That’s why they require scrutiny before reaching the test range, let alone the field. But ideas are cheap-actually implementing them can be time-consuming and costly.
And what if the radar has difficulty determining targets against a cluttered background? Machine-learning, Gaussian-based approaches could be more effective than human operators at distinguishing targets from clutter. In this case, adaptive or cognitive techniques such as spectrum interference avoidance would increase frequency agility so that the radar could operate in less-congested spectrum. Consider the impact of new algorithms on performance: If a radar system is drowned out by other signals or, worse, actively jammed, it will not effectively operate in a congested and contested spectrum. Frequently, innovation comes in the form of novel waveforms, algorithms, and architectures, and new RF or digital components. And there are countless ideas to bring new capability to radar systems.
