# US 11,307,003 B2 · Blimp-based aerial UAV defense system

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- Number: US 11,307,003 B2
- Document ID: US11307003B2
- Status: Granted
- Kind: B2
- Application number: 17/398,067
- Filed: 2021-08-10
- Published: 2022-02-24
- Granted: 2022-04-19
- Earliest claimed: 2020-08-24
- Assignees: King Abdulaziz University
- Applicants: King Abdulaziz University
- Inventors: Ahmed Barnawi
- Web page: https://patentgrep.com/patent/US11307003B2

## CPC

- F41H 11/02: Anti-aircraft or anti-guided missile {or anti-torpedo} defence installations or systems (cartridges or missiles for producing smoke or for dispensing radar chaff or infrared material F42B5/15, F42B12/48, F42B12/70; {informative reference: details of radar or sonar systems G01S7/00; jamming or electronic countermeasures G01S7/38, G01S7/495, H04K3/00})
- B64U 10/30: Lighter-than-air aircraft, e.g. aerostatic aircraft
- G06N 20/00: Machine learning
- B64U 2101/00: UAVs specially adapted for particular uses or applications
- B64U 2201/202: using tethers for connecting to ground station
- F41H: ARMOUR; ARMOURED TURRETS; ARMOURED OR ARMED VEHICLES; MEANS OF ATTACK OR DEFENCE, e.g. CAMOUFLAGE, IN GENERAL
- B64U: UNMANNED AERIAL VEHICLES [UAV]; EQUIPMENT THEREFOR
- G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS

## Abstract

A system, method, and non-transitory computer readable medium that detects trajectories of unmanned aerial vehicles (UAV) approaching a protected site is described. Airborne defense agents (ADAs) located at a fixed radius from the protected and equidistant from one another detect acoustic signals emitted by an approaching UAV. Circuitry included in each ADA use the detected acoustic signals to determine a direction and a distance of each UAV. A base station having a control center (BS-CC) located in the protected site communicates with the ADAs to aggregate direction and distance data from the ADAs. Using the aggregated direction and distance data, the BS-CC predicts routes towards the protected site of the approaching UAV and alerts the protected site of the predicted route of the approaching UAV.

## Claims

### Claim 1

A blimp-based aerial UAV defense system, comprising:
a plurality of airborne defense agents (ADAs), wherein each ADA includes a blimp located at a fixed radius from a protected site and equidistant from each other ADA, wherein each ADA equipped with:
a directional microphone array configured to detect acoustic signals emitted by a UAV;
a first computing device including a first computer-readable medium comprising first program instructions, executable by first processing circuitry, to cause the first processing circuitry to determine a direction and a distance of each approaching UAV of a plurality of unmanned aerial vehicles (UAV) from the ADA;
a GPS receiver configured to locate the ADA in three-dimensional space;
a transceiver;
a base station configured with a wideband communications link to communicate with the transceiver of each ADA and the protected site;
a control center (BS-CC) located within the base station and configured with a second computing device including a second computer-readable medium comprising program instructions, executable by second processing circuitry, to cause the second processing circuitry to aggregate the directions and distances of the approaching UAVs to predict routes towards the protected site and to alert the protected site of the predicted route of each approaching UAV,
a plurality of fixed towers of height H, each tower located at a fixed radius from the protected site and equidistant from each other tower;
a plurality of tethers, each tether of length L, wherein a top portion of each fixed tower is connected to one of the tethers of the plurality of tethers;
each blimp having a lower mount attached to one tether of the plurality of tethers; and
wherein each blimp has a motor, a propeller, and navigation circuitry configured to hold the first computing device and the directional microphone array of the ADA at a fixed height L+H above the ground and to hover the blimp at a steady position.

### Claim 2 (depends on 1)

The system of claim 1, further comprising:
wherein the first computing device includes switching circuitry configured to switch the power of each directional microphone ON and OFF in an alternating sequence; and
wherein the second processing circuitry is configured to transmit control signals to the ADAs to switch the directional microphone arrays ON and OFF simultaneously.

### Claim 3 (depends on 2)

The system of claim 2, further comprising:
wherein the first processing circuitry is configured to convert the acoustic signals from the time domain to the frequency domain, identify a set of frequency components and estimate the distance to the sound source.

### Claim 4 (depends on 3)

The system of claim 3, further comprising:
wherein each directional microphone of the directional microphone array is oriented to receive acoustic signals from a different angle;
wherein each first computing device includes circuitry configured to measure an amplitude, A, of each acoustic signal during the alternating ON periods;
wherein each first processing circuitry is configured to:
detect an angle of arrival, ω, of the acoustic signal from the angle of the directional microphone receiving the greatest amplitude; and
estimate the distance between each ADA and a UAV by measuring a propagation delay, τ, of the acoustic signal having the greatest amplitude.

### Claim 5 (depends on 4)

The system of claim 4, further comprising:
wherein the second processing circuitry is further configured to predict the route of an approaching UAV by triangulating the acoustic signals received by the directional microphone arrays of three equidistant ADAs.

### Claim 6 (depends on 5)

The system of claim 5, the second processing circuitry further comprising a machine learning processor configured to predict the route of each approaching UAV.

### Claim 7 (depends on 6)

The system of claim 6, further comprising:
wherein the second processing circuitry is further configured to estimate the speed of the approaching UAV by subtracting the distances estimated by each of three equidistant ADAs at a first ON period from the distances estimated at a second ON period and dividing the difference by the time between the first ON period and the second ON period.