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Frequency-modulated continuous wave (FMCW) radars are an important class of radar systems, and they are quite popular because of their simpler architecture and lower cost. A fundamental problem in FMCW radars is the nonlinearity of the voltage-controlled oscillator (VCO), which results in a range of measurement errors, problems in multitarget detection, and degradation in synthetic aperture radar (SAR) images. In this paper, we first introduce a novel upsampling theory, then propose new algorithms to improve range accuracy and multitarget detection capability. These improvements are demonstrated both by simulations and actual lab experiments on a 2.4 GHz radar system. There are several techniques reported in the literature for VCO nonlinearity correction, but what makes the proposed approach different is that we focus on real-time processing on low-cost hardware and optimize the design subject to this constraint. We first developed an optimal upsampling theory which is based on almost-causal finite impulse response (FIR) filters. Compared to the sinc-based noncausal interpolation-based upsamplers, the proposed approach is based on using interpolation filters with few number of coefficients. Furthermore, interpolators are trained for a specific class of signals rather than a highly general signal set. Therefore, the proposed approach can be implemented on lower-cost hardware and perform quite well compared to more expensive systems.

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A Kalman filter(KF)-based feedforward-feedback controller is proposed using the internal model(IM)-principle for accurate tracking of a desired trajectory, and fault-tolerant control of a quadrotor, despite input and output sensor measurements being affected by unknown disturbances, measurement noise and model perturbations. The quadrotor model is unstable and nonlinear. Its input is a nonlinear function of the roll, pitch and yaw, and its output is its position in the ground-fixed coordinates. The quadrotor is subject to model uncertainties, disturbances including wind gusts, aerodynamic drags, gravitational load, and Coriolis forces, and the inputs and the outputs are affected by unknown stochastic disturbances and measurement noise. Predictive analytics is used to estimate the true input by exploiting its smoothness and the randomness of the noisy input. The nonlinear system is better approximated using the linear parameter-varying (LPV) model described by piecewise-linear Box-Jenkins model at each operating point, than by conventional approximation techniques. The system and the associated Kalman filter (KF) are identified using novel emulator-generated data by minimizing the KF residual so that identified models are accurate, consistent and reliable. The proposed tracking, fault-tolerant control, and design of the KF residuals-based design of soft sensor were successfully evaluated on a simulated laboratory-scale quadrotor.