Underlying Technologies for Advanced Radar

Applied Tech Review | Friday, December 30, 2022

Electromagnetic spectrums are becoming increasingly contested battlefields. With increasing electronic countermeasure sophistication, fifth-generation fighter aircraft are less detectable, and major world powers are investing in cyber warfare technology to dominate the spectrum.

FREMONT, CA: The electromagnetic spectrum is becoming an increasingly contested battlefield. The sophistication of electronic countermeasures is increasing, fighter aircraft of the fifth generation are more challenging to detect, and most major world powers are investing in cyber warfare technology that will allow them to dominate the spectrum. Many devices use the spectrum for wireless connectivity as cellular providers roll out 5G and vehicle manufacturers promote V2X communication.

Scientists and engineers who design and test intelligence, surveillance, and reconnaissance (ISR) systems face new hurdles due to this progression. As engineers are tasked with creating increasingly complicated systems utilizing more cost- and time-efficient techniques, these obstacles present chances for innovation.

Gallium Nitride for Front-End Components

Gallium Nitride (GaN), regarded by some as the most significant semiconductor breakthrough since silicon, can work at a significantly greater voltage than standard semiconductor material. Higher voltage indicates more efficiency; therefore, RF power amplifiers and attenuators utilizing GaN consume less energy and generate less heat due to increased voltage. As more suppliers of GaN-based RF components with production-ready, dependable products join the market, the utilization of GaN-based amplifiers has expanded.

The growth of active electronically scanned array (AESA) radar systems requires this technology. Each of the hundreds or thousands of antennas in an AESA has its phase and gains control. These radar systems electronically steer beams using a phased array of transmitters and receivers without physically moving the antenna. Compared to conventional radars, these radar systems are gaining popularity due to their higher power on target, spatial resolution, and increased robustness. For instance, even if one element of the array fails, the radar will continue to function. Increased usage of GaN amplifiers in AESA radars should result in improved performance, as similar output power may be achieved with smaller form factors, and less cooling is required.

As the sophistication of GaN-based applications and solutions increases, so does the importance of matching component-level test results with those at the system level. Traditional component testing techniques employing vector network analyzers provide a precise, narrowband view of forward and reflected gain and phase. However, this common method's continuous wave (CW) stimulus does not adequately represent the component's final signal environment. Use the broad flexibility of vector signal analyzers and vector signal generators to generate pulses and modulated stimuli more reflective of real-world applications and their settings. Combining this capability with S-parameter analysis is a way of component-level testing that is becoming increasingly strategic.

Evolving FPGA Technology for Cognitive Techniques

FPGA technology is also constantly evolving. Modern FPGAs include substantially more logic, offer higher processing power per watt, and handle data streaming up to 150 Gb/s with dedicated IP blocks. Five years ago, such techniques were just inconceivable, but today's FPGAs' improved computational capacity makes them possible.

New FPGA technology enables the implementation of machine learning techniques into cognitive radar, which is one area of innovation. These strategies make radars more sensitive to their surroundings, providing more actionable intelligence. Instead of pre-programmed operating modes (searching mode, tracking mode, etc.), machine learning permits radars to adjust automatically to the optimal operating parameters, such as operating frequency and waveform kinds. Additionally, machine learning enables features such as automatic target recognition (ATR) and knowledge-assisted operation.

While aerospace and defense firms have utilized FPGA technology for many years, we have also witnessed the development of more advanced FPGA design tools. Higher-level tools can increase development efficiency by streamlining the transfer of host-based algorithms to FPGAs and incorporating low-level HDLs into the design. Through the abstraction of board infrastructures, such as PCI Express, JESD204B, memory controllers, and clocking, LabVIEW FPGA also benefits from the close NI hardware-software interaction. This transfers the focus of FPGA development from board support to algorithm design, thereby reducing development effort without compromising performance. Even for software engineers and scientists with no previous VHDL or Verilog experience or hardware engineers with tight deadlines, more abstracted FPGA tools can be a game-changer in reducing development cycles.

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