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The use of heterogeneous, non-collocated measurements for nonlinear structural system identification is explored herein. In particular, this paper considers the example of sensor heterogeneity arising from the fact that both acceleration and displacement are measured at various locations of the structural system. The availability of non-collocated data might often arise in the identification of systems where the displacement data may be provided through global positioning systems (GPS). The well-known extended Kalman filter (EKF) is often used to deal with nonlinear system identification. However, as suggested in (J. Eng. Mech. 1999; 125(2):133-142), the EKF is not effective in the case of highly nonlinear problems. Instead, two techniques are examined herein, the unscented Kalman filter method (UKF), proposed by Julier and Uhlman, and the particle filter method, also known as sequential Monte Carlo method (SMC). The two methods are compared and their efficiency is evaluated through the example of a three degree-offreedom system, involving a Bouc-Wen hysteretic component, where the availability of displacement and acceleration measurements for different DOFs is assumed. displacement response measurements is essential for the effective monitoring of structural response and the determination of the parameters governing it. Displacement and/or strain information in particular is of great importance when it comes to permanent deformations.The availability of acceleration data is usually ensured since this is what is commonly measured. However, most nonlinear models are functions of displacement and velocity and hence the convenience of acquiring access to those signals becomes evident. In practice, velocities and displacements can be acquired by integrating the accelerations although the latter technique presents some drawbacks. The recent advances in technology have provided us with new methods of obtaining accurate position information, through global position system receivers for instance. In this paper the potential of exploiting combined displacement and acceleration information for different degrees of freedom of a structure (non-collocated, heterogeneous measurements) is explored. In addition, the influence of displacement data availability is investigated in Section 5.3.The nonlinearity of the problem (both in the dynamics and in the measurement equations as will be shown) requires the use of sophisticated computational tools. Many techniques have been proposed for nonlinear applications in Civil Engineering, including the least squares estimation (LSE) [1,2], the extended Kalman filter (EKF) [3][4][5], the unscented Kalman filter (UKF) [6,7] and the sequential Monte Carlo methods (particle filters, PF) [8][9][10][11]. The adaptive LSE schemes depend on measured data from the structural system response. Since velocity and displacement are not often readily available, for their implementation these signals have to be obtained by integration and/or differentiation schemes. As mentioned above, this poses difficu...

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