System and method for detecting and identifying unmanned aircraft systems

Systems, methods, and apparatuses are presented herein for detecting and identifying unmanned aircraft systems (UAS) or drones. The system can include one or more UAS sensor nodes distributed about an area to be monitored. Each UAS sensor node can be communicably coupled to a central server but is able to conduct detection and identification procedures separate from the central server. The UAS sensor node can include a microphone that detects an audio signal generated within the area to be monitored. The node can convert the audio signal into a digital signal, can segment the audio signal, and can pass the signal through a bandpass filter. The node can also conduct a Fourier transform and smooth filtering on the digital audio signal before comparing the signal to multiple stored sample UAS audio signals for known UAS vehicles and motor stresses to determine a likelihood of a match.

FIELD OF THE DISCLOSURE

The present disclosure is generally directed to audio monitoring and evaluation and more particularly to systems and methods for detecting and identifying unmanned aircraft systems (“UAS”), such as drone aircraft, in flight.

BACKGROUND

The vast majority of users of UAS vehicles use them for legitimate personal or commercial purposes. However, recent history has shown that a UAS can quickly be converted from a device that is beneficial for commerce and/or amusement to one that can deliver harm and destruction to a desired location. While certain laws are in place regarding the proper use of UAS vehicles, diligent legislation will not ensure nefarious actors will be completely eliminated or deterred.

Conventional drone detection systems have been designed to monitor an area for UAS vehicle activity and notify a designated person or entity if a drone is believed to be in the designated area under surveillance. However, this kind of system is limited in its ability to fully characterize the threat. For instance, certain brands and models of UAS vehicles are able to carry greater payloads than other brands and models of UAS vehicles, making them more likely to be able to deliver contraband or destructive devices into the monitored area. Further, certain brands and models of UAS vehicles may include better technological upgrades than other UAS brands and models, which make them better suited for precision delivery of contraband or destructive devices into the monitored area.

In certain situations, knowing the brand and model of the UAS vehicle may not be sufficient to properly evaluate the likelihood of intent to do harm or cause destruction. In some cases, being able to identify the specific the UAS vehicle down to the serial number or tail number for the brand and model of the UAS vehicle may provide the receiving party with additional information. This additional information may help the receiving party determine likelihood that the UAS vehicle is entering the monitored area with a negative intent.

In addition, knowing additional information about the UAS vehicle could further help the receiving party to determine the intent of the UAS vehicle. For example, being able to evaluate the level of strain on the one or more motors driving the UAS vehicle could help determine if the UAS vehicle is carrying a payload that is in addition to the weight of the UAS vehicle. More granularly, being able to evaluate the amount or weight of the payload could provide greater insight into the likely make-up of the payload and the actual potential for damage or destruction cause by the UAS vehicle or whether it is likely to be one that is not of concern.

DETAILED DESCRIPTION OF THE EXAMPLE EMBODIMENTS

Certain relationships between dimensions of the UAS detection and identification system and between features of the UAS detection and identification system are described herein using the term “substantially.” As used herein, the term “substantially” indicates that each of the described dimensions or linear descriptions is not a strict boundary or parameter and does not exclude functionally similar variations therefrom. Unless context or the description indicates otherwise, the use of the term “substantially” in connection with a numerical parameter indicates that the numerical parameter includes variations that, using mathematical and industrial principles accepted in the art (e.g., rounding, measurement or other systematic errors, manufacturing tolerances, etc.), would not vary the least significant digit.

FIG. 1is a simplified block diagram illustrating an example UAS vehicle detection environment100, including a central server135and one or more UAS sensor nodes110a-ncommunicably coupled to the central server125and distributed throughout the detection environment100, in accordance with example embodiments of the disclosure. Referring now toFIG. 1, the UAS vehicle detection environment100can comprise any bounded or unbounded exterior space105. In addition, the UAS vehicle detection environment100can include covered areas, tunnels, areas bounded on fewer than all sides or any other space that is accessible by a UAS vehicle130. The exterior space105can be located in urban, suburban, or rural settings. Further, the exterior space105can be sparsely or densely populated and can include humans interacting within the space during the time of detection of a UAS vehicle130.

FIG. 4is a diagram of different brands and models of UAS vehicles130, in accordance with one example embodiment of the disclosure. Referring now toFIGS. 1 and 4, UAS vehicles130come in many different shapes and sizes. UAS vehicles130can also have many different types of rotors and numbers of rotors. In addition, UAS vehicles130can vary in the positioning of the rotors, both along the fuselage and whether vertically or horizontally oriented. Some of this variation can be attributed to the intended use of the UAS vehicle130, while other variations are more stylistic. WhileFIG. 4shows some varieties of UAS vehicles130, the display is not intended to show all of the different types of UAS vehicles130. For example,FIG. 4shows a UAS vehicle405that is generally shaped like a conventional aircraft and includes a single main rotor along the tail of the vehicle405.FIG. 4also shows a UAS vehicles415and420that are generally shaped like a helicopter and include a single main rotor and a tail rotor. In addition, the UAS vehicle410includes three rotors with at least one rotor positioned at a different elevation.FIG. 4also presents UAS vehicles425,430having eight rotors and five rotors respectively.

