Description:FIELD OF INVENTION
The present invention relates to a system for detecting drones and more particularly relates to detecting and identifying drones in harsh weather condition.(conditions)
BACKGROUND OF THE INVENTION
Drones are small unmanned aerial vehicles which are experiencing explosive growth, nowadays Drones have been widely used in many areas like aerial photography, traffic monitoring, and disaster monitoring. Drone flight is controlled by the remote control of a pilot on the ground. Recently, commercial drones have become available for broad public use. The prices dropped and the drones became much more easily available to the public, also with new technology it becomes very easy to operate. The increasing use of drones poses great threats to public security and personal privacy. This creates an enormous potential of hazard which Starting from people trying to spy governmental actions. To decrease threats a system is created to detect drones.. in case of harsh weather condition it became difficult to detect drones. Normal detecting systems are unable to detect drones in harsh weather conditions.
US10607462B2 disclosesa camera-based security system protects an asset by detecting an aerial surveillor and consequently storing notifications into a video archive, alerting to an operator console, and actuating privation apparatus. One or more cameras provide video streams to a processor which derives object motion. Attributes of object motion trigger notification to record and alert on conditions associated with an aerial surveillor. Tracking of pixels, pixel blocks, and motion vectors enable rules based determination of an airborne surveillance vehicle according to characteristic hovering or lingering by masking LSB of accumulated positive and negative movements. Actuators cause privation enhancement apparatus to obfuscate the protected asset (structure, area, or volume) or to interpose between the protected asset and the surveillor. The method traces a travel path of an object; and determines a ray from a private property to a surveillor drone.
CN107094062 (A) discloses an antenna array device and an all-airspace directional interference system for an unmanned aerial vehicle. The antenna array device provided by the invention comprises a shell of which a horizontal section is a regular n-polygon structure, and a vertical section of the shell is a polygon structure formed by a first trapezoid to X-th trapezoid sequentially stacked one by one; N is the number of antenna array element determined according to an interference airspace range, and X is a natural number within the range of 1-3; an antenna unit is vertically installed on each side plane of the shell, and the antenna units are used for transmitting radio frequency interference signal to airspace right ahead. The interference system provided by the invention comprises the antenna array device, and an interference generation module, a radio frequency channel module, a signal amplification module and a battery module installed in the shell of the antenna array device. The device and the system provided by the invention have the characteristic of multi-target and zero-delay narrow beam directional suppression interference, and can be used for interfering a telemetering link while resisting air and space integrated multiple targets of the unmanned aerial vehicle, and performing all-airspace accurate positioning and striking on the one or multiple unmanned aerial vehicles.
In the prior art, the existing invention is specifically designed for detection of drones in normal weather condition. The existing invention are unable to detect drones in harsh weather condition The present invention is capable of overcoming all drawbacks of the existing inventions hence there is a need for the present invention.
OBJECTIVE OF THE INVENTION
The main objective of the present invention is todetect a drone.
Another objective of the present invention is todetect and identify a drone in all weather conditions.
Yet another objective of the present invention is to reduce the time lost in the detection of drone.
Yet another objective of the present invention is to be cost-effective.
Further objectives, advantages, and features of the present invention will become apparent from the detailed description provided herein below, in which various embodiments of the disclosed invention are illustrated by way of example.

SUMMARY OF THE PRESENT INVENTION
The present invention relates to system for detecting drone in harsh weather condition. The present invention includes a drone detecting device, and a system computer. The drone detecting device includes a weather sensor, and a camera. The weather sensor collects the weather data. The camera captures images of flying objects. In an embodiment, the present invention uses combination of different types of weather sensor including, but not limited to, a barometer, Hygrometer, Anemometer, Pyranometer, Rain gauge. In the preferred embodiment, herein, the camera includes, but not limited to, a graphical camera, an Infrared and a thermal camera. In an embodiment, herein, the camera operate under extreme weather conditions ranging in between the temperatures of -3 degrees Celsius up to 60 degrees. The drone detecting device is connected to the system computer. The drone detecting device sends weather data and captured images to the system computer. The system computer includes a display unit, a system processing unit, and a system database. The display unit displays the drone detection data. The system processing unit is connected to the weather sensor to receive weather data. The system processing unit is also connected to the camera. Herein, the camera captures image of flying objects and the camera send images of flying objects to the system processing unit that identifies drones from the image of flying objects, wherein, the display unit displays the drone detection data. The system database stores the computer-readable instructions, an image processing artificial intelligence model, and a probabilistic model that are being used by the system processing unit to identify the path that drone would take in the time drone visibility is disappeared in harsh weather condition and as well as normal weather conditions. Herein, the system processing unit identifies drones from the image of flying objects with help of the image processing artificial intelligence model and identify the path that drone would take in the time drone visibility is disappeared in harsh weather condition by using probabilistic model. In the preferred embodiment, herein, the image processing artificial intelligence model is a neural network model that learns identifying drones from different images of drone in harsh weather.

