In the last five years, significant developments and applications have been made within radar communications. Signal Processing for Joint Radar-Communications delves into the recent advances within th ...Celý popis
In the last five years, significant developments and applications have been made within radar communications. Signal Processing for Joint Radar-Communications delves into the recent advances within the theory and applications of joint radar-communications (JRC) and includes the challenges that are still faced today. Bringing together newly written expert contributions from leading researchers within the field of Joint Radar-Communications, the book tackles key JRC signal processing challenges such as common waveform design for both radar and communications systems, receiver processing including interference mitigation methods, learning and cognition, resource allocation, jamming and clutter, optimization methods, and automotive JRC. It presents possible solutions to these challenges and highlights some future research directions.
The goal of this book is to further contribute to the diffusion of newly developed JRC tools into the radar and communications communities and to illustrate recent successes in applying modern signal processing theories to solving core problems in JRC. The contributors present new results on algorithmic methods and applications of JRC in diverse areas, which include autonomous vehicles, waveform design, information theory, privacy, security, beamforming, estimation theory, and sampling. This reflects the increasing number of applications in signal processing and communications. Research activities covered in the book include recognizing and solving convex optimization problems that arise in applications, deriving powerful algorithmic methods, utilizing the theory of convex problems to characterize and gain insight into the optimal solution and bounds on performance, developing techniques for exploiting problem structure in interior-point methods for large scale optimization, and convex relaxations of hard, non-convex problems
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