1. A method for recognition of one or more moving objects detected by a Doppler radar through artificial intelligence, the method comprising: receiving, by an Input module, data from one or more sources; providing the data to an artificial intelligence (AI) engine module and a modeller module; performing, by the AI engine module, one or more machine/deep learning process and analysis of the incoming Doppler shift to provide suggestion with respect to the type of moving object based on the historical data modelling; performing, by the modeller module, one or more modelling to recreate the doppler waveform based on the data received from the input module and various parameters, the modeller module is adapted to simulate the moving objects; and displaying and/or providing audio alert, by a display/alert module based on the suggestion received from the AI engine module.

2. The method as claimed in claim 1 comprising providing the suggestion received from the AI engine module to a user through an interaction module; and refining, by the AI engine module, a prediction model, if the suggestion is accepted by the user; and updating, by the AI engine module, a negative list used for elimination process, if the suggestion is rejected by the user, thereby improving the prediction of the AI engine module through the learning process.

3. The method as claimed in claim 1 comprising recording, by a recording module, to assist in playing back the waveforms.

4. A system for recognition of one or more moving objects detected by Doppler radar through artificial intelligence, the system comprising: an Input module is configured to receive data from one or more sources; an artificial intelligence (AI) engine module coupled with the input module for receiving the data, the AI engine module is configured to perform one or more machine/deep learning process and analysis of the incoming Doppler shift and provides a suggestion to the type of the moving object based on the historical data modelling; a modeller module coupled with the input module for receiving the data, the modeler module is configured to perform one or more modelling to recreate the Doppler waveform based on various parameters and simulate the moving objects; and a display/alert module coupled to the AI engine module for displaying and/or providing audio alert based on the suggestion received from the AI engine.

5. The system as claimed in claim 4 comprising an acceptance module coupled to the display/alert module for providing the suggestion received from the AI engine module to a user through an interaction module.

6. The system as claimed in claim 5, wherein the AI engine module is coupled with the acceptance module for receiving the user decision and configure for refining a prediction model, if the suggestion is accepted by the user; and updating a negative list used for elimination process, if the suggestion is rejected by the user, thereby improving the prediction of the AI engine module through the learning process.

7. The system as claimed in claim 4 comprising a recording module couple to the acceptance module for assisting in playing back the waveforms.

8. The system as claimed in claim 4, wherein the one or more sources comprises doppler radar, user data, and / or weather station.

The present invention relates to creation of a System to recognize the moving objects detected by the Doppler radar through deep learning and Artificial Intelligence techniques that would enable the radar operator sitting at beyond the visual range to classify the objects so detected. Considering the broad spectrum of usage of Doppler radars in Armed Forces and the civil sector. This system will have a major impact in the decision making. For example in Defense forces if the moving object is wrongly recognized as Tank instead of a heavy vehicle, it may lead to incorrect response leading to severe consequences.

Description of the related Art
Machine Learning, Deep Learning and Artificial Intelligence are transforming the quality of decision making. Deep modeling using the past historical data and patterns can assist any organization in taking the decisions in much better way.
Currently the Doppler Radars are capable of only detecting the moving objects and report their speed, direction etc. The Doppler shift produces a typical & varying sound through which a trained operator may guess the type of object, which is generally not very reliable. The Doppler Shift is a complex waveform dependent upon the nature of the vehicle, its material, and various moving parts and also on the time of the day and the weather conditions. In this system Artificial intelligence techniques are being used in the process of analyzing complex data and data rich in semantics as well as designing intelligent information systems. Using Machine/Deep learning & Artificial Intelligence Techniques we can create the models and recognize the object in much better way thereby reducing the chances of human error.

Summary
In One embodiment of the present invention, it provides a way to recognize the moving object through Doppler Shift so produced using the Deep Learning & Artificial techniques.
In Second embodiment of the present invention, it enables to do signature modeling of the moving objects.
In Third embodiment of the present invention, it enables recording of the Doppler Shift for later analysis.
In Fourth embodiment of the present invention, it analyses the information from the connected systems using Artificial Intelligence and machine learning techniques to take pre-emptive actions/suggestive measures. In Fifth embodiment of the present invention it enables to recreate audio Signatures modeling for various types of objects.

An embodiment of the present invention describes a method for recognition of one or more moving objects detected by a doppler radar through artificial intelligence. The method comprises receiving, by an Input module, data from one or more sources, providing the data to an artificial intelligence (AI) engine module and a modeller module, performing, by the AI engine module, one or more machine/deep learning process and analysis of the incoming Doppler shift to provide suggestion with respect to the type of moving object based on the historical data modelling, performing, by the modeller module, one or more modelling to recreate the doppler waveform based on the data received from the input module and various parameters, the modeller module is adapted to simulate the moving objects, and displaying and/or providing audio alert, by a display/alert module based on the suggestion received from the AI engine module.
In one embodiment, the method comprises providing the suggestion received from the AI engine module to a user through an interaction module, and refining, by the AI engine module, a prediction model, if the suggestion is accepted by the user, and updating, by the AI engine module, a negative list used for elimination process, if the suggestion is rejected by the user, thereby improving the prediction of the AI engine module through the learning process.
In another embodiment, the method comprises recording, by a recording module, to assist in playing back the waveforms.