The main advantage of the present invention is that the present invention detects and identifies a drone in all weather condition.
Another advantage of the present invention is that the present invention uses Artificial intelligence to detect drones.
Yet another advantage of the present invention is that the present invention reduces the time lost in the detection of drones.
Yet another objective of the present invention is that the present invention is cost-effective.
Further objectives, advantages, and features of the present invention will become apparent from the detailed description provided herein below, in which various embodiments of the disclosed invention are illustrated by way of example.
DETAILED DESCRIPTION OF THE INVENTION
While this invention is susceptible to embodiment in many different forms, there is shown in the drawings and will herein be described in detail specific embodiments, with the understanding that the present disclosure of such embodiments is to be considered as an example of the principles and not intended to limit the invention to the specific embodiments shown and described. In the description below, like reference numerals are used to describe the same, similar or corresponding parts in the several views of the drawings. This detailed description defines the meaning of the terms used herein and specifically describes embodiments in order for those skilled in the art to practice the invention.
Definition
The terms “a” or “an”, as used herein, are defined as one or as more than one. The term “plurality”, as used herein, is defined as two or more than two. The term “another”, as used herein, is defined as at least a second or more. The terms “including” and/or “having”, as used herein, are defined as comprising (i.e., open language). The term “coupled”, as used herein, is defined as connected, although not necessarily directly, and not necessarily mechanically. The term “comprising” is not intended to limit inventions to only claiming the present invention with such comprising language. Any invention using the term comprising could be separated into one or more claims using “consisting” or “consisting of” claim language and is so intended. The term “comprising” is used interchangeably used by the terms “having” or “containing”. Reference throughout this document to “one embodiment”, “certain embodiments”, “an embodiment”, “another embodiment”, and “yet another embodiment” or similar terms means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the present invention. Thus, the appearances of such phrases or in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, the particular features, structures, or characteristics are combined in any suitable manner in one or more embodiments without limitation. The term “or” as used herein is to be interpreted as an inclusive or meaning any one or any combination. Therefore, “A, B or C” means any of the following: “A; B; C; A and B; A and C; B and C; A, B and C”. An exception to this definition will occur only when a combination of elements, functions, steps or acts are in some way inherently mutually exclusive.
As used herein, the term "one or more" generally refers to, but not limited to, singular as well as the plural form of the term.
The drawings featured in the figures are for the purpose of illustrating certain convenient embodiments of the present invention and are not to be considered as limitation thereto. Term “means” preceding a present participle of an operation indicates a desired function for which there is one or more embodiments, i.e., one or more methods, devices, or apparatuses for achieving the desired function and that one skilled in the art could select from these or their equivalent in view of the disclosure herein and use of the term “means” is not intended to be limiting.
Fig. 1 illustrates block diagram of the system(100).The system(100)includes a drone detecting device(108), and a system computer(102). The drone detecting device(108) includes a weather sensor(120), and a camera(122). The drone detecting device(108) is connected to the system computer(102). The system computer(102) includes The system processing unit(106) is connected to the weather sensor(120). The camera(122) is also connected the system processing unit(106).

Fig. 2 illustrates an embodiment a method for detecting drone in harsh weather condition. In step(126) a system processing unit(106) executes computer-readable instructions to receive weather data from a weather sensor(120). In step(128), the system processing unit(106) send commands to an camera(122) to captures image of area of inspection. In step(130), the camera(122) send images of area of inspection to the system processing unit(106) detects flying objects from the image. In step(132), the system processing unit(106) executes computer readable instructions that uses image processing artificial intelligence model to identify drones, from detected flying objects. In step(134) the system processing unit(106) executes computer readable instructions that uses probabilistic model to identify the path that drone would take in the time drone visibility is disappeared in harsh weather condition and as well as normal weather conditions. In step(136) the system processing unit(106) display alerts and predicted path that drone would take, on the display unit(104). In step(138) thus the system processing unit(106) assists in detecting and tracking drone in harsh weather condition.