For each type of UAS vehicle130, including, but not limited to vehicles405-430, each rotor may be coupled to a motor that is independently powered and operated on the vehicle130. In addition, each rotor can be controlled separate and distinct from other rotors on the UAS vehicle130. Providing UAS vehicles130with independently operated rotors can allow for greater maneuverability of the UAS vehicle130. UAS vehicles130can range in weight from one to hundreds or thousands of pounds and have motors and rotors that can vary in size and shape. In addition, rotors for the different UAS vehicles130can be made of different materials, including, but not limited to, plastic, metal, metal alloys, composites, etc.).

The motors driving these UAS vehicles130are typically brushless DC motors. However other motor types are within the scope of this disclosure. Each of these motors driving these rotors can generate an audible noise under operation. In many instances, the noise generated is different not only between brands and models of UAS vehicles130but also from vehicle to vehicle within the same brand. As such, the audible noise can act like a fingerprint, individually identifying the specific UAS vehicle130, not just the type of UAS vehicle, the brand, or the model.

Throughout this disclosure, reference is made to different types of audio signals or acoustic waves that are generated by the motor or motors on a UAS vehicle130. The disclosed UAS sensor node110converts these audio signals into digital sound samples, splits up the samples into discreet sizes for comparison, processes each segment of the audio sample into a filtered and fast Fourier transformed sample, conducts an initial review pass of the sample segment to determine if the digital sample segment is from a UAS vehicle130generally and then does a second review pass comparing the filtered and fast Fourier transformed digital audio segment to known UAS vehicle signature audio files to determine more specific information about the UAS vehicle130, including, but not limited to, the brand of UAS vehicle, the model of UAS vehicle, the location of the UAS vehicle when detected, the level of strain on the motor and/or an estimated weight being carried by the UAS vehicle in addition to the vehicle weight, the potential payload being carried by the UAS vehicle, and the time and location of the UAS vehicle or the UAS sensor node110when the detection of the UAS vehicle occurred.

In one example embodiment, the UAS vehicle signature audio files are digital audio files stored within each individual UAS sensor node110. By placing the signature audio files in each sensor node110, it allows for a quicker determination as to likelihood of identification of a potential UAS vehicle in the monitored area and reduces the communication and computational strain on the central server115. In other example embodiments, the UAS vehicle signature audio files can be stored on the central server115, which can receive the audio signal from the UAS sensor node110and conduct the filtering, fast Fourier transforming and analysis of the received audio signal.

In certain example embodiments, at least a portion of the UAS vehicle signature audio files are generated through testing of different brands and models of different UAS vehicles130and by placing those vehicles130under different load levels to generate different levels of strain on the motors of those vehicles130. In addition, at least a portion of the UAS vehicle signature audio files are generated “in the field” using the system100disclosed herein. For example, as different UAS vehicles130pass through the monitored area105and one or more audio signals is detected and recorded by one or more UAS sensor nodes110a-nin the monitored area105, those fast Fourier transformed and filtered audio signals can be stored as an audio file in memory235(ofFIG. 2) locally at the particular UAS sensor node110and/or centrally at the server (so that the audio file can be transmitted to other UAS sensor nodes110). In addition to the field-sensed audio file, as much information as can be determined about the UAS vehicle (e.g., brand, model, ID, motor stress level, payload, etc.) can also be stored in the memory235and/or transmitted to the central server115for distribution to one or more other UAS sensor nodes110.

As discussed herein, reference may be made to UAS vehicle brands. A UAS vehicle brand is made by a particular manufacturer and can include multiple different types of UAS vehicle models. As discussed herein, reference may also be made to UAS vehicle models. A UAS vehicle model is a specific UAS vehicle product manufactured by a specific entity, though it may, and typically will, make multiples of that specific product. The UAS vehicle model can include, for example, a make, a brand name, a product name, a stock keeping unit, etc. Those of ordinary skill in the art will recognize that different entities may manufacture one or many different UAS vehicle models within a UAS vehicle class.

Returning toFIG. 1, the environment100can include one or more UAS sensor nodes110a-n. These UAS sensor nodes110a-n(collectively “110”) can be positioned in any form or fashion to sense sound generated in the monitored space105. For example, the UAS sensor nodes110may be positioned along the perimeter of the monitored space105. In another example, the UAS sensor nodes110may be positioned centrally within the monitored space105and aimed toward the exterior of the monitored space105. In yet another example, the UAS sensor nodes may be clustered and/or may be positioned throughout the monitored space105in an array format. While the example embodiment ofFIG. 1shows four UAS sensor nodes110a,110b,110c, and110d, this is for example purposes only, as the number of UAS sensor nodes110can be any number and can be dependent on the size, location, topography, and other factors of the monitored space.

