Powered by technological advances, commercial drones have wide applications in areas such as photography, search, and rescue. However, the popularity of drones has raised technical and safety challenges for drone management and supervision. Traditional detect-before-track sensing approaches have reduced performance due to drones' low radar cross section, low velocity, and high maneuverability, and a long integration time is required to perform the spectrogram-based drone characteristics estimation.
In this thesis, a coherent electromagnetic scattering model of the drone is applied to the track-before-detect algorithm to provide a better detection performance in low signal-to-noise ratio cases and jointly estimate the dynamic state of the drone, including range, velocity, rotation frequency, and signal intensity from drone body and rotors. With the help of tracking results, a fusion of spectrogram-based characteristics estimation approaches is developed to estimate the constructional parameters of the drone, and a novel model-based number of rotor and multi-rotation frequency estimation method is proposed. The algorithms are first verified with simulation data, achieving 85%-95% detection probability at the SNR level below 5 dB and an estimation accuracy up to 96% in the number of rotor estimation. The algorithms are also validated with the experimental data, achieving agreement with the estimation results.

Radar-based classification of human activities and gait have attracted significant attention with a large number of approaches proposed in terms of features and classification algorithms. A common approach in activity classification attempts to find the algorithm (features plus classifier) that can deal with multiple activities analysed in one study such as walking, sitting, drinking and crawling. However, using the same set of features for multiple activities can be suboptimal per activity and not take into account the diversity of kinematic movements that could be captured by diverse features. In this paper, we propose a hierarchical classification approach that uses a large variety of features including but not limited to energy features like entropy and energy curve, physical features like centroid and bandwidth, image-based features like skewness extracted from multiple radar data domains. Feature selection is used at each step of the hierarchical model to select the best set of features to discriminate the target activity from the others, showing improvements with respect to the more conventional approach of using a multiclass model. The proposed approach is validated on a large dataset with 1078 recorded samples of varying length from 5 s to 10 s of experimental data, yielding 95.4% accuracy to classify six activities. The approach is also validated on a personnel recognition task to identify individual subjects from their walking gait, yielding 83.7% accuracy for ten subjects and 68.2% for a significantly larger group of subjects, i.e., 60 people.