The present invention relates to system for detecting drone in harsh weather condition. The present invention includes a drone detecting device, and a system computer. The drone detecting device includes a weather sensor, and a camera. The weather sensor collects the weather data. The camera captures images of flying objects. In an embodiment, the present invention uses combination of different types of weather sensor including, but not limited to, a barometer, Hygrometer, Anemometer, Pyranometer, Rain gauge. In the preferred embodiment, herein, the camera includes, but not limited to, a graphical camera, an Infrared and a thermal camera. In an embodiment, herein, the camera operate under extreme weather conditions ranging in between the temperatures of -3 degrees Celsius up to 60 degrees. The drone detecting device is connected to the system computer. The drone detecting device sends weather data and captured images to the system computer. The system computer includes a display unit, a system processing unit, and a system database. The display unit displays the drone detection data. The system processing unit is connected to the weather sensor to receive weather data. The system processing unit is also connected to the camera. Herein, the camera captures image of flying objects and the camera send images of flying objects to the system processing unit that identifies drones from the image of flying objects, wherein, the display unit displays the drone detection data. The system database stores the computer-readable instructions, an image processing artificial intelligence model, and a probabilistic model that are being used by the system processing unit to identify the path that drone would take in the time drone visibility is disappeared in harsh weather condition and as well as normal weather conditions. Herein, the system processing unit identifies drones from the image of flying objects with help of the image processing artificial intelligence model and identify the path that drone would take in the time drone visibility is disappeared in harsh weather condition by using probabilistic model. In the preferred embodiment, herein, the image processing artificial intelligence model is a neural network model that learns identifying drones from different images of drone in harsh weather.
.
In an embodiment, the present invention relates to a method for detecting drone in harsh weather condition, the method comprising:
a system processing unit executes computer-readable instructions to receive weather data from a weather sensor,
further the system processing unit send commands to
an camera to captures image of area of inspection,
the camera send images of area of inspection to the system processing unit detects flying objects from the image.
the system processing unit executes computer readable instructions that uses image processing artificial intelligence model to identify drones,
from detected flying objects,
the system processing unit executes computer readable instructions that uses probabilistic model to identify the path that drone would take in the time drone visibility is disappeared in harsh weather condition and as well as normal weather conditions
the system processing unit display alerts and predicted path that drone would take, on the display unit,
thus the system processing unit assists in detecting and tracking drone in harsh weather condition.

In an embodiment, the method for path exploration of Detected drone in the event drone visibility is lost in harsh weather condition using probabilistic model, the method having

an system computer receives the drone planned flight data from drone;
an system computer receives the drone Past flight logs;
an system computer receives weather data from an weather sensor;
the system processing unit feeds drone planned flight data, drone Past flight logs and weather data into probabilistic model;
probabilistic model calculates the provide estimated path of the flight in the event drone visibility is lost in harsh weather condition; and
in case the probabilistic model calculates wrong estimated path of the flight then
the probabilistic model learns from mistake increase accuracy of path prediction.

In an embodiment, the method of training and increasing accuracy of path prediction the probabilistic model to predict the path of the flight in harsh weather condition, the method having