The example UAS sensor nodes110are each configured to individually sense, detect, and classify UAS vehicles130in the monitored space105. The example UAS sensor node can also be configured to transmit an alert to the central server115or to another device, such as a smartphone or hand-held electronic device upon detecting a UAS vehicle130. In one example, each UAS sensor node110a-ncan include a self-contained apparatus that may be positioned at any location for a particular monitored space105, including within a residence, within a building, or outside. Each UAS sensor node110a-nmay also include an exterior casing that is constructed from metal, hard plastic, soft plastic and/or a combination thereof. This exterior casing may resist leakage to allow for extended positioning of the UAS sensor node110in an outdoor environment.

In certain example embodiments, each UAS sensor node110a-ncan be assigned a unique identifier (e.g., a media access control (“MAC”) address) at the time of manufacture to enable a server or other electronic device to determine which UAS sensor node110a-nhas detected and identified a UAS vehicle130. Alternatively, a user may program or otherwise enter a unique name for each UAS sensor node110a-n. Each UAS sensor node110a-nmay then include the unique name or MAC address with any communication to the central server115or another electronic device (e.g., smartphone, hand-held electronic device, etc.) in order to uniquely identify the UAS sensor node110that is transmitting the information.

In addition, each UAS sensor node110a-nmay include a GPS transmitter250(ofFIG. 2) to determine and transmit the precise location of the UAS sensor node110a-nwithin a particular environment100to the central server115. This GPS data may be used by the central server115to determine the estimated (based on the location of the particular UAS sensor node) location of the UAS vehicle130or the precise location (based on triangulation of multiple detections of the UAS vehicle130from multiple UAS sensor nodes) of the UAS vehicle130.

The UAS vehicle detection environment100can also include a central server115or computer. The central server115can be a standard server computer or a cloud-based server computer. The central server115may include or otherwise be associated with suitable hardware and/or software for transmitting and receiving data and/or computer-executable instructions over one or more communication links or networks. The central server115may also include any number of processors for processing data and executing computer-executable instructions, as well as other internal and peripheral components currently known in the art or which may be developed in the future. Further, the central server115may include or be in communication with any number of suitable memory devices operable to store data and/or computer-executable instructions. For example, the central server115can be communicably coupled to one or more databases or memory storage devices (not shown) to store UAS vehicle audio files and detection events received from the one or more UAS sensor nodes110a-n. By executing computer-executable instructions, the central server forms a special-purpose computer or particular machine. As used herein, the term “computer-readable medium” describes any medium for storing computer-executable instructions.

The example central server115may be a computing device that includes any number of server computers, mainframe computers, networked computers, desktop computers, personal computers, mobile devices, smartphones, digital assistants, personal digital assistants, tablet devices, Internet appliances, application-specific integrated circuits, microcontrollers, minicomputers, and/or any other processor-based devices. Additionally, in certain example embodiments, the operations and/or control of the central server115may be distributed among several processing components. In addition to including one or more processors, the central server may further include one or more memory devices (or memory), one or more input/output (“I/O”) interfaces, and one or more network interfaces. The memory devices may be any suitable memory devices, for example, caches, read-only memory devices, random access memory devices, magnetic storage devices, removable storage devices, etc. The memory devices may store data, executable instructions, and/or various program modules utilized by the central server115and/or the UAS sensor nodes110a-n, for example, data files, an operating system (“OS”), and/or example UAS vehicle audio files.

The OS may be a suitable software module that controls the general operation of the central server115. The OS may also facilitate the execution of other software modules by the one or more processors. The OS may be any operating system known in the art or which may be developed in the future including, but not limited to, Microsoft Windows®, Apple OSX™ Apple iOS™, Google Android™, Linux, Unix, or a mainframe operating system.

The one or more I/O interfaces may facilitate communication between the central server115and one or more input/output devices, for example, one or more user interface devices, such as a display120, keypad, control panel, remote control, mouse, microphone, etc., that facilitate user interaction with the central server. In certain example embodiments, the display120may be situated locally with respect to the central server115. In other example embodiments, the display120may be positioned remotely from all or a substantial portion of the central server115. The display120can be any form of display known to those of ordinary skill in the art, including, but not limited to, a cathode ray tube (CRT) display, a plasma display, a light emitting diode (LED) display, an organic LED display (OLED), a touchscreen display, a heads-up display (HUD), a virtual reality display, or the like.

