Small target classification is an important but difficult task in maritime surveillance radar measurements. Due to the small sizes of small targets, no spatial scattering structural information is available for classification. The radar cross section fluctuation and radial velocity variation of small targets form the main information of their classification. In this paper, a small target classification method based on standardized time-radial velocity images and residual network with improved focal loss is proposed to classify four types of small targets. The proposed pre-processing including the coordinate, size and intensity standardization transfers a time-frequency distribution of target returns to a standardized time-radial velocity image conveying target radar cross section fluctuation and movement patterns. The generalizability of the classification method includes that data from different radar measurements can be used to train classifiers and the trained classifiers can also be applied to other maritime radar measurements. Using the standardized images as inputs, classifiers are built by residual network with improved focal loss. Different from the focal loss that puts more attention on difficult samples, the improved focal loss uses a sigmoid-shaped weight function and puts more attention on middle samples. The proposed classifiers are used to classify the four classes of small targets, anchored buoys, floating boats, moving small targets, and low altitude unmanned aerial vehicle from the IPIX, CSIR databases and two datasets using unmanned aerial vehicle as test target in oceanic background. Experimental results show that the classifiers based on the residual network and improved focal loss attain better and more robust performance than the classifiers based on other neural networks.

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.

High-resolution maritime surveillance radars need to detect small targets and moderate/large targets such as ships in short/medium distance regions. At a single range resolution, radar echoes of moderate/large targets severely affect detection of small targets around them. In this paper, a simple digital filtering method is given to realize range resolution conversion by which multiresolution radar echoes data are generated from high range resolution data. As one of the main contributions, the compound-Gaussian model with inverse Gaussian textures (CGIG) is extended to characterize multiresolution sea clutter. Moreover, an across -resolution parameter estimation method is proposed to estimate the parameters of the multiresolution CGIG model based on across-resolution moment relationship. The other contribution is a hierarchical target detection scheme in the multiresolution CGIG clutter model. Simple non-adaptive noncoherent integration detectors are used at lower resolution channels to detect moderate/large targets and adaptive near-optimum coherent detectors in the CGIG distributed sea clutter are adopted at higher resolution channels to detect small targets using the detections from the lower resolution channels as the prior information on environment. Finally, the multiresolution CGIG model and hierarchical target detection scheme are examined by an X-band island-based measured data using an unmanned aerial vehicle (UAV) as test target.

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.

For maritime radars to detect low-velocity small targets, it is always difficult to exclude false alarms from sea spikes in observation time of the order of subsecond. This paper investigates the characteristics of sea spikes in full-polarimetric maritime radars and proposes a sea-spike discrimination method based on polarimetric Doppler offset differences. As the first contribution, an improved sea spike identification method is presented. In the method, the generalized Pareto intensity distribution (GPID) is used to model Bragg scattering background at each polarization and GPID-dependent amplitude threshold, the minimum time width, and range-time connected support are combined to extract sea spikes with range-time supports. As the second contribution, the deep analysis of sea spikes in seventy full-polarimetric IPIX datasets shows that sea spikes synchronously occur at HH, HV, and VV polarizations as a large-scale scattering phenomenon from breaking waves and sea spikes have different Doppler offsets at the three polarizations. Two new indexes of the polarimetric repeatability rate and amplitude peak-mean ratio and the concept of full-polarimetric sea spikes are introduced to analyze polarimetric characteristics of sea spikes. As the third contribution, a method of detection of low-velocity small targets and discrimination of sea spikes based on polarimetric Doppler offset differences is developed for full-polarimetric maritime radars. It realizes effective detection of low-velocity small targets and exclusion of false alarms from sea spikes in a short coherent processing interval of about one tenth of one second.