A review of RFI mitigation techniques in microwave radiometry

Radio frequency interference (RFI) is a well-known problem in microwave radiometry (MWR). Any undesired signal overlapping the MWR protected frequency bands introduces a bias in the measurements, which can corrupt the retrieved geophysical parameters. This paper presents a literature review of RFI detection and mitigation techniques for microwave radiometry from space. The reviewed techniques are divided between real aperture and aperture synthesis. A discussion and assessment of the application of RFI mitigation techniques is presented for each type of radiometer.Peer ReviewedPostprint (published version

Evaluation and development of methods for time-frequency analysis of heart rate variability

Non-stationary signals are very common in nature, consider for example speech, music or heart rate. Using the concept of time-frequency analysis this thesis studies the performance of different time-frequency distributions of both simulated and real non-stationary signals. The signals studied are linear and non-linear frequency modulated (FM) signals. Two methods are studied to increase performance of the signals' time-frequency distributions. Since lag-independent kernels perform well with slow varying frequency modulated signals both methods use these. One method uses filtering with compact support lag-independent kernels and the other uses a penalty function with multitapers corresponding to lag-independent kernels. These methods are then evaluated using two performance measures and the results are used to improve the time-frequency distributions of heart rate variability signals. The thesis suggests that both of these methods improve the time-frequency distribution of such signals

Spectral Analysis for Signal Detection and Classification : Reducing Variance and Extracting Features

Spectral analysis encompasses several powerful signal processing methods. The papers in this thesis present methods for finding good spectral representations, and methods both for stationary and non-stationary signals are considered. Stationary methods can be used for real-time evaluation, analysing shorter segments of an incoming signal, while non-stationary methods can be used to analyse the instantaneous frequencies of fully recorded signals. All the presented methods aim to produce spectral representations that have high resolution and are easy to interpret. Such representations allow for detection of individual signal components in multi-component signals, as well as separation of close signal components. This makes feature extraction in the spectral representation possible, relevant features include the frequency or instantaneous frequency of components, the number of components in the signal, and the time duration of the components. Two methods that extract some of these features automatically for two types of signals are presented in this thesis. One adapted to signals with two longer duration frequency modulated components that detects the instantaneous frequencies and cross-terms in the Wigner-Ville distribution, the other for signals with an unknown number of short duration oscillations that detects the instantaneous frequencies in a reassigned spectrogram. This thesis also presents two multitaper methods that reduce the influence of noise on the spectral representations. One is designed for stationary signals and the other for non-stationary signals with multiple short duration oscillations. Applications for the methods presented in this thesis include several within medicine, e.g. diagnosis from analysis of heart rate variability, improved ultrasound resolution, and interpretation of brain activity from the electroencephalogram

Detection of Nonstationary Noise and Improved Voice Activity Detection in an Automotive Hands-free Environment

Speech processing in the automotive environment is a challenging problem due to the presence of powerful and unpredictable nonstationary noise. This thesis addresses two detection problems involving both nonstationary noise signals and nonstationary desired signals. Two detectors are developed: one to detect passing vehicle noise in the presence of speech and one to detect speech in the presence of passing vehicle noise. The latter is then measured against a state-of-the-art voice activity detector used in telephony. The process of compiling a library of recordings in the automobile to facilitate this research is also detailed

Detecting compact binary coalescences with seedless clustering

Compact binary coalescences are a promising source of gravitational waves for second-generation interferometric gravitational-wave detectors. Although matched filtering is the optimal search method for well-modeled systems, alternative detection strategies can be used to guard against theoretical errors (e.g., involving new physics and/or assumptions about spin/eccentricity) while providing a measure of redundancy. In previous work, we showed how "seedless clustering" can be used to detect long-lived gravitational-wave transients in both targeted and all-sky searches. In this paper, we apply seedless clustering to the problem of low-mass ($M_\text{total}\leq10M_\odot$) compact binary coalescences for both spinning and eccentric systems. We show that seedless clustering provides a robust and computationally efficient method for detecting low-mass compact binaries