The central server115or computer can be communicably coupled to the one or more UAS sensor nodes110a-n. In one example embodiment, the central server computer115is communicably coupled to each of the one or more UAS sensor nodes110a-nvia one or more communications networks125. The network125may include one or more independent and/or shared private and/or public networks including the Internet or a publicly switched telephone network. In other example embodiments, the central server115may communicate with each of the UAS sensor nodes110a-nvia direct connections and/or communication links.

FIG. 2is a simplified block diagram of a UAS sensor node110a-nofFIG. 1, in accordance with one example embodiment of the disclosure. Referring now toFIGS. 1 and 2, the example UAS sensor node110a-ncan include a housing for the placement of components included in each node110a-n. In addition, the UAS sensor node110a-ncan include one or more microphones205. The microphones may be positioned within the housing, along an exterior of the housing, or provided adjacent to the housing and communicably coupled to other components within the housing. The example microphone205can be a directional or omnidirectional microphone.

The microphone205can be configured to have a sensitivity range within a frequency band associated with the sound generated by the motors of UAS vehicles130. For example, the microphone205can be configured to detect ultrasonic frequency bands. In addition, the microphone205may also be configured to have an acoustic range to detect sounds from a UAS vehicle130anywhere in the range of substantially 1 foot to substantially 1 mile from the microphone205and more particularly within one-half mile of the microphone205.

In certain example embodiments, each UAS sensor node110a-nmay include multiple microphones205. For example, when two or more microphones205are provided, each microphone205may be provided along an exterior of the node housing but may face a different direction from the housing. This may allow for an increased arc from the housing at which UAS vehicles130may be detected.

Each UAS sensor node110a-ncan also include a sound card210communicably coupled to the one or more microphones205and a processor220. For example embodiments where the UAS sensor node110includes multiple microphones205, the sound card210may be communicably coupled to and service the multiple microphones or a sound card210may be provided for each microphone205. The example sound card210is configured to record and digitize an audio signal sensed by the one or more microphones205. The sound card210may be any type of sound card known to those of ordinary skill in the art.

The sound card210may be configured to digitize the sound sample into a 16-bit, 32-bit, 64-bit, or 128-bit digital signal. In operation, the sound card210can include an analog-to-digital converter215to convert the audio signal received from the one or more microphones to a digital audio signal. In addition, the sound card210may be configured to break up or divide the digital audio signal into multiple digital audio segments of a desired length. In one example, the length of each digital audio segment is substantially one second. However, in other example embodiments, the length of each digital audio segment can be any other time length including any length within the range of substantially 0.01 seconds to substantially one second, and any time length within the range of substantially one second to substantially one minute. The length of time for each sample may be a user-configurable parameter selected by the user. The sound card may be configured to transmit the converted and divided digital audio segments to the processor220for additional processing and evaluation.

Each UAS sensor node110a-ncan also include one or more processors220. The one or more processors may be communicably coupled to one or more of the one or more microphones205, sound card210, analog-to-digital converter215, bandpass filter225, smoothing filter230, and one or more memory or data storage devices235. The one or more processors may also be operably coupled to a power supply240to provide electrical power for the one or more processors240. The one or more processors220may be implemented as appropriate in hardware, software, firmware, or combinations thereof. Software or firmware implementations of the one or more processors220may include computer-executable or machine-executable instructions written in any suitable programming language to perform the various functions described herein. Hardware implementations of the one or more processors220may be configured to execute computer-executable or machine-executable instructions to perform the various functions described herein. The one or more processors220may include, without limitation, a central processing unit (CPU), a digital signal processor (DSP), a reduced instruction set computer (RISC), a complex instruction set computer (CISC), a System-on-a-Chip (SoC), a microprocessor, a microcontroller, a field programmable gate array (FPGA), or any combination thereof for handling specific data processing functions or tasks. Each UAS sensor node110a-nmay also include a chipset (not shown) for controlling communications between the one or more processors220and one or more of the other components of the UAS sensor node110.

Each UAS sensor node110a-ncan also include a bandpass filter225communicably coupled to the processor220. In one example, the bandpass filter is a part of the programming provided in the processor220and the operations of the bandpass filter are conducted by the processor220. In one example embodiment, the bandpass filter225is a Butterworth bandpass filter or a maximally flat magnitude filter. The bandpass filter225can be configured to low-pass filter each digital signal segment processed by the sound card210. In one example, the cutoff frequencies for the bandpass filter can be anywhere in the range of substantially 1 kilohertz (kHz) to substantially 90 kHz and more preferably anywhere in the range of substantially 1 kHz to substantially 75 kHz, and even more preferably substantially 5 kHz and substantially 65 kHz.