the system processing unit feeds the Past flight logs into the probabilistic model to learn about the flight path in in the event drone visibility is lost in harsh weather condition;
in case the probabilistic model calculates wrong estimated path of the flight then
the system processing unit executes a correction model;
correction model gathers the relationship of the past estimation and past flight log;
based on a combination of past estimation data and past real flight data correction parameters are prepared and feed into probabilistic model to decrease the external noise and improve estimation accuracy.
In an embodiment, the present invention relates to system for detecting drone in harsh weather condition. The present invention includes one or more drone detecting devices, and one or more system computers. The one or more drone detecting devices include one or more weather sensors, and one or more cameras. One or more weather sensors collect the weather data. The one or more cameras capture images of flying objects. In an embodiment, the present invention uses combination of different types of weather sensor including, but not limited to, a barometer, Hygrometer, Anemometer, Pyranometer, Rain gauge. In the preferred embodiment, herein, the one or more cameras include, but not limited to, a graphical camera, an Infrared and a thermal camera. In an embodiment, herein, the one or more cameras operate under extreme weather conditions ranging in between the temperatures of -3 degrees Celsius up to 60 degrees. The one or more drone detecting devices are connected to the one or more system computers. The one or more drone detecting devices send weather data and captured images to the one or more system computers. The one or more system computers include one or more display units, a system processing unit, and a system database. The one or more display units display the drone detection data. The system processing unit is connected to the weather sensor to receive weather data. The system processing unit is also connected to the one or more cameras. Herein, the one or more cameras capture image of flying objects and the one or more cameras send images of flying objects to the system processing unit that identifies drones from the image of flying objects, wherein, the one or more display units display the drone detection data. The system database stores the computer-readable instructions, an image processing artificial intelligence model, and a probabilistic model that are being used by the system processing unit to identify the path that drone would take in the time drone visibility is disappeared in harsh weather condition and as well as normal weather conditions. Herein, the system processing unit identifies drones from the image of flying objects with help of the image processing artificial intelligence model and identify the path that drone would take in the time drone visibility is disappeared in harsh weather condition by using probabilistic model. In the preferred embodiment, herein, the image processing artificial intelligence model is a neural network model that learns identifying drones from different images of drone in harsh weather.
.
In an embodiment, the present invention relates to a method for detecting drone in harsh weather condition, the method comprising:
a system processing unit executes computer-readable instructions to receive weather data from a weather sensor;
further the system processing unit send commands to one or more cameras to captures image of area of inspection;
the one or more cameras send images of area of inspection to the system processing unit detects flying objects from the image;
the system processing unit executes computer readable instructions that uses image processing artificial intelligence model to identify drones,
from detected flying objects;
the system processing unit executes computer readable instructions that uses probabilistic model to identify the path that drone would take in the time drone visibility is disappeared in harsh weather condition and as well as normal weather conditions;
the system processing unit display alerts and predicted path that drone would take, on the one or more display units; and
thus the system processing unit assists in detecting and tracking drone in harsh weather condition.

In an embodiment, the method for path exploration of Detected drone in the event drone visibility is lost in harsh weather condition using probabilistic model, the method having
one or more system computers receives the drone planned flight data from drone;
one or more system computers receives the drone Past flight logs;
one or more system computers receives weather data from one or more weather sensors;
the system processing unit feeds drone planned flight data, drone Past flight logs and weather data into probabilistic model;
probabilistic model calculates the provide estimated path of the flight in the event drone visibility is lost in harsh weather condition; and
in case the probabilistic model calculates wrong estimated path of the flight then
the probabilistic model learns from mistake increase accuracy of path prediction.

In an embodiment, the method of training and increasing accuracy of path prediction the probabilistic model to predict the path of the flight in harsh weather condition, the method having

the system processing unit feeds the Past flight logs into the probabilistic model to learn about the flight path in in the event drone visibility is lost in harsh weather condition;
in case the probabilistic model calculates wrong estimated path of the flight then
the system processing unit executes a correction model;

correction model gathers the relationship of the past estimation and past flight log;

based on a combination of past estimation data and past real flight data correction parameters are prepared and feed into probabilistic model to decrease the external noise and improve estimation accuracy.
Further objectives, advantages, and features of the present invention will become apparent from the detailed description provided herein below, in which various embodiments of the disclosed present invention are illustrated by way of example and appropriate reference to accompanying drawings. Those skilled in the art to which the present invention pertains may make modifications resulting in other embodiments employing principles of the present invention without departing from its spirit or characteristics, particularly upon considering the foregoing teachings. Accordingly, the described embodiments are to be considered in all respects only as illustrative, and not restrictive, and the scope of the present invention is, therefore, indicated by the appended claims rather than by the foregoing description or drawings. Consequently, while the present invention has been described with reference to particular embodiments, modifications of structure, sequence, materials and the like apparent to those skilled in the art still fall within the scope of the invention as claimed by the applicant

Claims:

