A system identification method with roughly quantized data using semidefinite programming

This paper provides a semidefinite programming approach to an identification of linear systems with roughly quantized outputs. Measurement data sampled from low resolution sensors have large quantization errors, which deteriorate the identification accuracy. The identification problem is formulated into nonlinear and nonconvex programming, however, the proposed approach provides a new method to obtain an approximate optimal value via recursive semidefinite programming. Numerical examples demonstrate that we can estimate both plant parameters and true output and show the effectiveness of the proposed method.