at any point in time. This location could be defined as, and is not limited to “a Bearing/Range from a sensor location, a Latitude/Longitude location, or X/Y coordinate on a Cartesian Plane.”
“Target Track” is a set of target information attributes over time. This set of data points collected over time could be, and is not limited to, bearing information. A data set of bearing could be used to generate a bearing track, which in turn could provide a basis for bearing trend analysis capabilities.
“Target Motion” refers to a direction of motion. This motion normally derived from observing multiple target positions over a specified time period. Track motion attributes could be defined as Course/Speed, Heading/Speed, or X/Y velocities, but are not limited to these attributes.
“Track Measurement” (or Observation) refers to a unique recording of energy radiated from a source or reflected from a target at as specific moment in time. Measurements could be (and are not limited to) Bearing, Conical Angle (in the case of a non-stabilized line array), Range, Inverse Range, Depression/Elevation (D/E), Wave Front Curvature Time Delay, andor Tonal Frequency. Typically track measurements are produced by, or are the output of, a sensor tracker-based function that attempts to follow a specified detected energy source in the sensor's environment. For the purpose of this disclosure, one of these measurements, namely bearing information, is referred to in many of the descriptions herein. With respect to the invention the method could be applied to any form of data received by the sensor subsystem.
“Solution” (or State Vector) refers to the joining of the target position information with the target track information to define a target's unique position and motion at a specific moment in time. Using a solution, one could project the target to a future time and resolve or determine the target's projected position, or project backward in time to resolve where the target was at a moment in time in the past.
“Solution Generated Track (SGT)” refers to a set of solution based data points generated by the use of solution extrapolation or projection algorithms. This set of data points collected over time could be, and is not limited to, bearing information. This data set represents a trend of possible target information over time.
“Received Energy Trace (RET)” refers to a set of received high-energy data points. This set of data points collected over time could include, and is not limited to, bearing energy information. An acoustic raster display may display this energy as a function of bearing. A collective set of high-energy data over time creates an energy trace on an acoustic raster display.
Manual, automatic and computer-aided manual methods for determining location, direction and speed of targets (Target Motion Analysis or TMA) are known in the art. These methods of TMA maintain a few basic aspects in common. For the purpose of this paper the Manual Adaptive Target Motion Analysis Evaluation (MATE), Maximum Likelihood Estimator (MLE), Kalman Statistical Track (KAST) and Solution Imaging Target Motion Analysis Evaluator (SITE) algorithms will be addressed. Each of these TMA algorithms requires a set of track measured data or observations. Each adjusts a set of parameters, as for example x/y position and x/y velocity of the target in the Cartesian plane, to make the track parameters agree with the measurements via a functional relationship. In general, for each of these methods, the set of parameters that agrees well with the measured data is deemed to be the estimated resulting solution. In these traditional methods, a Target Motion Analysis algorithm processes “Track Measurement” data to achieve a desired solution.
An example of a manual method is the Manual Adaptive Target Motion Analysis Evaluation (MATE). In this method, the operator defines a set of tracker-based measurements to be used, edits the measurements to remove bad data, and modifies the parameters of the solution in an attempt to minimize the errors between the measured value and the theoretical value at the same moment in time. An example of an automatic or computer-aided method is a Maximum Likelihood Estimator (MLE). In this method, the MLE algorithm automatically defines the tracker measurement data set based upon algorithm control parameters, attempts to pre-edit the measurements to remove bad data, and automatically adjusts parameters in an algorithmic manner so as to achieve the best solution that agrees with the measurement data set. A second example of an automatic or computer-aided method is a Kalman Statistical Track (KAST). In this method, the KAST starts with a guess at the solution, receives tracker-based measurement data points one at a time and pre-edits the measurements with respect to bad data parameters, then uses the measurements to improve the guess in attempts to narrow in on the best solution with respect to the measurement data set. An example of a manually controlled computer-aided method is a Solution Imaging Target Motion Analysis Evaluator (SITE). In this method, the operator defines a set of tracker-based measurements to be used, edits the measurements to remove bad data, and selects parameters for solution image generation. The image generation process: generates a matrix of the solutions. Each solution is used to quantify the errors between a measured value and a theoretical value at the same moment in time. The resulting matrix provides a graphical representation of solution possibilities coded with respect to the solution error detected. MATE, MLE, KAST and SITE work on a predefined set of tracker-based measurement data per algorithm execution. All algorithms require some type of data editing function to remove bad tracker data from the data set prior to algorithm execution. MLE and KAST adjust the parameters automatically, while MATE and SITE require an operator to adjust the parameters.
An underlying assumption for each of these algorithms is that high-quality tracker-based measurement data is provided to the TMA algorithms from a measurement subsystem, and that the received measurement data has already been
Larosa Victor P.
Muniak Charles G.
Duane Morris LLP
Lockheed Martin Corporation
Pihulic Daniel
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