A new adaptive procedure for the detection of a random signal in a background of colored interference plus white noise, all having unknown statistics is derived. The procedure is based on the development of linear models for the two hypotheses H₀ and H₁. Under hypothesis H₀ a wanted signal is absent and under H₁ the wanted signal to present. After parameterizing the input signal under the two hypotheses, the log likelihood ratio is easily established in terms of the innovations processes. The test statistic thus obtained has an intuitive interpretation, which conversely provides a basis for the choice of model candidates for the signal detection problem. Different modelling approaches generally result in different receiver performance. Therefore, choosing appropriate type of model for the two pypotheses is a key step in order to enhance detection. A computationally efficient technique has been developed for this purpose. This adaptive detection method is applied to construct a radar detection scheme for detecting a moving target in a surveillance radar environment. The detector is composed of a pair of adaptive whitening filters followed by a log likelihood ratio calculator. The new scheme is tested by using actual radar data, including weather and ground clutter. Comparison is made with the previously described scheme known as innovations-based detection algorithm (IBDA). The results show that the performance of the new scheme is much better than the IBDA. In a related study, the spatial correlation between radar clutter from adjacent rings is exploited to construct two new adaptive clutter suppression schemes. The schemes are tested by real radar data. Experimental results show that these schemes are around 5 dB and 10 dB better than the adaptive lattice prediction-error filter in terms of improvement factor, respectively.

Doctor of Philosophy (PhD)