Abstract: This paper investigates the multi-sensor allocation strategy problem for linear system identification in a networked environment. A binary quantization mechanism is introduced to save communication bandwidth. For addressing the redundancy and channel congestion issues during data transmission, a differential event driven communication mechanism is proposed, and its communication rate is provided. It is shown that such mechanism has the capability of complete information recovery. Based on the available data at the receiving end, identification algorithms for various finite impulse response systems are constructed, and their convergence performance is analyzed. Furthermore, the multi-sensor allocation problem under constrained communication resources is modeled as a constrained optimization problem, and an improved genetic algorithm is designed to give the optimal solution. Finally, a numerical example is used to verify the correctness and effectiveness of theoretical results.

Key words: multi-sensor, system identification, event driven mechanism, constrained communication resources