## 1 Introduction  
System identification is routinely used to estimate the parameters of the mathematical model of a system from the measured data [1]. Estimating vehicle model parameters has been an intriguing problem for the evaluation of ride comfort [2]. A detailed literature survey is presented in [3]. In general, the parameter estimation procedures can be divided into two-groups: time-domain approaches [4] and frequency-domain approaches [5]. Moreover, in terms of the data collection aspect, the studies can be divided into two-groups: system identification using excitation data [6] and system identification on a vehicle driven on different road profiles [7]. Time-domain system identification using excitation input is the major method followed in the literature. [PERSON] and [PERSON] proposed a robust technique to identify suspension parameters based on linear regression of impulse-momentum equations by using randomised time intervals for integration [8]. [PERSON] used a hardware-in-the-loop vehicle simulator, applied rectangular bump motion to the vehicle and used torque inputs for excitation in all-wheel-drive vehicles [9]. [PERSON] and [PERSON] used a linearised seven-degree-of-freedom model and a singular perturbation method to identify the parameters of an active suspension system [10]. [PERSON] et al. used a Jacobian approach to determine suspension and damping rates of a planar double-wishbone suspension mechanism and compared their results against ADAMS simulations [11]. [PERSON] et al. also worked on a double-wishbone quarter car model to estimate suspension damping when subjected to harmonic and bump/pothole excitation [12]. Numerous frequency-domain system identification approaches using specific excitation signals are also available in the literature. [PERSON] et al. utilized IMU signals only to estimate the transfer functions for wheels and active suspension actuators in the presence of noise and uncertainties [13]. Similarly, [PERSON] and [PERSON] used accelerometer inputs for suspension parameter estimation and compared estimation results of a Kalman filter, particle filter and neural networks [14]. [PERSON] et al. used a matrix inversion approach to incorporate vehicle dynamic constraints in a full-scale four-post rig for system identification with noisy experimental data [15]. [PERSON] et al. worked on motorcycles and used assumptions for the distribution of vehicle forward speed to identify suspension characteristics with a frequency-domain approach [16]. [PERSON] and [PERSON] used a sine sweep to identify the parameters of a non-linear full vehicle model and the shock absorber hysteresis characteristics on an experimental test rig [6]. [PERSON] used dynamic programming to identify the vehicle vibration parameters by applying experimental data collected from a motor vehicle with noisy instrumentation [17]. Similar to this paper, employing different road profiles for vehicle ride parameter estimation has been studied in the literature. In the time domain, [PERSON] et al. drove their vehicle over bumps and used the continuous time response data demonstrating the vehicle dynamic response to estimate the suspension parameters [4]. Similarly, [PERSON] et al. used four accelerometer measurements on a vehicle traversing over a single bump to estimate vehicle parameters using subspace identification, model updating and direct continuous time system identification techniques [18]. [PERSON] benefited from sinusoidal test track profiles in the time domain, and he proposed a method to identify the suspension system parameters using a Laplace domain reduced order to handle the complexity between a full car and a quarter car model [7]. [PERSON] et al. estimated vehicle and tire parameters for a vehicle with different passenger and luggage loads using a Kalman filtering-based approach in real-life driving profiles [19]. [PERSON] et al.