Peng-Jia Zou

Millimetre‐wave road traffic surveillance radars are used for vehicle detection and tracking. Under rainy conditions, rain attenuation and rain backscatter severely degrade the vehicle detection performance of radars. In this paper, based on a big database of rain clutter collected by two radars at different rainfall levels, rain clutter modelling and the assessment of the vehicle detection ability of radars under rainy conditions are investigated. As the first contribution, the clutter‐to‐noise ratio (CNR) and signal‐to‐clutter‐noise ratio (SCNR) of the road traffic surveillance radar under rainy conditions are derived as functions of radial distance, based on the rain attenuation and backscatter models. The derived formulas highly accord with the measured CNR change of radars under rainy conditions, which can be used to evaluate radar performance and optimise the radar operating mode. As the second contribution, a range‐varying compound‐Gaussian model (CGM) with a clutter map cell partition is introduced to model rain clutter with range‐varying statistics, and a best‐type selection method of amplitude distributions is proposed. Based on the analysis of a big database of rain clutter at different rainfall levels, the range‐varying CGMs with gamma and lognormal textures are recommended to model rain clutter of millimetre‐wave road traffic surveillance radars.

Compound-Gaussian models (CGMs) are widely used to characterize sea clutter. Various types of texture distributions have been developed so that the CGMs can cover sea clutter in different conditions. In this paper, the Weibull distributions are used to model textures of sea clutter, and the CGM with Weibull-distributed textures is used to derive the CGWB distributions, a new type of biparametric distribution. Like the classic K-distributions and Compound-Gaussian with lognormal texture (CGLN) distributions, the biparametric CGWB distributions without analytical expressions can be represented by the closed-form improper integral. Further, the properties of the CGWB distributions are investigated, and four moment-based estimators using sample moments, fractional-order sample moments, and generalized sample moments are given to estimate the parameters of the CGWB distributions. Their performance is compared by simulated clutter data. Moreover, measured sea clutter data are used to examine the suitability of the CGWB distributions. The results show that the CGWB distributions can provide the best goodness-of-the-fit for low-resolution sea clutter data as alternatives to the classic K-distributions.

In this letter, an outlier-robust estimation method using the generalized regression neural network (GRNN) and multiple sample percentiles and truncated moments is proposed to estimate the three parameters of the compound-Gaussian clutter model with generalized inverse Gaussian textures (CG-GIG). The tri-parametric CG-GIG distributions include K-distributions, generalized Pareto intensity (GP) distributions, and CGIG distributions as biparametric subclasses and thus possess a strong ability to model various sea clutter data without type selection. It is shown that the ratios of the sample percentiles and truncated moments can uniquely be determined by the CG-GIG distribution, despite its overparameterization. The GRNN is trained to generate an approximate inverse function from the ratio vector to the CG-GIG distribution. By the trained GRNN, the GRNN-based outlier-robust parameter estimators are constructed. Experimental results using simulation data and measured sea clutter data show that the GRNN-based estimators outperform the moment-based estimators and the parametric curve fitting estimation (PCFE) method in performance.

In this paper, a compound-Gaussian model (CGM) with the Nakagami-distributed textures (CGNG) is proposed to model sea clutter at medium/high grazing angles. The corresponding amplitude distributions are referred to as the CGNG distributions. The analysis of measured data shows that the CGNG distributions can provide better goodness-of-the-fit to sea clutter at medium/high grazing angles than the four types of commonly used biparametric distributions. As a new type of amplitude distribution, its parameter estimation is important for modelling sea clutter. The estimators from the method of moments (MoM) and the [zlog(z)] estimator from the method of generalized moments are first given for the CGNG distributions. However, these estimators are sensitive to sporadic outliers of large amplitude in the data. As the second contribution of the paper, outlier-robust tri-percentile estimators of the CGNG distributions are proposed. Moreover, experimental results using simulated and measured sea clutter data are reported to show the suitability of the CGNG amplitude distributions and outlier-robustness of the proposed tri-percentile estimators.

In this article, a spatial-temporally varying generalized Pareto intensity distribution (STV-GPID) model is presented to characterize large-scene sea clutter in high-resolution maritime surveillance radars, and a multiscan recursive Bayesian bipercentile (MSRB-BiP) estimation method is proposed to implement the outlier-robust estimation of parameters in the STV-GPID model. Considering that sea clutter characteristics are affected by sea states and the viewing geometry of a radar, the large scene is segmented into clutter map cells based on an empirical backscattering coefficient model of sea surface to predict radar cross section (RCS) of sea surface per unit physical area. Sea clutter intensities on each clutter map cell are modeled by a GPID. In the parameter estimation, the data of previous scans are transformed into the prior information on the parameters to reduce the storage burden of radar systems. The MSRB-BiP estimator updates the parameters of the STV-GPID model recursively by a mixed sample set with the returns of the present scan and simulated data using the prior information. The mixture ratio adjusts the forgetting rate of data to adapt to temporally varying characteristics of sea clutter. At least, it brings three merits: low storage requirement, outlier robustness, and mitigation of spatial small sample size of a single scan. The convergence and robustness of the estimation method are verified by simulated data. The experimental results on two measured radar datasets verify the effectiveness of the MSRB-BiP estimators, and the errors at the steady state are reduced at least 17.7% and 66.7%, respectively.