The rapid development of Advanced Driver Assistance Systems (ADAS) necessitates enhanced performance in automotive radar systems, with Phase Modulated ContinuousWave (PMCW) radar emerging as a key technology due to its high resolution, interference resistance, and robust performance. Despite these advantages, PMCW radar faces challenges such as high computational complexity and Doppler-induced range sidelobes. This thesis addresses these challenges by proposing an adaptive block FFT correlation method to reduce computational complexity and enhance processing efficiency, ensuring reliable target detection. Additionally, we tackle Dopplerinduced range sidelobes by introducing code diversity and novel frame designs for MIMO systems, leveraging cyclic shifts and Hadamard matrices to balance sidelobe attenuation and sequence set size requirements. Through extensive analysis and simulations, the proposed methods demonstrate significant improvements in radar performance, especially in detecting weak targets behind strong reflectors. The findings contribute to developing more efficient, reliable, and scalable PMCW radar systems for advanced automotive applications.

Occupancy grid maps are fundamental to autonomous driving algorithms, offering insights into obstacle distribution and free space within an environment. These maps are used for safe navigation and decision-making in self-driving applications, forming a crucial component of the automotive perception framework. An occupancy map is a discretized representation of a chosen environment that is constructed using point cloud information obtained from sensor modalities like LiDAR and radar. In this project, we formulate the problem of estimating the occupancy grid map using sensor point cloud data as a sparse binary occupancy value reconstruction problem. We utilize the inherent sparsity of occupancy grid maps commonly encountered in automotive scenarios. Besides, the spatial dependencies between the grid cells are exploited to provide a better reconstruction of the boundaries of the objects inside the range of the map and to suppress the false alarms emerging from the reflections coming from the road. To address sparsity and spatial correlation jointly, we propose an occupancy grid estimation method that is based on pattern-coupled sparse Bayesian learning. The proposed method shows enhanced detection capabilities compared to two benchmark methods, based on qualitative and quantitative performance evaluation with scenes from the automotive datasets nuScenes and RADIal.

People counting data in offices is used in many applications like HVAC system control and space management to increase comfort, decrease energy consumption and optimise space utilisation. In contrast to past approaches using imaging modalities that tend to be either expensive or intrusive, we consider single-pixel thermopile sensors for people counting. These sensors may already be deployed as part of a smart lighting system to provide temperature data for HVAC controls. Firstly, a statistical sensor model for thermopile temperature measurements is proposed. The proposed people counting method enhances the CUSUM RLS algorithm to estimate temperature change caused by people entering or leaving. We estimate mean temperature changes upon detection of an occupancy event, and then estimate the people count using a maximum likelihood on the estimated temperature change. Finally, PIR vacancy data is merged with the people count estimation to increase accuracy. We obtain an average counting error of 0.11 and 0.19 for 90% of the instants respectively when considering 15 minute windows for simulated and experimental datasets. A second aspect of the thesis considers the problem of commissioning plan detection. We leverage the two-sided CUSUM signals to address this problem. The two-sided CUSUM scores for a pair of sensors are used to calculate similarity measures; these features are used in a Random Forest Classifier to detect commissioning changes of the sensor pair. Using simulated data with the thermopile signal model, we show that the proposed method achieves a true positive rate (determining the correct layout) of 90.2% and false positive rate of 1.3%.