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Target detection serve as one of the primary objectives in a radar system. From observations, contaminated by receiver thermal noise and interference, the processor needs to determine between target absence or target presence in the current measurements. To enable target detection, the observations are filtered by a series of signal processing algorithms. The algorithms aim to extract information used in subsequent calculations from the observations. In this thesis and the appended papers, we investigate two techniques used for radar signal processing; spectral estimation and space-time adaptive processing.
In this thesis, spectral estimation is considered for signals that can be well represented by a parametric model. The considered problem aims to estimate frequency components and their corresponding amplitudes and damping factors from noisy measurements. In a radar system, the problem of gridless angle-Doppler-range estimation can be formulated in this way. The main contribution of our work includes an investigation of the connection between constraints on rank and matrix structure with the accuracy of the estimates.
Space-time adaptive processing is a technique used to mitigate the influence of interference and receiver thermal noise in airborne radar systems. To obtain a proper mitigation, an accurate estimate of the space-time covariance matrix in the currently investigated cell under test is required. Such an estimate is based on secondary data from adjacent range bins to the cell under test. In this work, we consider airborne bistatic radar systems. Such systems obtains non-stationary secondary data due to geometry-induced range variations in the angle-Doppler domain. Thus, the secondary data will not follow the same distribution as the observed snapshot in the cell under test. In this work, we present a method which estimates the space-time covariance matrix based upon a parametric model of the current radar scenario. The parameters defining the scenario are derived as a maximum likelihood estimate using the available secondary data. If used in a detector, this approach approximately corresponds to a generalized likelihood ratio test, as unknowns are replaced with their maximum likelihood estimates based on secondary data.