In this paper, four adaptive radar architectures for target detection in heterogeneous Gaussian environments are devised. The first architecture relies on a cyclic optimization exploiting the Maximum Likelihood Approach in the original data domain, whereas the second detector is a function of transformed data which are normalized with respect to their energy and with the unknown parameters estimated through an Expectation-Maximization-based alternate procedure. The remaining two architectures are obtained by suitably combining the estimation procedures and the detector structures previously devised. Performance analysis, conducted on both simulated and measured data, highlights that the architecture working in the transformed domain guarantees the constant false alarm rate property with respect to the interference power variations and a limited detection loss with respect to the other detectors, whose detection thresholds nevertheless are very sensitive to the interference power.

The importance of high resolution range profiles (HRRPs) for radar applications like tracking or classification is well known. In the scientific literature several approaches have been investigated to obtain HRRPs from wideband radar signals. Recent works show that noise radar waveforms can be exploited in this sense due to their high resolution and low peak to sidelobe ratio (PSLR) properties. However their use can cause some issues in applications where long time integrations are required, e.g., in the presence of a low effective radiated power (ERP) transmitter: recording the reference signal in this case would be difficult due to the big quantity of data. This work proposes a real time digital processing schematic based on linear feedback shift registers (LFSRs) which is very flexible and has a low computational burden: its internal state can be easily controlled and reproduced in reception, where a multichannel correlator is exploited as matched filter. The resulting signal, compared to typical “pulse compression” and noise radar waveforms, shows similar performances but a lower order of complexity in terms of real time generation and reception.