Abstract:
The present system for modeling intelligent sensor selection and placement takes signal and sensor information and calculates a statistical inference. As signal data passes through a series of processors, it is transformed by functions to account for signal emission, sensor reception, environmental factors, and noise. This produces a simulation of what the emitted signal would appear to be at a given sensor. The system may be used to select the most effective sensors for a given area or to determine the best sensor coverage for a given area.

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
       [0001]    This patent application claims the benefit of U.S. Provisional Application No. 62/261,560 filed Dec. 1, 2015. The above application is incorporated by reference herein. 
     
    
     STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT 
       [0002]    The invention described herein was made by an employee of the United States Government and may be manufactured and used by the Government of the United States of America for governmental purposes without the payment of any royalties thereon or therefore. 
     
    
     FIELD OF INVENTION 
       [0003]    This invention relates to the field of computer modeling and more specifically to a system for intelligent sensor placement and sensor network optimization. 
       BACKGROUND OF THE INVENTION 
       [0004]    The US Army Corps of Engineers relies extensively on the use of sensors to provide mission critical data in a range of environments. There are a multitude of commercially available sensors which perform a particular range of functions, and which may offer varying levels of performance depending upon the weather, terrain, and other conditions of the environment in which they are placed. 
         [0005]    Wave propagation modeling is an essential component for data-gathering, tracking, and surveillance activities which require the intelligent placement of sensors. Intelligent placement of sensors is difficult, often requiring testing of numerous settings and configurations to ensure that an area is adequately covered. Both topography and weather affect the efficacy of the model. Physical barriers may divert signals. The transmission of light, sound, or radio waves on a clear, dry day may be different from a foggy night or a rainy, windy day. 
         [0006]    Many companies such Atmospheric and Environmental Research (AER) Inc., NextGen Federal Systems, ProLogic Inc., Toyon Corporation, and Blue Ridge Research and Consulting LLC provide specialized sound, seismic, and image sensors designed to provide optimum sensing capability for particular purposes and environments. However, selection of these sensors may require time-consuming trial and error. 
         [0007]    To design reliable sensor networks, the military and private sector require data as to the sensing capabilities and signal features of sensors. Engineers also require extensive environmental data to optimize placement of signal emitters and sensors, as these factors effect sensor performance. 
         [0008]    There is an unmet need in the art for computer modeling systems which can intelligently select appropriate emitting and sensing devices and calculate the probability of signal detection for various placements and points in time. 
         [0009]    There is a further unmet need for a computer modeling system that can simulate geographical and environmental conditions that impact transmission of light, sound, and other energy waves for military and civilian applications. 
       SUMMARY OF THE INVENTION 
       [0010]    The present invention is a system for modeling performance of geographically distributed sensors. The system includes a user interface configured to receive user defined Signal_Feature Attributes and user defined Directional Attributes. The system includes an Instantiation Processor configured to instantiate Signal Objects. Each of the Signal Objects includes the Signal_Feature Attributes. The system includes a Radiated_Signal Processor configured to receive the Signal Object and user defined Directional Attributes and to perform a Radiated_Signal Function. This function calculates Directional_Dependence Attributes for each of the Signal_Feature Attributes and updates the Signal Object with the Directional_Dependence Attributes. The system includes a Signal_Propagation Processor configured to receive the Directional_Dependence Attributes and environmental data, and perform a Signal_Propagation Function. This function updates the Signal Object with Vector_Signal Attributes for each of the Signal_Feature Attributes. The system includes a Noise/Gain_Transfer Processor configured to receive the Vector_Signal Attributes and Sensor_Device Attributes and perform a Noise/Gain_Transfer Function. This function updates the Signal Object with Scalar_Signal Attributes. The system includes an Inference Processor configured to receive the Scalar_Signal Attributes and the Sensor_Device Attributes and perform an inference Function. This function creates a statistical model of signal propagation from which an inference can be drawn. 
     