Another embodiment of the present invention describes a system for recognition of one or more moving objects detected by Doppler radar through artificial intelligence. The system comprises an Input module is configured to receive data from one or more sources, an artificial intelligence (AI) engine module coupled with the input module for receiving the data, the AI engine module is configured to perform one or more machine/deep learning process and analysis of the incoming Doppler shift and provides a suggestion to the type of the moving object based on the historical data modelling, a modeller module coupled with the input module for receiving the data, the modeler module is configured to perform one or more modelling to recreate the Doppler waveform based on various parameters and simulate the moving objects, and a display/alert module coupled to the AI engine module for displaying and/or providing audio alert based on the suggestion received from the AI engine.
In one embodiment, the system comprises an acceptance module coupled to the display/alert module for providing the suggestion received from the AI engine module to a user through an interaction module.
In another embodiment, the AI engine module is coupled with the acceptance module for receiving the user decision and configure for refining a prediction model, if the suggestion is accepted by the user; and updating a negative list used for elimination process, if the suggestion is rejected by the user, thereby improving the prediction of the AI engine module through the learning process.
In yet another embodiment, a recording module couple to the acceptance module for assisting in playing back the waveforms.
In further embodiment, the one or more sources comprises Doppler radar, user data, and / or weather station.

Brief Description of the accompanying Drawings

Figure 1 depicts an architecture of the present invention.
Figure 2 depicts the flow chart, according to an embodiment of the present invention.
Figure 3 depicts the Level Zero Context diagram, according to an embodiment of the present invention.
Figure 4 depicts the Level One Context diagram, according to an embodiment of the present invention.

Detailed Description of the Invention

The embodiments of the present invention will now be described in detail with reference to the accompanying drawings. However, the present invention is not limited to the embodiments. The present invention can be modified in various forms. Thus, the embodiments of the present invention are only provided to explain more clearly the present invention to the ordinarily skilled in the art of the present invention. In the accompanying drawings, like reference numerals are used to indicate like components.

The specification may refer to “an”, “one” or “some” embodiment(s) in several locations. This does not necessarily imply that each such reference is to the same embodiment(s), or that the feature only applies to a single embodiment. Single features of different embodiments may also be combined to provide other embodiments.

As used herein, the singular forms “a”, “an” and “the” are intended to include the plural forms as well, unless expressly stated otherwise. It will be further understood that the terms “includes”, “comprises”, “including” and/or “comprising” when used in this specification, specify the presence of stated features, integers, steps, operations, elements and/or components, but do not preclude the presence or addition of one or more other features integers, steps, operations, elements, components, and/or groups thereof. It will be understood that when an element is referred to as being “connected” or “coupled” to another element, it can be directly connected or coupled to the other element or intervening elements may be present. Furthermore, “connected” or “coupled” as used herein may include operatively connected or coupled. As used herein, the term “and/or” includes any and all combinations and arrangements of one or more of the associated listed items.

Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure pertains. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.

This Invention is envisaged to provide a quick and more efficient way of recognizing the moving objects detected through the Doppler Radars using Machine/Deep Learning/Artificial Intelligence Techniques.
Figure 1 shows the architecture showing various components of the system. The invention will connect to the radar and ingest the Doppler shift through the data acquisition device. The data will then be fed to the main processing system for analysis, result display & Alerts.

Figure 2 shows the flow chart. The Doppler Frequency goes through learning & analysis process using AI engine and then the final recognition process provides the results also taking input from the historical data modeling. After recognition the results are displayed and alerts generated. The input waveforms and results are also recorded for later use.

Figure 3 shows the zero level context diagram. The inventions take following inputs: Doppler Shift, Weather data and the Source Object Data. The output is the simulations, alerts, Recorded results and object Recognition.

Figure 4 shows Level One context Diagram with interconnectivity of all the modules. Various Modules are :-
(a) Input Module: To ingest the inputs from the Doppler radar, Weather Station and the user
(b) AI Engine: Does all the machine/deep learning process and analysis of the incoming Doppler shift and provides the suggestion to the type of the moving object based on the historical data modeling. The engine keeps on improving its predictions through the learning process.
(c) Modeler Module: Does all the modeling to recreate the Doppler Waveform based on various parameters. Will also be used to simulate the moving objects.
(d) Display/Alert Module: This Module displays and provide audio alerts as well on the results.
(e) Acceptance Module: Provides an interaction module for the user to reject/accept the system recognition results and/or provide his own inputs for machine learning process. If accepted the AI engine will refine the prediction model. If rejected, the input can be used to update the negative list for elimination process.
(f) Recording Module: Records and assist in playing back the waveforms.

