Machine Learning and Artificial Intelligence in Radar Technology

Technology is always changing. A large percentage of it will make our lives easier by enhancing how we learn or go about our daily jobs in ways that were never thought before. Artificial intelligence and machine learning stand at the forefront of technology’s future, including their use in radar technology. The purpose of this article is to define what AI and machine learning are, how they relate to each other and what their role may be in radar technology.

Simply put, artificial intelligence is technology that incorporates human intelligence to machines. This is accomplished by the machine following a set of problem-solving algorithms to complete tasks.

The roots of AI are rooted in different research disciplines, including computer science, futures and philosophy. AI research is separated into streams that relate to the AI application’s objective of “thinking vs. acting” or “human-like decision vs. ideal, rational decision.” This utilizes four research currents:

1)Cognitive Modeling – thinking like a human
2) Turing Test – acting like a human when interacting with humans
3) Laws of Thought – a weak AI pretends to think while a strong AI is mind that has mental states
4) Rational Agent – the intelligence is produced through the act of agents that are characterized by five traits that include:
Operating autonomously
Perception of their environment
Persisting over an extended time period
Adapting to change
Creating and pursuing goals
Artificial intelligence agents can be categorized into four different types:

1) Simple reflex agent that reacts to sensor data
2) Model-based reflex agent that considers the agent’s internal state
3) Goal-based agent that determines the best decision to achieve its goals based on binary logic
4) Utility-based agent whose function is to maximize its utility
5) Any of the four agents can become a learning agent through the extension of its programming.

The term machine learning is used to describe techniques that can be used to solve a variety of real-world problems by using computer systems that are able to solve problems through learning instead of being programmed to solve problems.

Some machine learning systems are able to work without constant supervision. Others use supervised learning techniques that apply an algorithm on a set of known data points to gain insight on an unknown set of data to construct a model.

A third type, reinforcement learning continually learns from its observations that are obtained through interacting with its environment through iteration.

Creating a machine learning model typically employs three main phases:

Model initiation where the user defines the problem, prepares and processes the chosen data set and chooses the applicable machine learning algorithm
Performance estimation where various parameter combinations that describe the algorithm are validated and the best performing one is chosen
Deployment of model to begin solving the task on the unseen data
Machine learning adapts and mimics the cognitive abilities of human beings, but in an isolated manner.

Despite their differences, there is some confusion regarding what each technology does. This confusion is often exacerbated by the fact that both terms are often mistakenly used interchangeably. In reality, AI depends on machine learning to accomplish its goals.


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