Each UAS sensor node110a-ncan also include a smoothing filter230communicably coupled to the processor220. In one example, the smoothing filter is a part of the programming provided in the processor220and the operations of the smoothing filter are conducted by the processor220. In one example embodiment, the smoothing filter230is configured to smooth out noise in each of the fast Fourier transformed digital signal samples generated by the processor220. For example, the smoothing filter230can include a 25-point running mean filter that smooths out noise in the fast Fourier transformed digital signal samples. In other example embodiments, the smoothing filter230can employ anywhere in the range of a substantially 5-point to a substantially 50-point running mean filter and more preferably anywhere in the range of a 15-point to 35-point running mean filter.

Each UAS sensor node110a-ncan also include one or more memory or storage devices235communicably coupled to the processor220. Each memory or storage device235can be any suitable memory devices, for example, caches, read-only memory devices, random access memory devices, magnetic storage devices, removable storage devices, etc. The memory or storage devices235can be configured to include instructions for completing the processes and methods described herein. Further, the memory or storage devices can include one or more tables, listings, or schedules of UAS vehicle audio sample files that are used for comparison to the received and processed audio signals to determine if the received audio signals are associated with a UAS device130.

FIG. 3is an example data structure300of audio files of UAS vehicle audio samples stored within the memory or data storage devices235of each UAS sensor node110a-nofFIGS. 1 and 2, in accordance with one example embodiment of the disclosure. As shown inFIG. 3, the example data structure can include an audio file for each record stored in the data structure300. Associated with each audio file can be one or more pieces of information specifying the UAS vehicle130that generated the sound included on the audio file. In one example embodiment, the one or more pieces of information can include the brand or manufacture of the UAS vehicle130, the model of the UAS vehicle130, a unique identifier for the UAS vehicle130(e.g., serial number, tail number, registration number), the owner of the UAS vehicle130(e.g., based on tail number), and motor strain level (e.g., low strain, medium strain, high strain). In addition, or in the alternative, other information can be associated with each audio file. For example, instead of, or in addition to, motor strain level, fields identifying the specific weight of payload for the UAS vehicle, or information specifying the payload (e.g., camera, missile, explosive, etc.) may be included.

The example data structure300may be a dynamic data structure300in that it is capable of being constantly updated. For example, as new UAS vehicles130are created, testing on the vehicles130can be conducted to determine new coefficients and intercept data based on the new audio samples. The new coefficients and intercepts data, which are applied to the filtered and smoothed audio files collected at a particular UAS sensor node110a-das part of the comparison process, can be stored in the data structure300via the central server115passing the coefficients and intercept data and associated information to each UAS sensor node110a-nvia the network125. Further, as each UAS sensor node110a-dcollects audio signals that cannot be associated with a particular UAS vehicle audio sample in the data structure, the newly collected audio signal can either be transmitted by the particular UAS sensor node110a-dto the central server115via the network125for further analysis and determination of coefficients and intercept data to be used in the comparison process and/or can be added to the data structure300as an audio file along with any other information known about the particular UAS vehicle130. As such, each UAS sensor node110a-nis a learning computer capable of detecting new UAS vehicle signals and storing them for future comparison. It should be appreciated that in other embodiments, the data structure300may include fewer or additional fields. Moreover, while the data structure300is shown as a flat file, in other example embodiments the data structure300may be hierarchal with a highest level corresponding to UAS vehicle brands, a second level corresponding to UAS vehicle models, and a lowest level corresponding to motor strain levels.

Each UAS sensor node110a-ncan also include one or more power supplies240electrically coupled to the processor220. In addition, the power supply240may be directly or indirectly coupled to any one or more of the other components of the UAS sensor node. The power supply240can be any currently known or future developed power supply and can be configured to provide all of the power needs for the respective UAS sensor node110a-n. In one example embodiment, the power supply240is a direct current (DC) power supply, such as a battery. In this example, the battery can be a rechargeable battery. Further, the UAS sensor node110can include a solar panel or array, a turbine or the like to recharge the power supply in order to increase the battery life. In other example embodiments, the power supply240is an alternating current (AC) power supply. While not shown, the node110a-ncan also include a combination of power supplies, include back-up power supplies to further increase battery life for each UAS sensor node110a-nwhile positioned out in the field.

FIG. 5is a flow chart illustrating an example method500for detecting and identifying UAS vehicles130in a monitored area, such as the monitored area105, in accordance with one example embodiment of the disclosure. Referring now toFIGS. 1-3 and 5, the example method500begins at the start block and proceeds to block502, where an audio signal is received by at least one UAS sensor node110a-n. In certain example embodiments, the audio signal can be received at more than one of the UAS sensor nodes (e.g.,110a,110b, and110c) and the signal strength, the GPS receiver245in each node110a-c, and the positioning of each UAS sensor node110a-c, as determined based on the GPS receiver data, can be used to triangulate the location of the source of the audio signal.

