Abstract
Currently, human operators provide cognition in a radar system. However, advances in the “digitization” of radar front-ends, including digital arbitrary waveform generators (AWG) and advanced high performance embedded computing (HPEC) make it possible to vary all key radar parameters (power, pulse length, number of pulses, pulse repetition frequency (PRF), modulation, frequency, polarization) on a pulse-by-pulse basis within ns or ms and over a wide operating range. This timescale is much faster than the decision-making ability of a human operator. The cognitive-inspired techniques in radar, that are intensively developing last years, mimic elements of human cognition and the use of external knowledge to use the available system resources in an optimal way for the current goal and environment. Radar systems based on the perception-action cycle of cognition that senses the environment, learns relevant information from it about the target and the background and then adapts the radar to optimally satisfy the needs of the mission according to a desired goal are called cognitive radars. In the article, recent ideas and applications of cognitive radars were analyzed.
Highlights
The research and analysis made by the NATO Science & Technology (S&T) Organization and underlying in the report “Science & Technology Trends 2020-2040
For an adaptive radar to be cognitive, it has to satisfy four processes [7]: 1) perception-action cycle for maximizing information gain about the radar environment computed from the observable data; 2) memory for predicting the consequences of actions involved in illuminating the environment and parameter selection for environmental modeling; 3) attention for prioritizing the allocation of available resources in accordance with their importance; 4) intelligence for decision-making, whereby intelligent choices are made in the face of environmental uncertainties
Where R KA – KA sample estimate of R based on direct methods of prior knowledge incorporation into the adaptive filtering process; c1, c2, c3 0 – weighting factors that reflect the relative confidence in the various estimates
Summary
There is a need for processes to transform both structured data (e.g. machine learning techniques with varying levels of complexity, from regression to neural networks) and unstructured data (e.g. using deep learning and natural language processing) into insights and foresight for decision-makers These tools may be applied to critical defense and security challenges such as sensors that can pre-process information and provide adaptive use of frequencies, bandwidth and signals – cognitive radars. For an adaptive radar to be cognitive, it has to satisfy four processes [7]: 1) perception-action cycle for maximizing information gain about the radar environment computed from the observable data; 2) memory for predicting the consequences of actions involved in illuminating the environment and parameter selection for environmental modeling; 3) attention for prioritizing the allocation of available resources in accordance with their importance; 4) intelligence for decision-making, whereby intelligent choices are made in the face of environmental uncertainties. The purpose of the article is to analyze recent ideas and applications of cognitive radars
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