By Dr. Arik D. Brown
Counter unmanned aerial system (C-UAS) mission capability protects maneuver forces globally. It provides crucial situational awareness and neutralization against unmanned aerial systems (UAS) to maintain airspace advantage.
The proliferation of UAS and their suitability for nefarious intentions has made them a high priority focus area for both commercial and DoD applications.
Current UAS in the Military Market
COTS (commercial off-the-shelf) UAS are inexpensive and easily procurable, providing an advanced level of capability for invading protected air spaces that have critical infrastructure and personnel who need to be defended.
Larger UAS have increased travel distance and cargo carrying capability, making them more dangerous. For expanded lethality, UAS can easily be retrofitted to carry kinetic weapons and non-kinetic payloads (electronic attacks or EAs). This can lead to loss of life, damage to structural assets, or denial of service for communications and radar systems.
UAS Swarms vs. Current C-AUS Technology
The current emergence and development of swarms of UAS has accelerated the need to provide counter-swarm (c-swarm) capabilities to augment existing C-UAS. In order to develop c-swarm strategies and capabilities, it is important to understand how current C-UAS technology performs against c-swarm threats.
Detecting and tracking a large number of small objects is challenging, and the spatial diversity of flight formations make swarms difficult to defend against.
DoD C-Swarm Advancements
As c-swarms continue to rapidly evolve, the DoD has developed a variety of detection and countermeasure systems. There has been a concerted effort to commonize the acquisition and fielding of systems so that the most effective, expeditionary, and easy-to-use solutions are provided to the warfighter.
In support of this, the Joint C-sUAS Office (JCO) has released the recommended “interim C-sUAS list.” System commonality (multi-mission) capabilities provide greater leverage for low acquisition and operational costs, and a smaller logistics footprint.
C-UAS operational systems such as MADIS (above) use a radar as the primary sensor integrated with other detection systems and effectors. This multi-layered approach is essential in combating UAS swarms. RADA USA’s RPS-42 radar system is the primary C-UAS detection sensor for the MADIS system.
Radar Performance Considerations for C-Swarm
Radars transmit RF energy and filter the reflected returns from the environment to detect and track desired targets of interest. Primary filtering is based upon the target characteristics, which include:
- Azimuth angle
- Elevation angle
Based upon these characteristics, radars are typically placed into one of three categories:
- 2D Radar – This category of radar traditionally provides range and azimuth angle measurement for target detection. This capability is somewhat limited because the target’s elevation angle and velocity are not measured. For c-swarm applications, an additional sensor is required, not for an added layer of defense, but to supplement the lack of elevation angle measurement.
- 3D Radar – Radars in this category provide range, azimuth angle, and elevation angle of detected targets. These radars have improved performance over 2D radars for c-swarm application. However, the range resolution of a 3D radar limits its ability to differentiate between drones that are flying in a tight formation.
- 4D Radar – These radars provide the radar performance required for c-swarm application. They measure the range, azimuth angle, elevation angle, and velocity of the target. Pulse-Doppler processing allows for the additional velocity measurement, providing optimal radar performance for combatting UAS swarms.
2D radars can also employ pulse-Doppler processing, however, they are still limited by the lack of an elevation angle measurement. The low-fidelity 2D radar track information may not be enough to cue a sensor, such as an EO/IR camera.
Range resolution refers to a radar’s ability to decipher between individual targets that are close in range. It is defined by the time duration of the transmitted pulse and is expressed as:
The relationship between range resolution and pulsewidth is shown below.
Making the pulsewidth smaller to achieve the desired resolution minimizes the transmitted energy and thereby reduces the range performance of the radar. To counteract this effect, pulse compression is used to support larger pulsewidths with an effective compressed pulsewidth for improved range resolution.
For UAS swarms, the resolution will be dictated based on the class of UAS. This becomes challenging for small UAS (e.g., DJI Mavic equivalents ~0.3 m in size) where the desired range resolution would be in the range of several meters. This can be problematic for a radar that does not employ pulse-Doppler processing (3D radar).
For ranges greater than 1-3 km, the range resolution will also be compromised. This is because in order to discriminate targets, the pulsewidth must be reduced to get the desired resolution. This reduces the power on target for detection at maximum ranges.
Pulse-Doppler processing is a common technique used by 4D radars to measure the velocity of detected targets. It is extremely powerful and can mitigate the limitations of range resolution described previously. Signal returns are shifted in frequency by a Doppler frequency shift.
By filtering the signal with a Doppler filter bank, signal returns can be discriminated based upon their Doppler frequency and converted to velocity using the relation:
For software-defined radars, the measured Doppler velocity resolution can easily be adjusted and made smaller (< 1 m/s).
For situations where a swarm of UAS is closer than the range resolution of the radar, the targets can still be resolved using their velocity, as long as the UAS in the swarm fly at velocities that differ by an amount greater than the Doppler velocity resolution. This implies that the optimal radar for C-Swarm is a 4D radar.
Pulse-Doppler processing is effective for discriminating closely-spaced targets moving at different velocities. However, there is an additional impact on performance that must be considered. The Doppler velocity resolution is inversely proportional to the radar dwell time.
So, an optimization must be done that maintains the overall scan or frame rate while also minimizing the Doppler resolution (making the resolution as small as possible, i.e. being able to resolve targets with less than 0.5 m/s velocity difference).
The azimuth and elevation angle accuracy of a radar is determined by the antenna beamwidth and signal-to-noise ratio (SNR). For practical purposes, the radar waveform used for detection is designed to have sufficient SNR, so the accuracy is primarily dependent on beamwidth (BW). BW is a function of the radar antenna length and the operational RF frequency (𝐵𝑊 ≅ c / f • L).
These two parameters, L and f, can be optimized to achieve the desired BW. Actual systems usually can achieve an accuracy of at least one-tenth of the BW as shown above.
For c-swarm applications, the accuracy should ideally be within a degree or less (as a benchmark a 1° error at 1km is ~17m) to minimize angle error as a function of increased detection range. Using a layered c-swarm approach, with one or multiple effectors and a camera sensor, target angles will be reported with angle accuracies proportional to the radar antenna beamwidth.
C-Swarm Radar Parameters for Effective Performance
An effective c-swarm holistic solution must have a layered approach to address the ongoing threats of UAS swarms. Radar systems are a key technology partner for addressing UAS swarms, providing detection, identification, tracking, and classification capabilities.
4D radars are the preferred radar solution as they provide range, velocity, and angle measurements for detection and tracking and provide target track information to c-swarm system partner technologies.