Book Description

Cognitive or Fully Adaptive Radar (FAR) is an area of research that is inspired by biological systems and focuses on developing a radar system capable of autonomously adapting its characteristics to achieve a variety of different tasks such as improved environment sensing and spectral agility. The FAR framework implements a dynamic feedback loop (sense, learn, adapt) within a software defined radar (SDR) system and the environment that emulates a Perception Action Cycle (PAC). The implementation of the FAR framework on SDRs relies on solver-based optimization techniques for their action selection. However, with the increase of optimization complexity, there becomes a heavy impact on time to solution convergence, which limits real-time experimentation. Additionally, many "cognitive radars" lack a memory component resulting in repetitive optimization routines for similar/familiar perceptions. Using an existing model of the FAR framework, a neural network inspired refinement is made. Through the use of neural networks, a subset of machine learning, and other machine learning concepts, a substitution is made for the solver-based optimization component for the FAR framework applied to single target tracking. Static feedforward neural networks and dynamic neural networks were trained and implemented in both a simulation and experimentation environment. Performance comparisons between the neural network and the solver-based optimization approaches show that the static neural network based approach had faster runtimes which lead to more perceptions and sometimes better performance through lower resource consumption. A comparison between the simulation results of the static feed-forward neural network, the dynamic recurrent neural network, and the solver is also made. These comparisons further support the notion of neural networks being able to provide a memory component for cognitive radar through the incorporation of learning, moving toward truly cognitive radars. Additional research was also performed to further show the advantages of neural networks in radar applications of rapid waveform generation. The FAR framework is also extended from the single-target tracking FAR framework to a multiple target tracking implementation. The multi-target implementation of the FAR framework displays the benefits of adaptive radar techniques for multiple target environments where complexity is increased due to the increased number of targets present in the scene as well as the need to resolve all targets. Refinements and additions were made to the existing cost functions and detection/tracking frameworks due to the multiple target environment. Experimental and simulated results demonstrate the benefit of the FAR framework by enabling a robust adaptive algorithm that results in improved tracking and efficient resource management for a multiple target environment. In addition to this, the Hierarchical Fully Adaptive Radar (HFAR) framework was also applied to the problem of resource allocation for a system needing to perform multiple tasks. The Hierarchical Fully Adaptive Radar for Task Flexibility (HFAR-TF)/Autonomous Decision Making (ADM) work applies the HFAR framework to a system needing to engage in balancing multiple tasks: target tracking, classification and target intent discernment ("friend", "possible foe", and "foe"). The goal of this Ph.D. is to combine these objectives to form a basis for establishing a method of improving current cognitive radar systems. This is done by fusing machine learning concepts and fully adaptive radar theory, to enable real-time operation of truly cognitive radars, while also advancing adaptive radar concepts to new applications.