To characterize the Doppler spectrum center shift and width spread of airborne radar clutter, a novel Doppler distributed clutter model is established in this paper. Based on this model, a series of new parametric methods are also proposed to estimate Doppler parameters in temporal domain and achieved more excellent performance than conventional frequency domain methods. Both simulation and real experimental results are also provided to validate this new clutter model and new approaches.

In this paper we perform an experimental analysis for assessing the Constant False Alarm Rate behavior of four coherent adaptive radar detectors in the presence of experimentally measured clutter data. To this end we exploit several data files containing both land and lake clutter, collected by two radar systems at different polarizations, range resolutions, and frequency bands.

In this paper we design two statistical tests to ascertain whether radar data comply with the hypotheses of multivariate Gaussianity and covariance persymmetry. For the first issue we develop a statistical procedure based on quadratic distributional distances, which exploits the representation of Gaussian vectors in generalized spherical coordinates. Moreover, in order to analyze the persymmetry property of the disturbance covariance matrix, we design a testing procedure based on the generalized likelihood ratio test. We thus apply the new tests to L-Band experimentally measured clutter data, collected by the MIT Lincoln Laboratory Phase One radar, at the Katahdin Hill site.

The radar detection of targets in the presence of sea clutter has historically relied heavily upon the radial velocity of targets with the respect to the radar platform either by estimating the relative target dopplers (such as for STAP processing) or by examining the path targets traverse from scan to scan. However, for targets with little to no radial velocity component, it can become quite difficult to differentiate targets from the surrounding sea clutter. This paper addresses the detection of slow-moving targets in sea clutter and develops an approach for the non-coherent detection of such targets when high range resolution is available.

A new Constant False Alarm Rate (CFAR) detection algorithm that takes into account the statistics of the sample in the test cell for reference sample selection is proposed and analysed in this paper. The new CFAR algorithm, designated as Switching CFAR (S-CFAR), is designed to operate in homogeneous environment and multiple targets with clutter transition situations. Mathematical analysis in an homogeneous environment shows that the S-CFAR processor has very small CFAR loss compared to the conventional cell-averaging CFAR processor. Monte-Carlo simulations in a non-homogeneous environment demonstrate that the S-CFAR processor has improved robustness against interference targets and clutter power transition. The S-CFAR processor is also simple to design and implement since no sample ordering is required.