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New radars can detect Drones carrying IEDs, biological or chemical agents, a critical technology for Counter UAS systems

The increased commercialization of drones is increasing the risks that these drones can be used by terrorists and criminals. The small drones such as a quadcopter or model airplane are readily available and it is highly probable that existing technology would allow unfriendly forces to retrofit them, giving the aircraft the ability to deliver weaponized explosives or hazardous materials.

 

Researchers are developing counter-drone technology, also known as counter-UAS, C-UAS, or counter-UAV technology, referring to systems that are used to detect and/or intercept unmanned aircraft. The first step for Counter-UAV technology is the Detection , that is the collection of some phenomenological information captured by a sensor like radar signature, acoustic signature, Photon reflectance, IR signature and  RF emission. Some of systems used are Passive visible imagers (UV, visible, NIR), Passive thermal imagers (SWIR, MWIR, LWIR), LIDARS, ESM receivers acoustic sensors and magnetic detection systems.

 

However, their low operational altitude along with small size, small RCS and small IR signature of the UAV makes it a difficult target for most of the common air defense systems such as antiaircraft guns and shoulder-fired IR missiles. The sophistication of commercial  drones, is also increasing and require very little input from the pilot.

 

Radar detects the presence of small unmanned aircraft by their radar signature, which is generated when the aircraft encounters RF pulses emitted by the detection element. These systems often employ algorithms to distinguish between drones and other small, low-flying objects, such as birds. ). ECM or RF systems Identifies the presence of drones by scanning for the frequencies on which most drones are known to operate. Algorithms pick out and geo-locate RF-emitting devices in the area that are likely to be drones.

 

The detection of  low, slow, and small (LSS) unmanned aerial systems (UAS) pose many challenges . In white House incident, the Radar systems designed to detect flying objects such as airplanes, missiles and larger military drones failed to pick up the small two foot diameter quadcopter drone as it entered the restricted area around the White House. Relying on visual observation to detect drones is equally ineffective; at a distance of several hundred feet, drones can become all but invisible to the naked eye.

 

“Small UASs are rapidly becoming low-cost aerial platforms for hostile reconnaissance, targeting, and weapon delivery. Unlike traditional air targets, small UASs: 1) fly at low altitudes (e.g., < 400 ft) which make them easily hidden by complex terrain, 2) move at slow speeds (e.g., < 90 kts) which make them difficult to differentiate from other movers, and 3) are small in size (e.g., < 55 lbs.) making them difficult to sense,” says DARPA.

 

 

Detecting Drones with Doppler-Based Radar

DMT, LLC started making radar systems in 2002, and rapidly grew to build and sell these systems across the globe. DMT uses Doppler radar to track objects on land, sea and in the air. In 2015 it began testing its pulsed-Doppler radars against commonly available drones. Today it produces the longest-range, drone-detection radar systems on the market.

 

When people hear “Doppler radar” mentioned, they most often think of weather radars. Pulsed-Doppler security radars use similar technology, but instead of trying to detect and display weather, these radars choose to detect and ignore weather while seeking intruders.

 

In addition to seeing through weather, the security Doppler radar also tries to ignore the reflected radar energy from vegetation, waves, and more. Anything that is being detected by the radar that is not the desired object is referred to as clutter. And the ability to remove clutter is commonly known as clutter rejection.

 

A radar signal is a sinusoidal wave of RF (radio frequency) energy that is transmitted in the air like ripples on water. The wavelength is the distance (usually in centimeters) over which the wave repeats. It is equal to the inverse of the transmitted frequency of the radar. A Doppler radar monitors the reflected RF waves from objects over time. The typical pulsed radar system will transmit bursts (pulses) of RF energy. The time between the pulses is used to measure the range to objects.