In one example, the received audio signal is an analog audio signal. The audio signal can be received by the one or more microphones205of the UAS sensor node110. In one example embodiment, each microphone205is a USB microphone that is configured to receive and measure audible and ultrasonic sound using a micro electro-mechanical system (MEMS) technology sensing element that operates similar to a human ear drum. In one example embodiment, the frequency response for the receiving microphone205is within a range of substantially 2 kHz to substantially 95 kHz of sound. The source of the audio signal can be a UAS vehicle130. However, the source of the audio signal could alternatively be an animal, a person, nature-based sounds (e.g., wind, rain, running water, rustling leaves, etc.), automobiles, other machines, and the like. One benefit of the disclosed method is an initial review is conducted to determine is the source of the audio signal is even likely to be from a UAS vehicle before conducting a more in depth secondary analysis of the audio signal.

At block504, the received analog audio signal is converted to a digital audio signal. For example, the microphone205can pass the analog audio signal to the sound card210, which can use an analog-to-digital converter215to convert the analog audio signal to a digital audio signal. In one example embodiment, the audio signal is sampled at a rate of 200 kHz with a 16-bit resolution. However, other sample rates between 30 kHz to 400 kHz and other resolutions, including, but not limited to, 8-bit, 32-bit, 64-bit, and 128-bit resolutions are also within the scope of this disclosure.

At block506, the sound card210or another portion of the UAS sensor node110a-nseparates or divides the digital audio signal into digital signal segments of a predetermined length. One example of a one-second digital signal segment is the digital signal segment605shown in the graph600ofFIG. 6. In one example embodiment, the predetermined length of each digital signal segment605is 1 second or 1 Hz. Alternatively, other predetermine lengths for the digital signal segments605, including those within the range of between substantially 0.01 seconds to substantially one minute, can be used. Separating the digital signal into smaller segments605, as disclosed in block506, is beneficial in the analysis of the audio signal because it reduces or eliminates Doppler effects on the audio signal by faster moving UAS vehicles130. Further, by using the example predetermine length of one second, the system is able to generate an updated prediction of UAS vehicle activity in the monitored area105every second.

At block508, a counter variable X, representing each digital signal segment605created in block506from the digital audio signal, is set equal to one. At block510, the first digital signal segment605is passed through a bandpass filter225at the UAS sensor node110to create a bandpassed digital sample X. For example, the processor220can pass the first digital signal segment605through the bandpass filter225or can conduct the bandpass filtering on the digital signal segment605. One example of a one-second long bandpassed digital sample X is the bandpassed digital sample705shown in the graph700ofFIG. 7. In one example embodiment, the bandpass filter is a Butterworth bandpass filter225with cutoff frequencies of substantially 5 kHz and substantially 65 kHz.

At block512, a fast Fourier transform is applied to the bandpassed digital sample X705. In one example, the fast Fourier transform is conducted on the bandpassed digital signal sample705by the processor220or another portion of the particular UAS sensor node110. The bandpassed digital sample X705can be Fourier transformed using a 1 Hz bin size, in one example embodiment. Alternatively, any other bin size could be used. The Fourier transform of the bandpassed digital sample X, breaks the waveform of the bandpassed digital sample X into an alternate representation characterized by sine and cosine. One example of a one-second long fast Fourier transformed digital signal sample X is the fast Fourier transformed digital signal sample805shown in the graph800ofFIG. 8.

At block514, the fast Fourier transformed digital signal sample X805is passed through a smoothing filter230to create a smoothed digital signal sample X. One example of a one-second long smoothed digital signal sample X is the smoothed digital signal sample905shown in the graph900ofFIG. 9. For example, the processor220may pass the Fourier transformed digital signal sample X805to the smoothing filter230to smooth out the sample into a sample like that shown at905ofFIG. 9. Alternatively, the processor220may conduct the smoothing process. In one example, the smoothing process includes passing the Fourier transformed digital signal sample X805though a multi-point running mean filter to smooth out the noise in sample X. In certain example embodiments, the multi-point running mean filter is a 25-point running mean filter. In another example, the multi-point running means filter can be anywhere within the range of a substantially 5-point to a substantially 50-point running mean filter and more preferably anywhere in the range of a substantially 15-point to a substantially 35-point running mean filter.

At block516, the processor220compares the smoothed digital signal sample X905to sound signals similar to sound files generated by UAS vehicles130. For example, the processor220can analyze the smoothed digital signal sample X905via a 1-class support vector machine algorithm with a linear kernel. The 1-class support vector machine algorithm is an unsupervised learning approach to define a binary function that evaluates to a nonzero value in the input-space region where most of the data lies. In this instance, the 1-class support vector machine algorithm is based off what is disclosed in Estimating the Support of a High-Dimensional Distribution, Bernhard Scholkopf et al., Microsoft Research, Microsoft Corporation, November 1999 (available via the Internet at http://www.cs.cmu.edu/˜aarnold/ids/postal.pdf), the entire contents of which is incorporated herein by reference for all purposes.

