Non-resolved target detection system and methods
Systems and methods are provided for modeling non-resolved object detection performance of sensor systems. An exemplary method includes: receiving a first data from or associated with a plurality of sensors; creating, using the first data, a plurality of models for each of the sensors; calculating, using the models and the first data, signal-to-ratio (SNR) values for the sensors against a target; creating, using the SNR values, a plurality of probability of detection models for each of the sensors, wherein one of the probability of detection models is a quantum illumination probability of detection model; and determining, using the detection models, non-resolved target detection performance of each of the sensors.
1 . A system for modeling non-resolved object detection performance of sensor systems, the system comprising one or more computing device processors; and
one or more computing device memories, coupled to the one or more computing device processors, the one or more computing device memories storing instructions executed by the one or more computing device processors, wherein the instructions are configured to:
receive a first data from or associated with at least one RADAR sensor, quantum RADAR sensor, or Electro-Optical and Infrared (EOIR) sensor;
create, using the first data, a plurality of detailed illumination models for each of the sensors;
calculate, using the detailed illumination models and the first data, signal-to-ratio (SNR) values for the sensors against a target using a target signal, localized standard deviation of background noise, and a received intensity point on a single frame at a center pixel location;
create, using the SNR values, a plurality of probability of detection models for each of the sensors, wherein at least one of the probability of detection models is a quantum illumination probability of detection model, noise from cloud clutter model that characterizes noise induced from cloud clutter, spatial match filter and velocity matched filtering model that keeps a peak location of a point source unchanged, EOIR atmospheric attenuation that determines a loss of signal propagating through the atmosphere, and a quantum radar sensor model that produces a quantum illumination optimal receiver probability of detection;
determine, using the detection models, non-resolved target detection performance of each of the EOIR sensor; and
display, via a user interface, sensor properties and visual performance metrics thereby improving an operator's understanding of an object detected and its surroundings.
2 . The system of claim 1 , wherein the plurality of models comprises a plurality of illumination models used by RADAR sensors, quantum RADAR sensors, and/or Electro-Optical and Infrared (EOIR) sensors.
3 . The system of claim 1 , wherein the plurality of models comprises a plurality of models for modeling at least one RADAR sensor.
4 . The system of claim 1 , wherein the plurality of models comprises a plurality of models for modeling at least one Electro-Optical and Infrared (EOIR) sensor.
5 . The system of claim 1 , wherein the plurality of detailed illumination models comprises a plurality of models for modeling at least one a quantum RADAR sensor.
6 . The system of claim 1 , wherein the plurality of detailed illumination models comprises a plurality of models for modeling noise from cloud clutter for both Short Wave Infrared (SWIR) and Medium Wave Infrared (MWIR) wavebands.
7 . The system of claim 1 , wherein the plurality of models comprises a plurality of models for modeling spatial matched filters and velocity matched filters to improve the SNR values and the non-resolved target detection performance of an Electro-Optical and Infrared (EOIR) sensor.
8 . The system of claim 1 , wherein plurality of probability of detection models comprises at least one of the following: i) a single pulse probability of detection model for nonfluctuating targets, ii) a probability of detection model for an optimal classic illumination protocol, iii) a probability of detection for the optimal classic illumination protocol, and iv) a multiple pulse probability of detection model for fluctuating targets.
9 . A method for modeling non-resolved object detection performance of sensor systems, the method comprising:
receiving a first data from or associated with at least one RADAR sensor, quantum RADAR sensor, or Electro-Optical and Infrared (EOIR) sensor;
creating, using the first data, a plurality of detailed illumination models for each of the sensors;
calculating, using the detailed illumination models and the first data, signal-to-ratio (SNR) values for the sensors against a target using a target signal, localized standard deviation of background noise, and a received intensity point on a single frame at a center pixel location;
creating, using the SNR values, a plurality of probability of detection models for each of the sensors, wherein at least one of the probability of detection models is a quantum illumination probability of detection model, noise from cloud clutter model that characterizes noise induced from cloud clutter, spatial match filter and velocity matched filtering model that keeps a peak location of a point source unchanged, EOIR atmospheric attenuation that determines a loss of signal propagating through the atmosphere, and a quantum radar sensor model that produces a quantum illumination optimal receiver probability of detection;
determining, using the detection models, non-resolved target detection performance of each of the sensors; and
displaying, via a user interface, sensor properties and visualize performance metrics thereby improving an operator's understanding of an object detected and its surroundings.
10 . The method of claim 9 , wherein the plurality of models comprises a plurality of illumination models used by RADAR sensors, quantum RADAR sensors, and/or Electro-Optical and Infrared (EOIR) sensors.
11 . The method of claim 9 , wherein the plurality of models comprises a plurality of models for modeling at least one RADAR sensor.
12 . The method of claim 9 , wherein the plurality of models comprises a plurality of models for modeling at least one Electro-Optical and Infrared sensor.
13 . The method of claim 9 , wherein the plurality of models comprises a plurality of models for modeling at least one a quantum RADAR sensor.
14 . The method of claim 9 , wherein the plurality of models comprises a plurality of models for modeling noise from cloud clutter for both Short Wave Infrared (SWIR) and Medium Wave Infrared (MWIR) wavebands.
15 . The system of claim 9 , wherein the plurality of models comprises a plurality of models for modeling spatial matched filters and velocity matched filters to improve the SNR values and the non-resolved target detection performance of an Electro-Optical and Infrared (EOIR) sensor.
16 . The method of claim 9 , wherein the first data comprises data received by a user from a user interface regarding properties of at least one of the plurality of sensors.
17 . The method of claim 9 , wherein plurality of probability of detection models comprises at least one of the following: i) a single pulse probability of detection model for nonfluctuating targets, ii) a probability of detection model for the optimal classic illumination protocol, iii) a probability of detection for an optimal classic illumination protocol, and iv) a multiple pulse probability of detection model for fluctuating targets.
18 . A non-transitory computer-readable storage medium storing instructions which when executed by a computer cause the computer to perform a method for modeling non-resolved object detection performance of sensor systems, the method comprising:
receiving a first data from or associated with at least one RADAR sensor, quantum RADAR sensor, or Electro-Optical and Infrared (EOIR) sensor;
creating, using the first data, a plurality of detailed illumination models for each of the sensors;
calculating, using the detailed illumination models and the first data, signal-to-ratio (SNR) values for the sensors against a target using a target signal, localized standard deviation of background noise, and a received intensity point on a single frame at a center pixel location;
creating, using the SNR values, a plurality of probability of detection models for each of the sensors, wherein at least one of the probability of detection models is a quantum illumination probability of detection model, noise from cloud clutter model that characterizes noise induced from cloud clutter, spatial match filter and velocity matched filtering model that keeps a peak location of a point source unchanged, EOIR atmospheric attenuation that determines a loss of signal propagating through the atmosphere, and a quantum radar sensor model that produces a quantum illumination optimal receiver probability of detection;
determining, using the detection models, non-resolved target detection performance of each of the sensors; and
displaying, via a user interface, sensor properties and visualize performance metrics thereby improving an operator's understanding of an object detected and its surroundings.