How can you incorporate prior knowledge and physics-based models into radar and sonar signal processing?

1 Machine learning and AI for radar and sonar

Machine learning and artificial intelligence are fields of computer science that aim to create systems that can learn from data and perform tasks that require human-like intelligence. Machine learning and AI can be applied to radar and sonar signal processing to enhance the capabilities of existing methods, such as detection, classification, tracking, imaging, and estimation. For example, machine learning and AI can help to reduce false alarms, identify complex targets, adapt to changing environments, and optimize system parameters.

2 Prior knowledge and physics-based models

Prior knowledge and physics-based models are sources of information that can help to guide and constrain the machine learning and AI algorithms for radar and sonar signal processing. Prior knowledge refers to any information that is known or assumed about the system, the target, or the environment, such as the geometry, the motion, the material properties, or the statistical characteristics. Physics-based models are mathematical representations of the physical phenomena that govern the generation, propagation, and reflection of the waves, such as the wave equation, the scattering theory, or the Doppler effect.

3 Incorporating prior knowledge and physics-based models

Incorporating prior knowledge and physics-based models into machine learning and AI for radar and sonar signal processing can be done in various ways, dependent on the type and amount of data, the complexity and accuracy of the models, and the desired outcome of the system. Data augmentation, feature extraction, model-based learning, and model inversion are common approaches. Data augmentation involves generating synthetic data or modifying existing data with knowledge to increase quantity and diversity of training data for machine learning algorithms. Feature extraction transforms raw data into a more meaningful representation using knowledge to highlight salient features and reduce irrelevant noise. Model-based learning uses knowledge to design or choose machine learning algorithms that are suitable for the problem domain and that can capture underlying structure of the data. Model inversion uses machine learning algorithms to infer unknown parameters or variables from observed data.

4 Examples of applications

Incorporating prior knowledge and physics-based models into machine learning and AI for radar and sonar signal processing can yield many benefits. Radar target recognition, for instance, involves using machine learning and AI to identify the type and state of a target from its radar signature. Prior knowledge and physics-based models can help generate realistic synthetic signatures, extract relevant features, and design classifiers for different target categories. Sonar image reconstruction is another application, in which prior knowledge and physics-based models can be used to augment the measurement with simulated data, transform the measurement into a suitable domain, and reconstruct an image using inverse problems or generative models. Lastly, radar waveform design involves optimizing the shape and parameters of a radar signal to achieve a specific objective with the help of prior knowledge and physics-based models. These models can define the objective function, constrain the feasible space, and guide the optimization process.

5 Challenges and future directions

Incorporating prior knowledge and physics-based models into machine learning and AI for radar and sonar signal processing presents a few challenges. For example, there must be a balance between the complexity of the models and their accuracy; they should be accurate enough to capture the essential physics, but not so complex that they cause computational issues or overfitting. Additionally, the knowledge and models may be uncertain or variable, so they must be validated, updated, and adapted as needed. Furthermore, the machine learning and AI outputs should be consistent with the knowledge and models, as well as understandable to humans. Despite these challenges, incorporating prior knowledge and physics-based models into machine learning and AI offers a promising direction for advancing these technologies. Future research could explore innovative ways to combine, refine, and exploit these sources of information to improve performance, reliability, and efficiency for various applications.

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?