In the example embodiment described herein, the input data (a training set of filtered, fast Fourier transformed, smoothed audio previously recorded with known UAS vehicles130) are mapped to feature space using a linear kernel. Once in feature space, a hyperplane is established between the origin and the mapped input data such that the margin between the origin and these inputs is maximized. Establishing this hyperplane sets a boundary for the testing of new audio data, like that received in block502ofFIG. 5.

When this model is applied to new processed audio data, such as the smoothed digital signal sample X905, determining if a UAS vehicle130is present is a matter of calculating which side of the hyperplane the smoothed digital signal sample X905finds itself. If the evaluation of the smoothed digital signal sample X905reveals a positive value, then the audio sample received at block502is similar enough to the previously recorded drone audio such that we can classify that a drone is present. Conversely, a negative value shows the audio is too different from any drone we have seen and is therefore an outlier and likely not a drone. This approach has worked very well in out processing chain for weeding out audio data from the rest of the environment that is not a drone.

The support vector machine algorithm acts as an outlier rejection method for determining if the smoothed digital signal sample X905could not possibly be originating from a UAS vehicle130. The objective in block516is to separate those samples that are potentially originating from a UAS vehicle130from everything else in the acoustic background.

At block518, an inquiry is conducted to determine if the smoothed digital signal sample X is similar to an audio signal from a UAS vehicle130. In one example, the inquiry is conducted by the process and the determination is made based on the comparison in block516. If the smoothed digital signal sample X is not similar to an audio signal from a UAS vehicle, then the NO branch is followed to block522. In block522, an inquiry is conducted to determine if there is another digital signal segment to evaluate. For example, if the original audio signal received at the microphone205was ten seconds in length and the signal was divided up into one second increments, then there would be 10 digital signal segments to evaluate for the particular audio signal. In one example, the determination can be made by the processor220of the particular UAS sensor node110. If there is not another digital signal segment to evaluate, the NO branch can be followed back to block502to receive the next audio signal at the microphone. On the other hand, if there is another digital signal segment to evaluate, the YES branch can be followed to block524, where the counter variable X is incremented by one. The process then returns to block510to pass the next digital signal segment X through the bandpass filter.

Returning to the inquiry of block518, if the smoothed digital signal sample X is similar to an audio signal generated by a UAS vehicle130, the YES branch can be followed to block526, where the processor220can store the smoothed digital signal sample X in memory235. Alternatively, the processor220can transmit the smoothed digital signal sample X to the central server115which can store it in memory or a database associated with the central server115. At block528, sample UAS vehicle sound files are received and/or accessed. For example, the sound files can be provided by the central computer115to each UAS sensor node110a-nprior to the start of the analysis and can be updated in real-time or near real-time. In one example, the sample UAS sound files can be stored in the memory235of each UAS sensor node110a-nin a form substantially similar to that shown and described for the data structure300ofFIG. 3.

At block530, the processor220of the UAS sensor node110a-ncan conduct logistical regression analysis on the smoothed digital sample X905. In one example embodiment, the processor220utilizes a one-versus-rest (OVR) logistic regression on the smoothed digital sample X905to determine the identity of a detected UAS vehicle130. In this example, logistic regression seeks to treat class differentiation as a 0 or 1 binary problem, where 1 represents an “in-class” sample and 0 represents an “out-of-class” sample. Here, OVR logistic regression treats each model of UAS vehicle130as an individual class and seeks to differentiate it against all other classes. A separate OVR logistic regression model is trained for each UAS class. For example, if there are ten classes of UAS vehicle130, ten separate logistic regression OVR models are actually trained. In the training framework, the logistic regression algorithm minimizes a logistic cost function to build an N-dimensional hyperplane mapping between class “0” and class “1”, and a set of coefficients with an intercept is output that defines this hyperplane.

For example, once a new audio sample is gathered, such as in block502, and processed, such as in blocks504,506, and510-514, a decision function is calculated using the smoothed digital signal sample X905and the coefficients/intercepts from each of the OVR models stored in the UAS sensor node110. The output of the decision function, a scalar value, represents the distance between an individual (“one”) UAS vehicle class and the remaining UAS vehicle classes, where a greater distance represents more similarity to a particular UAS vehicle class. The identity of the UAS vehicle130is thus predicted as the UAS vehicle class that led to a maximum decision function value. So if you have ten classes represented in the audio data, you will get ten decision function values.