The present invention described a system for recognition of one or more moving objects detected by Doppler radar through artificial intelligence. The system comprises an input module, an artificial intelligence (AI) engine module, a modeler module, and a display/alert module. The Input module is configured to receive data from one or more sources. The one or more sources comprises Doppler radar, user data, and / or weather station. The artificial intelligence (AI) engine module is coupled with the input module for receiving the data. The AI engine module is configured to perform one or more machine/deep learning process and analysis of the incoming Doppler shift and provides a suggestion to the type of the moving object based on the historical data modelling. The modeller module is coupled with the input module for receiving the data. The modeler module is configured to perform one or more modelling to recreate the Doppler waveform based on various parameters and simulate the moving objects. The display/alert module coupled to the AI engine module for displaying and/or providing audio alert based on the suggestion received from the AI engine. The acceptance module is coupled to the display/alert module for providing the suggestion received from the AI engine module to a user through an interaction module. The AI engine module is coupled with the acceptance module for receiving the user decision and configure for either refining a prediction model, if the suggestion is accepted by the user, and/or updating a negative list used for elimination process, if the suggestion is rejected by the user. Thereby, the system is adapted to improve the prediction of the AI engine module through the learning process.
The system also comprises a recording module. The recording module is coupled to the acceptance module for assisting in playing back the waveforms. The one or more sources comprises Doppler radar, user data, and / or weather station.

The present invention describes a method for recognition of one or more moving objects detected by a Doppler radar through artificial intelligence. The method comprises receiving, by an Input module, data from one or more sources; providing the data to an artificial intelligence (AI) engine module and a modeller module; performing, by the AI engine module, one or more machine/deep learning process and analysis of the incoming Doppler shift to provide suggestion with respect to the type of moving object based on the historical data modelling; performing, by the modeller module, one or more modelling to recreate the doppler waveform based on the data received from the input module and various parameters, the modeller module is adapted to simulate the moving objects; and displaying and/or providing audio alert, by a display/alert module based on the suggestion received from the AI engine module.
The method for recognition of one or more moving objects detected by a doppler radar through artificial intelligence also comprises providing the suggestion received from the AI engine module to a user through an interaction module; and refining, by the AI engine module, a prediction model, if the suggestion is accepted by the user; and updating, by the AI engine module, a negative list used for elimination process, if the suggestion is rejected by the user, thereby improving the prediction of the AI engine module through the learning process.
Additionally, the method comprises recording, by a recording module, to assist in playing back the waveforms.

Although the invention of the method and system has been described in connection with the embodiments of the present invention illustrated in the accompanying drawings, it is not limited thereto. It will be apparent to those skilled in the art that various substitutions, modifications and changes may be made thereto without departing from the scope and spirit of the invention.

CLAIMS:We claim:
1. A method for recognition of one or more moving objects detected by a Doppler radar through artificial intelligence, the method comprising:
receiving, by an Input module, data from one or more sources;
providing the data to an artificial intelligence (AI) engine module and a modeller module;
performing, by the AI engine module, one or more machine/deep learning process and analysis of the incoming Doppler shift to provide suggestion with respect to the type of moving object based on the historical data modelling;
performing, by the modeller module, one or more modelling to recreate the doppler waveform based on the data received from the input module and various parameters, the modeller module is adapted to simulate the moving objects; and
displaying and/or providing audio alert, by a display/alert module based on the suggestion received from the AI engine module.

2. The method as claimed in claim 1 comprising
providing the suggestion received from the AI engine module to a user through an interaction module; and
refining, by the AI engine module, a prediction model, if the suggestion is accepted by the user; and
updating, by the AI engine module, a negative list used for elimination process, if the suggestion is rejected by the user, thereby improving the prediction of the AI engine module through the learning process.

3. The method as claimed in claim 1 comprising recording, by a recording module, to assist in playing back the waveforms.

4. A system for recognition of one or more moving objects detected by Doppler radar through artificial intelligence, the system comprising:
an Input module is configured to receive data from one or more sources;
an artificial intelligence (AI) engine module coupled with the input module for receiving the data, the AI engine module is configured to perform one or more machine/deep learning process and analysis of the incoming Doppler shift and provides a suggestion to the type of the moving object based on the historical data modelling;
a modeller module coupled with the input module for receiving the data, the modeler module is configured to perform one or more modelling to recreate the Doppler waveform based on various parameters and simulate the moving objects; and
a display/alert module coupled to the AI engine module for displaying and/or providing audio alert based on the suggestion received from the AI engine.

5. The system as claimed in claim 4 comprising
an acceptance module coupled to the display/alert module for providing the suggestion received from the AI engine module to a user through an interaction module.

6. The system as claimed in claim 5, wherein the AI engine module is coupled with the acceptance module for receiving the user decision and configure for
refining a prediction model, if the suggestion is accepted by the user; and
updating a negative list used for elimination process, if the suggestion is rejected by the user, thereby improving the prediction of the AI engine module through the learning process.

7. The system as claimed in claim 4 comprising a recording module couple to the acceptance module for assisting in playing back the waveforms.

8. The system as claimed in claim 4, wherein the one or more sources comprises doppler radar, user data, and / or weather station.