Sainan Shi

In this paper, a new-brand feature-based detector via an improved concave hull classifier (FB-ICHC) is proposed to detect marine small targets. The dimension of feature space is suggested to be three, making a compromise between high detection accuracy and low computational cost. The main contributions are in the following two aspects. On the one hand, three features are well-designed from time series and Doppler spectrum, called relative phase zero ratio (RPZR), relative variation coefficient (RCV), and whitened peak height ratio (WPHR). RPZR can measure the pseudo-period properties in phase time series, insensitive to SCRs. In the Doppler spectrum, RCV reflects fluctuation variation in high SCR cases and WPHR describes the intensity property after clutter suppression in low SCR cases. On the other hand, in 3D feature space, an improved concave hull classifier is developed to further shrink the decision region, where a fast two-stage parameter search is designed for low computational cost and accurate control of false alarm rate. Finally, experimental results using open-recognized datasets show that the proposed FB-ICHC detector can improve detection performance by over 20% and reduce runtime by over 49%, compared with existing feature-based detectors with three features.

Currently, recurrence plot (RP) is a good tool to analyze the non-stationary sea clutter time series for small target detection in the existing detectors. To further improve detection performance, a dual-channel detector is proposed to fully exploit information from complex time series. First, amplitude time series and phase time series form two channels and generate two RPs. Second, two well-designed features are respectively extracted from the two RPs to construct a 2D feature vector, called relative Shannon entropy (RSE) and relative truncate diagonal length (RTDL). Third, decision threshold is determined by an improved extreme learning machine (IELM) classifier with false-alarm control, where target-guided prior information is utilized in parameter optimization. Finally, experimental results using IPIX datasets show that the proposed detector can attain good and robust performance, compared with the existing detectors.

Robust and accurate parameter estimation of K-distribution clutter plays an import role in target detection for marine radars. The estimation performance of existing estimators is limited by only two moments or percentiles. To break this limit, a self-learning estimator using gate recurrent unit (GRU) network is proposed to estimate shape parameter by a well-designed feature vector composed of ten moment ratios and thirteen percentile ratios. It is theoretically proved that the feature vector is independent of scale parameter. Then, the scale parameter is determined by a shape-parameter-dependent percentile. Finally, it is verified by simulated data and measured data that the proposed estimator is suitable for the complicated and various clutter environments.

The existing automatic target recognition (ATR) methods for synthetic aperture radar (SAR) images mainly utilize the real-valued magnitude information while often ignoring the phase information. However, the phase information also provides important details, which can be utilized to improve the ATR performance. To address this issue, a fully complex-valued light-weight network called CVLWNet is proposed based on complex-valued operations, such as complex-valued convolution and complex-valued batch normalization. Besides, to achieve reduced parameters and enhanced robustness of the designed network, many complex-valued blocks of operations are built, including the CMish activation function, the complex-valued residual link block (CVReLBlock), the lightweight complex-valued cross stage partial block (LC-CSPBlock). In the designed CVLWNet, the input, output, and weight parameters are all complex-valued, which makes it possible to sufficiently exploit the complex-valued characteristics of SAR data. Comparative experiments are conducted with the moving and stationary target acquisition and recognition (MSTAR) dataset. Compared with the state-of-the-art real-valued and complex-valued models under both standard and extended operating conditions, the performance of proposed method is verified.

This article proposes a multiscan joint noncoherent detection method of small targets in high-resolution sea clutter with spatial–temporally structural textures induced by large-scale swells and waves, which is composed of intrascan noncoherent integration at each spatial resolution cell followed by interscan integration on a radial velocity template. The main contributions of the article are as follows. First, a concept of noncoherent radial velocity spectrum (NCRVS) is proposed to model the nonhomogeneity of multiscan integration of sea clutter with structural textures. In the NCRVS, the noncoherent intrascan integration and the interscan integration by passing the retrospective filter bank are fully combined. Second, the NCRVS of sea clutter has different statistics at individual radial velocity bins and the lognormal distributions are used to fit their statistics and an outlier-robust analytical bipercentile estimator is constructed for parameter estimation. Third, based on the lognormal-distributed NCRVS model, a double-channel multiscan joint detector (DC-MJD) is proposed, where the intrascan noncoherent integration channel is used to find floating or low radial velocity targets and the whitening plus intrascan noncoherent integration channel is used to find target with high radial velocity. Simulated data and measured X-band radar data are used to verify the DC-MJD detector. In comparison with the existing three multiscan detectors, the DC-MJD detector improves the detection probability by 96% in the measured data with a test target.