Class representations when training the logistic regression model can be as granular or as coarse as desired. A class could be a particular UAS represented by tail number, or as broad as all quadcopters or all fixed-wing vehicles, etc. At block532, the processor220or another portion of the UAS sensor node110a-ncompares the smoothed digital sample X to each stored UAS sound file in the data structure300or in another location. For example, the decision function values are converted into probabilistic measures of similarity between one UAS vehicle class and the rest of the UAS vehicle classes. Each decision function value described above is input into a logistic function (standard mathematical logistic function). The output of this calculation is between 0 and 1. A high value, for example 0.9, would represent greater similarity to the individual UAS vehicle class (“one”) than the “rest” of the UAS vehicle classes. This calculation is repeated for all decision function values and a summation is performed over all outputs.

At block534, the processor220or another portion of the UAS sensor node110a-ngenerates a probability score for each UAS sample audio file based on the probability that the smoothed digital sample X matches the particular UAS sample audio file. For example, the values of each individual UAS vehicle class can then be divided by the sum determined in the prior block to give the UAS vehicle class relative probabilities that one UAS vehicle130is present over the rest of the potential UAS vehicle classes.

At block536, the processor220or another portion of the UAS sensor node110a-nevaluates all of the probability scores generated at block534for each of the UAS sample audio files and determines the UAS sample audio file that has the highest probability score that the smoothed digital sample X matches the particular UAS sample audio files. For example, the greatest probability is always associated with the UAS vehicle class having the greatest decision function value. In certain examples, each of the probabilities can be compared by the processor220of the UAS sensor node110to a predetermined threshold value. If, based on the comparison, the processor220determines that none of the probabilities are greater than the predetermined threshold value, then confidence that the received audio signal in block502is from one particular UAS vehicle130versus one or more other UAS vehicles is low and the identity of the specific type and payload of the UAS vehicle130, from which the audio signal was received at block502, is set as indiscernible.

In one example, identifying the highest probability score can be accomplished by organizing the UAS sample audio files by probability score with regard to the particular smoothed digital sample X. Alternatively, one-on-one matching of probability scores for each UAS sample audio file may be conducted by the processor220or another portion of the UAS sensor node110a-nto determine the highest score. While the example embodiment describes identifying the highest probability score of a match between the UAS sample audio files and the smoothed digital sample X, in another example embodiment, comparison and generation of probability scores could be based on identifying the UAS vehicle classes that are least likely to be a match, for which, the lowest probability scores would be identified.

At block538, the processor220or another portion of the UAS sensor node110a-ncan identify the UAS vehicle details associated with the UAS sample audio file having the highest probability score. For example, the processor220, based on the identification of the UAS sample audio file having the highest probability score in block536, can access the matching record in the data file300containing the UAS sample audio files and determine the details of the particular UAS vehicle130. At block540, the processor220or another portion of the UAS sensor node110a-ncan determine either the location of the particular UAS sensor node110a-nor the estimated location of the UAS vehicle130using the GPS receiver245for the one or more UAS sensor nodes110that detected the UAS vehicle. In certain example embodiments, data from only a single GPS receiver245is evaluated to determine an estimated location. In other example embodiments, GPS receiver data from multiple UAS sensor nodes that have detected the UAS vehicle130are evaluated and triangulation techniques are used to estimate the location, direction, and/or speed of the UAS vehicle130.

At block542, the processor220transmits the UAS vehicle details for the highest probability UAS sample audio file and location information to the central server115. In addition, or in the alternative, the UAS vehicle details for the highest probability UAS sample audio file and the location information can be sent directly to a user's smartphone or other digital display device. At block544, the identifying information for the UAS vehicle130associated with the UAS sample audio file having the highest probability score is displayed on the display device120or another display device of the user along with the location, speed, and/or direction information for the UAS vehicle130. In one example embodiment, the system can generate a graphical user interface that includes a map or grid that includes the monitored area105. The details of the UAS vehicle130can be generated on the map or grid at the location determined based on the one or more GPS receiver data. The process can then continue to block522to determine if there is another signal segment to evaluate.

Although unmanned aircraft detection systems methods, functions, components, and parts have been described herein in accordance with the teachings of the present disclosure, the scope of coverage of this patent is not limited thereto. On the contrary, this patent covers all embodiments of the teachings of the disclosure that fairly fall within the scope of permissible equivalents.

Although example embodiments of the disclosure have been described, one of ordinary skill in the art will recognize that numerous other modifications and alternative embodiments are within the scope of the disclosure. Furthermore, while various example implementations and architectures have been described in accordance with example embodiments of the disclosure, one of ordinary skill in the art will appreciate that numerous other modifications to the example implementations and architectures described herein are also within the scope of this disclosure.

Accordingly, blocks of the block diagrams and steps of the flow diagrams support combinations of means for performing the specified functions, combinations of elements or steps for performing the specified functions and program instruction means for performing the specified functions. It will also be understood that each block of the block diagrams and step of the flow diagrams, and combinations of blocks in the block diagrams and steps of the flow diagrams, may be implemented by special-purpose, hardware-based computer systems that perform the specified functions, elements or steps, or combinations of special-purpose hardware and computer instructions.