A constrained joint optimization approach to dynamic sensor configuration

Through intelligent integration of sensing and processing functions, the sensing technology of the future is evolving towards networks of configurable sensors acting in concert. Realizing the potential of collaborative real-time configurable sensor systems presents a number of challenges, including the need to address the massive global optimization problem resulting from incorporating a large array of control parameters. The paper proposes a systematic approach to addressing complex global optimization problems by constraining the problem to a set of key control parameters and recasting a mission-oriented goal into a tractable joint optimization formulation. Using idealized but realistic physical models, a systematic methodology to approach complex multi-dimensional joint optimization problems is used to compute system performance bounds for dynamic sensor configurations.