    
     
       BRIEF DESCRIPTION OF SEVERAL VIEWS OF THE DRAWING(S) 
         [0011]      FIG. 1  illustrates an exemplary method implemented on an exemplary EASEE system for modeling an optimized sensor network. 
           [0012]      FIG. 2  illustrates an exemplary EASEE system, which is an integrate computer apparatus with components that may be geographically distributed. 
       
    
    
     TERMS OF ART 
       [0013]    As used herein, the term “Directional Attribute” refers to any physical attribute affecting the direction of a signal. 
         [0014]    As used herein, the term “Directional_Dependence Attributes” refers to any attribute which is a function of directional attribute. 
         [0015]    As used herein, the term “environmental data” refers to weather, topography, permanent or temporary structures, field cover, or any other physical phenomena affecting the direction or magnitude of a signal. 
         [0016]    As used herein, the term “Instantiate” refers to creating an object with one or more used-defined attributes. 
         [0017]    As used herein, the term “interface” refers to a component for receiving, transmitting, or displaying data. 
         [0018]    As used herein, the term “object” refers to a data structure containing values and attributes which invoke functions when populated or updated. 
         [0019]    As used herein, the term “packet”refers to a data structure in which data is aggregated and transmitted between functions, objects, or interfaces. 
         [0020]    As used herein, the term “processor” refers to any hardware, software, or electrical component which performs mathematical or statistical computations. 
         [0021]    As used herein, the term “user defined” refers to attributes or values that are updated or changed by a user, external data base, sensor input, or other source. 
       DETAILED DESCRIPTION OF THE INVENTION 
       [0022]      FIG. 1  illustrates exemplary method  100  implemented on exemplary EASEE system  200  for modeling an optimized sensor network. 
         [0023]    Each function identified in  FIG. 1  may be viewed as a processor or processing module in which information is systematically represented by a Signal Object, and pursuant to which information flowing between each module is efficiently and reliably controlled through the generic attribute specification of the Object specifications. In various embodiments, the invention may be implemented in Java or any other programming language. The use of a novel Signal Object, with attributes and unique functions, ensures the reliability of the model. For example, the inputs and outputs at each stage in the modeling process are compatible, e.g., only acoustic signal features can be sent to an acoustic sensor representation. 
         [0024]    In Step  1 , a user instantiates a Signal Object by identifying Signal_Feature Attributes through a user interface. An Instantiation Processor instantiates the Signal Object after the user input is received. The Signal_Feature Attributes are updated by a series of functions which revise the attributes of the instantiated Signal Object to create various statistical models which ultimately allow inferences to be drawn as to signal propagation probability or detection of an emitter with known location coordinates by a sensor with known location coordinates. In various embodiments, the user-defined Signal_Feature Attributes represent the identifying qualities of the signal that are used for sensing and processing. The use of a novel Signal Object with attributes and unique functions ensures the reliability of the model. For example, the inputs and outputs at each stage in the modeling process are compatible, e.g., only acoustic signal features can be sent to an acoustic sensor representation. For example, for visible processing features may represent red, yellow or green bands; for acoustic processing they may be octave bands on user interface. Each user defined Signal_Feature Attribute is associated a statistical model describing the random variations or uncertainties in the signal, such as an exponential or Gaussian distribution. Inferences, such as signal-to-noise ratio, probabilities of detection, target bearing, and location accuracy, are calculated from the signal statistical models in later steps. 
         [0025]    In Step  2 , a Radiated —  Signal Function is invoked by the Signal Object once the attributes required for inputs to the function are invoked. The Radiated_Signal Function requires that the user populate the Signal_Feature Attributes, as well as location coordinates. The Radiated_Signal Function produces output that is a statistical model for each feature which conveys the dependence of the signal on the direction of emission (azimuth and elevation angle) relative to the source. The statistical model and directional dependence are grouped together as Directional_Dependence Attributes for each signal feature. The Directional_Dependence Attributes for all of the features can be grouped into a Directional_Dependence Packet. 