 

The pulsed-Doppler radar transmits groups of pulses (i.e., the radar dwell), and then monitors changes in frequency across those pulses. The changes in the frequency are due to the stretching and compressing of the waves of the radar signal by the movement of the object away from or toward the radar, respectively. This is identical to the change of tone due to the stretching and compression of sound waves from the horn of a car that passes by an observer. A Fast-Fourier Transform (a mathematical algorithm) applied to the digitized reflected radar signal supplies a measure of all frequency changes from the object, and frequency can be converted to radial velocities. Radial velocities are the speeds of objects in the direction of the radar. As long as the proper waveform is used, all velocities present on the object will be measured. For example, for a human the main body velocity (from the torso in this case) as well as the arm and leg radial velocities will be measured. If you watch the Doppler response long enough, the gait of a person can be established.

 

The Doppler velocities from other than the main body are often referred to as micro-Doppler signatures. For quadcopter drones, the responses from the blades and motors constitutes the micro-Doppler signatures. With sufficient testing, a database of signatures unique to each drone can be generated. Each future Doppler signature measured by that radar can be compared to that database for drone characterization. The stored signatures can also be used to eliminate objects that are not drone-like.

 

If a radar can detect a small drone, it will also detect birds. The main-body signature of a bird can resemble that of a drone, especially for larger birds that are flying in straight-line paths or spiral-ing slowly on thermal updrafts. The micro-Doppler signature, however, will be substantially different.

 

Characterization of drones using micro-Doppler requires:

  • The correct frequency, PRF (pulse repetition frequency), and pulse width;
  • A sufficient number of pulses to obtain the micro-Doppler components;
  • High sampling rates in the analog-to-digital hardware;
  • FFT analysis of data collected over a group of pulses (the dwell), and clutter rejection algorithms that operate in the frequency domain.

 

The ability to resolve an object is a function of the transmitted frequency. At a minimum, the size of the object of interest should be several times greater than the wavelength of the radar. For example, the largest dimension of a DJI Phantom 4 is 15 inches, or 38.1 cm. Assuming a factor of 3 (object size to wavelength), the wavelength should be about 2.4 GHz or greater. However, the Phantom 4 is made of plastic that is largely transparent to the radar. The battery, camera, and motors are the main radar reflective components of the drone. The maximum dimension of the Phantom 4 battery is about 4.5 inches, or 11.43 cm. Therefore, the radar transmitted frequency should be 7.9 GHz or greater. And the frequency cannot be too high, or the radar will not be able to see through rain, fog, and smoke. If the drones are flying over trees on the way to the radar, the clutter rejection of the tree limbs and leaves becomes imperative. Clutter rejection of vegetation is much more difficult with frequencies greater than 12 GHz. Of course, as the drone drops in size, the required frequency will also change.

 

The PRF will determine the maximum speed that can be measured unambiguously. For example, an X-Band radar with a 4 kHz PRF rate can measure speeds up to 72 mph. The radar will continue to detect objects faster than that speed, but the wrong speed and wrong direction of motion will be reported. In terms of drones, the PRF is important for micro-Doppler feature detection. DMT has found that certain selections of PRF will amplify micro-Doppler features.

 

The signal-to-noise ratio (S/N) is the ratio of reflected energy from the object to internal noise sources. People often use the radar range equation to calculate the maximum range of the radar for a given object, and the minimum detectable S/N is one of the equation’s parameters. If S/N is insufficient, then additional transmitted power or a bigger antenna or both is needed. The bigger the pulse width, the greater the transmitted power becomes, and the greater the range. Since drones are small, the signal-to-clutter ratio (S/C) also affects range. The S/C is the amount of reflected energy from the object of interest relative to the amount of reflected energy from everything else. The antenna beamwidth and the pulse width determine S/C. The smaller the pulse width, the greater the S/C. So, increasing transmitted power by increasing the pulse width may improve range, but only if the S/C isn’t overly reduced. Micro-Doppler signatures can be blurred or corrupted if S/C is too low. The ability to change pulse width is important for a drone detection radar.