I/WE CLAIM
1. A system (100) for detecting drone in harsh weather condition, the system(100) comprising:
an at least one drone detecting device(108), the at least one drone detecting device(108) having
an at least one weather sensor(120), the at least one weather sensor(120) collects the weather data, and
an at least one camera(122), the at least one camera(122) captures images of flying objects;
an at least one system computer(102), the at least one drone detecting device(108) is connected to the at least one system computer(102), the at least one drone detecting device(108) sends weather data and captures images to the at least one system computer(102), the at least one system computer(102) having
an at least one display unit(104), the at least one display unit(104) displays the drone detection data, and
a system processing unit(106), the system processing unit(106) is connected to the weather sensor(120) to receive weather data, further the system processing unit(106) is also connected to the at least one camera(122), , wherein, the at least one camera(122) captures image of detected flying objects and the at least one camera(122) send images of detected flying objects to the system processing unit(106) that identifies drones from the image of detected flying objects, wherein, the at least one display unit(104) displays the drone detection data, and
a system database(124), system database(124) stores the computer-readable instructions, an image processing artificial intelligence model, and a probabilistic model that are being used by the system processing unit(106) to identify the path that drone would take in the time drone visibility is disappeared in harsh weather condition and as well as normal weather conditions,
wherein, the system processing unit(106) identifies drones from the image of detected flying objects with help of the image processing artificial intelligence model and identify the path that drone would take in the time drone visibility is disappeared in harsh weather condition by using probabilistic model.
.
2. The system(100) as claimed in claim 1, wherein, the image processing artificial intelligence model is a neural network model that learns identifying drones from different images of drone in harsh weather.
3. The system(100) as claimed in claim 1, wherein, the at least one camera(122) is selected from a graphical camera, an Infrared and a thermal camera.
4. The system(100) as claimed in claim 1, wherein, the system(100) uses combination of different types of weather sensor(120) selected from a barometer, Hygrometer, Anemometer, Pyranometer, Rain gauge. .
5. The system(100) as claimed in claim 1, wherein, the at least one camera(122) operate under extreme weather conditions ranging in between the temperatures of -30 degrees Celsius up to 60 degrees ..
6. The system(100) as claimed in claim 1, wherein, a method for detecting drone in harsh weather condition, the method comprising:
a system processing unit(106) executes computer-readable instructions to receive weather data from a weather sensor(120),
further the system processing unit(106) send commands to
an at least one camera(122) to captures image of area of inspection,
the at least one camera(122) send images of area of inspection to the system processing unit(106) detects flying objects from the image.
the system processing unit(106) executes computer readable instructions that uses image processing artificial intelligence model to identify drones,
from detected flying objects,
the system processing unit(106) executes computer readable instructions that uses probabilistic model to identify the path that drone would take in the time drone visibility is disappeared in harsh weather condition and as well as normal weather conditions
the system processing unit(106) display alerts and predicted path that drone would take, on the at least one display unit(104),
thus the system processing unit(106) assists in detecting and tracking drone in harsh weather condition.
.

7. The method claimed in claim 6, wherein, the method for path exploration of .
Detected drone in the event drone visibility is lost in harsh weather condition using probabilistic model, the method having

an at least one system computer(102) receives the drone planned flight data from drone,
an at least one system computer(102) receives the drone Past flight logs,
an at least one system computer(102) receives weather data from an at least one weather sensor(120),
the system processing unit(106) feeds drone planned flight data, drone Past flight logs and weather data into probabilistic model.
probabilistic model calculates the provide estimated path of the flight in the event drone visibility is lost in harsh weather condition,
in case the probabilistic model calculates wrong estimated path of the flight then
the probabilistic model learns from mistake increase accuracy of path prediction.
8. The method as claimed in claim 7, wherein, the method of training and increasing accuracy of path prediction the probabilistic model to predict the path of the flight in harsh weather condition, the method having

the system processing unit(106) feeds the Past flight logs into the probabilistic model to learn about the flight path in in the event drone visibility is lost in harsh weather condition,
in case the probabilistic model calculates wrong estimated path of the flight then
the system processing unit(106) executes a correction model,

correction model gathers the relationship of the past estimation and past flight log,

based on a combination of past estimation data and past real flight data correction parameters are prepared and feed into probabilistic model to decrease the external noise and improve estimation accuracy