         [0026]    In Step  3 , the Directional_Dependence Attributes or Signal Object are entered or updated, and a Signal_Propagation Function is invoked. This function receives as input the Directional_Dependence Packet containing the Directional_Dependence Attributes for each signal feature, produced in Step  2 , the locations of the emitter and the sensor, and environmental data. The environmental data is statistical information as to geographical (e.g., topology), geological, and atmospheric environment (e.g., weather). The Signal_Propagation Function produces as output a statistical model direction of energy propagation of each signal received by the sensor and its magnitude, resulting in a Vector_Signal Attribute each feature. The Vector_Signal Attributes for each feature are grouped into a Vector_Signal Packet. 
         [0027]    In Step  4 , a Noise/Gain_Transfer Function is invoked. The Vector_Signal Packets are partitioned into signals of interest and background noise. A transfer function (gain) is applied to each signal feature, and sensor self-noise is added for each feature. The total power associated with the signals of interest, and the background noise, are then separately calculated and represented as Scalar_Signal Attributes. The Scalar_Signal Attributes for all features are grouped into a Scalar_Signal Packet. 
         [0028]    In Step  5 , an Inference Function is invoked to calculate a statistical model of signal propagation from which an inference can be drawn such as the mean signal-to-noise ratio, probability of detection, or target bearing. The inferences for each feature are grouped into an Inference Packet. 
         [0029]    In Step  6 , the statistical model and/or inferences may be displayed on the user interface. 
         [0030]    In an optional Step  7  of one embodiment, a fusion processing module receives as input the Inference Packets produced by an inference processing module, or by fusion processing modules. There are two types of fusion processing modules: feature fusion processors, which fuse all of the features in a packet into a single inference, and packet fusion processors, which receive multiple packets and fuse them (feature-by-feature) into a single inference packet. These two types of fusion processors implement the fuseFeatures and fusePackets methods, respectively, which provides a type-safe specification of the input and output inferences. 
         [0031]    Certain embodiments may also include Sensor Databases containing Sensor_Device Attributes, Signal_Feature Databases containing Signal_Feature Attributes, and/or Relational Databases containing emitter and signal data, as well as Signal_Feature Attributes. 
         [0032]      FIG. 2  illustrates EASEE system  200 , which is an integrated computer apparatus with components that may be geographically distributed. 
         [0033]    In the exemplary embodiment shown, user interface  10  is configured to receive a user interface configured to receive user defined Signal_Feature Attributes. Instantiation Processor  20  instantiates Signal Object  30  which is an object that includes said user defined Signal_Feature Attributes. User interface  10  is also configured to receive user selected Directional Attributes. 
         [0034]    Radiated_Signal Processor  40  is configured to receive Signal Object  30  and user defined Directional Attributes from user interface  10 . Radiated_Signal Processor  40  performs Radiated_Signal Function to calculate Directional_Dependence Attributes for each signal feature and update Signal Object with these Directional_Dependence Attributes. Thus updated, Signal Object  30  represents a statistical model of the directional dependence of each signal feature. 
         [0035]    Signal_Propagation Processor  50  receives Directional_Dependence Attributes and environmental data, and performs a Signal_Propagation Function to update Signal Object  30  with Vector_Signal Attributes for each signal feature. In the exemplary embodiment shown, Signal_Propagation Processor  50  utilizes external statistical environmental models and databases to incorporate geographical, terrain, geological, weather, and other available environmental data. As updated, Signal Object  30  represents a statistical model of the direction and magnitude of each signal feature. 
         [0036]    Noise/Gain Transfer Processor  60  which receives the Vector_Signal Attributes and Sensor_Device Attributes and performs Noise/Gain_Transfer Function to update Signal Object  30  with Scalar_Signal Attributes. 
         [0037]    Inference Processor  70  receives Scalar_Signal Attributes and performs an Inference Function to create a statistical model  80  of signal propagation from which an inference can be drawn. In various embodiments, these inferences may relate to various aspects of performance such signal-to-noise ratio, probabilities of detection, target bearing, and location accuracy. 
         [0038]    It will be understood that many additional changes in the details, materials, procedures and arrangement of parts, which have been herein described and illustrated to explain the nature of the invention, may be made by those skilled in the art within the principle and scope of the invention as expressed in the appended claims. It should be further understood that the drawings are not necessarily to scale; instead, emphasis has been placed upon illustrating the principles of the invention.