 

The number of pulses determine the Doppler resolution of a radar. An X-Band radar with 256 pulses in the dwell will have a Doppler resolution of 7.8125 Hertz, which is equal to a speed resolution of 0.283 mph. Drop the number of pulses to 128, and the speed resolution goes up to 0.567 mph. As the speed resolution decreases, the micro-Doppler improves. If the speed resolution is too large, the main body signature dominates and the ability to characterize diminishes to unusable levels.

 

Once the parameters are set on the pulsed-Doppler radar, all the Doppler signatures from the drone may be measured. By creating a database of Doppler signatures  from a wide variety of drone types and aspect angles, the radar will easily characterize drones and eliminate all other false-positives, such as bird signatures.

 

Blighter’s Battle-proven A400 Series Counter-UAV Radar Enhanced to Better Detect Low, Slow and Small Drones.

Blighter Surveillance Systems Ltd (www.blighter.com) (“Blighter”), a British designer and manufacturer of electronic-scanning (e-scan) radars and counter-drone solutions, has enhanced its Blighter A400 series counter-drone air security radar to better detect and report low, slow and small unmanned aircraft systems (UAS) or drones.

 

The ruggedised Blighter A400 series counter-UAV radar has been optimised with the addition of 40° antennas, D3 technology and an adaptable DSP platform to better detect and report Nano, Micro and Miniature drones at ranges from 10 metres up to 3.6 km (2.2 miles) and larger drones and aircraft at ranges up to 10 km (6.2 miles), even while ‘on-the-move’. Blighter engineers are also providing a software development kit to systems integrators, primes and the military to ease integration of the radar with other sensors, C2 systems, as well as with kinetic and non-kinetic disruptors.

 

Angus Hone, CEO, Blighter Surveillance Systems, said: “Countering the threat caused by rogue drones is now a global issue and an increasing concern for the military, government and homeland security forces across every continent. Our Blighter A400 series radars are battle proven as the detection element of the strategic counter-UAS AUDS system deployed since 2016 in Iraq by US forces and more recently at London Gatwick Airport.”

 

The new U40 antennas are now available to increase vertical elevation coverage from 30 to 40-degrees. Digital Drone Detection (D³) technology with sensitivity boost has also been added. This will allow the A400 series to better extract the tiny radar reflections from modern plastic bodied drones even when flying close to the ground or near buildings where clutter reflections are relatively large.

 

Mark Radford, co-founder and chief technology officer (CTO), Blighter Surveillance Systems, said: “Our symmetric transmit/receive e-scan architecture allows pin-point focus in complex cluttered environments and its use of ‘Ku Band’ spectrum with 2cm wavelength is ideal for interaction with small drones. What’s more, our e-scan technology combined with micro-Doppler signal processing allows us to manage clutter on-the-move yet still detect low, slow and small threats in the air and on the ground.”

 

The Blighter A400 series radars are modular non-rotating, e-scan systems using power efficient PESA (passive electronically scanned array) and FMCW (frequency modulated continuous wave) technologies to provide reliable, small and slow drone detection even in complex environments. Blighter radars are deployed in 35 countries to deliver round the clock all-weather protection along borders, for coastal facilities, at military bases, and to guard critical national infrastructure such as airports, oil and gas facilities and palaces

 

U.K. British company Blighter Surveillance Systems Ltd. — which designs and makes electronic-scanning (e-scan) radars and surveillance solutions — is under contract to supply its counter-UAV [unmanned aerial vehicle] radar technology to Liteye Systems (Centennial, Colorado and Colchester, U.K.) as part of Liteye’s recently signed multimillion-dollar contract with the U. S. Department of Defense (DoD) to deliver numerous containerized anti-unmanned aircraft systems (known as C-AUDS) to the DoD by the end of the fourth quarter of 2018.

 

 

 

 

 

 

 

About Rajesh Uppal

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