In this letter, a new feature-based detector using dispersion relative entropy (FD-DRE) is proposed to detect sea-surface small targets, where clutter suppression is well designed in both Doppler and entropy domains. In Doppler domain, whitened Doppler spectrum is presented to mitigate the main sea clutter region, so as to magnify the geometric difference between sea clutter and returns with target. In entropy domain, dispersion relative entropy (DRE) is developed to condense the difference into single feature for detection, where relative operator can further aggravate the fluctuation of sea clutter. Finally, the experimental results on IPIX database show that FD-DRE detector can significantly improve performance relative to the existing single-feature-based detectors and attain competitive performance as the recent tri-feature-based detector.

In this letter, a self-learning method using gradient boosting decision tree (GBDT) is proposed to estimate two parameters of K-distributed sea clutter. Different from the traditional methods using limited two moments or percentiles, a feature vector extracted from four moment ratios and nine percentile ratios are fully exploited by a nonlinear GBDT model, as to automatically estimate shape parameter. It is proved that the feature vector is independent of scale parameter. Then, scale parameter is determined by a shape-parameter-dependent percentile. Finally, both simulated data and measured data are used to confirm that the proposed estimator can attain robust and good performance in complicated and various clutter environments.

This paper aims to develop a feature-based detector using seven existing salient features of radar returns to improve the detection ability of high-resolution maritime ubiquitous radars to sea-surface small targets. Maritime ubiquitous radars form simultaneously dwelling beams at multiple azimuths by digital array receiver and allow long observation time for detection. Due to absence or incompletion of training samples of radar returns with various types of sea-surface small targets, the detection boils down to designing a one-class classifier in the seven-dimensional (7D) feature space mainly by using training samples of sea clutter. A feature compression method, though maximizing interclass Bhattacharyya distance, is proposed to compress the 7D feature vector into one 3D feature vector with the help of simulated radar returns of typical targets. In the compressed 3D feature space, a modified convexhull learning algorithm is given to determine one convex polyhedron decision region of sea clutter at a given false alarm rate. In this way, a feature-compression-based detector is constructed, which can exploit more features of radar returns to improve detection performance. It is verified by the recognized and open IPIX and CSIR radar databases for sea-surface small target detection. The results show that it attains obvious performance improvement.

It is a difficult task for high-resolution maritime radars operating at a fast scan mode to find sea-surface floating and low-velocity small targets due to ubiquitous sea clutter, sporadic sea spikes like target returns, and shortage of shared database. In this paper, a simulation method is presented to generate high-resolution radar returns of a local sea surface with a structural trend in texture and sea spikes by integrating the existing results on large-scale sea surface generation, sea surface reflectivity, Doppler characteristics of sea clutter, and properties of sea spikes. Generally, sea-surface small target detection at a fast scan mode is composed of intrascan integration to suppress sea clutter and interscan integration to exclude false alarms and sea spikes. Based on the Doppler difference between targets and sea clutter at the two time scales of a coherent processing interval (CPI) of tens of milliseconds and a scan period of several seconds, a Doppler-guided retrospective filter (DGRF) detector is proposed, which uses the optimum coherent detection in intrascan integration and a DGRF in interscan binary and test statistic integrations. The two integrations and Doppler consistency of integrated plots are fused for the final decision. Owing to the Doppler guidance, the proposed detector effectively impedes the integration of false alarms of the intrascan processing and provides significant detection performance improvement, which is verified by the simulated data and real radar data with test small targets.