The present study deals with a new identification algorithm from binary output measurements. The study focuses on the class of Finite Impulse Response (FIR) systems. The proposed algorithm is based on the estimation of correlation functions. A geometric interpretation is proposed and leads to a formulation of the algorithm using a Singular Value Decomposition (SVD). A convergence analysis is proposed showing the mean-square convergence with a rate of$\mathscr{O}\left(N^{-1}\right)$, Monte Carlo simulations are proposed to confirm performance.

This paper presents a subspace identification al-gorithm in the case of binary measurements on the output. The algorithm is a three-step algorithm. It requires the input signal to be periodic and the knowledge of the noise distribution. An analysis is provided and introduces to the well behavior of the algorithm. Numerical simulations confirm the analysis and good performance of the proposed algorithm.

In this paper we propose a first identification algorithm of system operating in closed-loop based on binary measurements both on the input and the output. The proposed approach in the paper is based on the estimation of the correlation function of the input and output from binary data. Some simulation results are then given in order to illustrate the performance of the proposed algorithm.