For multiple-input multiple-output radar employing the advantage of virtual array, the general assumption of the perfect orthogonality between multiple waveforms is proved to be unachievable. Therefore, to achieve good orthogonality, transmission schemes employing frequency diversity or chirp modulation diversity have been extensively investigated. Inspired by the idea of “diversity means superiority,” approach which integrates multiple diversities in a single waveform might be used to improve the orthogonality. In this paper, a novel waveform named discrete frequency and chirp-rate coding waveform (DFCCW) consisting of both frequency and chirp modulation diversities is proposed. Based on the auto- and cross-ambiguity functions of the DFCCW, we prove that the proposed approach is able to decrease the cross ambiguity peak values. To generate and optimize a set of Pareto-optimal DFCCWs, a many-objective optimization algorithm named nondominated sorting genetic algorithm with differential evolution is presented. Besides, the optimal criteria considering Doppler effects ensure that the DFCCW possess good performance in presence of moving targets. Numerical results show that the DFCCW achieves better orthogonality than the waveforms that utilize only single diversity type.

This paper proposes a method for the intelligent detection of high-frequency surface wave radar (HFSWR) targets. This method cascades the adaptive constant false alarm (CFAR) detector variability index (VI) with the convolutional neural network (CNN) to form a cascade detector (VI)CFAR-CNN. First, the (VI)CFAR algorithm is used for the first-level detection of the range–Doppler (RD) spectrum; based on this result, the two-dimensional window slice data are extracted using the window with the position of the target on the RD spectrum as the center, and input into the CNN model to carry out further target and clutter identification. When the detection rate of the detector reaches a certain level and cannot be further improved due to the convergence of the CNN model, this paper uses a dual-detection maps fusion method to compensate for the loss of detection performance. First, the optimized parameters are used to perform the weighted fusion of the dual-detection maps, and then, the connected components in the fused detection map are further processed to achieve an independent (VI)CFAR to compensate for the (VI)CFAR-CNN detection results. Due to the difficulty in obtaining HFSWR data that include comprehensive and accurate target truth values, this paper adopts a method of embedding targets into the measured background to construct the RD spectrum dataset for HFSWR. At the same time, the proposed method is compared with various other methods to demonstrate its superiority. Additionally, a small amount of automatic identification system (AIS) and radar correlation data are used to verify the effectiveness and feasibility of this method on completely measured HFSWR data.

Aiming at solving the problem that the unambiguous Doppler range of slow-time MIMO is narrower than that of conventional MIMO signals, a new signal processing method based on tensor decomposition is proposed. In the proposed method, a novel tensor model is designed to describe the echo signals of slow-time MIMO radar and retains more useful information which is lost in conventional methods based on Doppler spectrum separation. The proposed algorithm applies PARAFAC to separate target parameters into three matrices, then minimum mean squared error estimation and least squares method are used to estimate the target parameters respectively. Simulation experiments have demonstrated that the proposed method can exploit unambiguous Doppler range, and simultaneously estimate target’s direction of departure, direction of arrival and Doppler frequency without further parameter pairing.

This paper presents a novel method for clutter suppression and target detection on the Range-Doppler (RD) spectrum of High Frequency Surface Wave Radar (HFSWR). The approach replaces the traditional classification layer of the residual neural network with a regression layer, forming the Residual Regression Network (RRN). Subsequently, a target-embedded dataset is constructed using real measurement data and supplemented simulation data to train the network in learning the characteristics of targets, clutter, and noise interference in the RD spectrum of HFSWR, thus generating a spectrum containing only target information. Introducing an exponential weight control factor, the network output is exponentially weighted and Hadamard multiplied with the original input’s spectrum to obtain the final spectrum for detection. This final spectrum is then fed into the Constant False Alarm Rate (CFAR) detector for further analysis. Through the verification of multiple data sets of target embedded data and measured data and multiple experimental groups, this method demonstrates superior clutter suppression and target detection performance compared to existing methods. Operators can adjust the suppression level by modifying the exponential weight control factor and optimize detection performance by tuning the control factor and CFAR detector parameters according to specific operational requirements.

High-Frequency Surface Wave Radar (HFSWR) is a crucial tool for oceanic remote sensing and surveillance; however, radar target detection is challenged by the presence of background clutter and interference. In response, this paper designs a novel dual-scale branch fusion network specifically for detecting target signals in the range-Doppler (RD) spectrum. The network effectively enhances the ability to distinguish between targets and clutter by combining large-scale environmental feature sensing with small-scale target signal structure analysis. Additionally, we propose a novel detection threshold adjustment mechanism based on the RD spectrum perception network. First, an initial detection threshold is calculated using the traditional Constant False Alarm Rate (CFAR) method. Then, the output of the softmax layer in the RD spectrum perception network is used to adjust the threshold, improving the robustness and accuracy of the detection process. The RD spectrum perception network is trained jointly using data from the Automatic Identification System (AIS) associated with HFSWR and simulated target-embedded data. Multiple validations and analyses of the proposed detection method are conducted with these datasets. Experimental results demonstrate that the proposed method has good detection performance, outperforming several other existing methods.

Ship detection has been an area of focus for high-frequency surface wave radar (HFSWR). The detection and identification of ship formation have proven significant in early warning, while studies on the formation identification are limited due to the complex background and low resolution of HFSWR. In this paper, we first establish a spatial distribution model of ship formation in HFSWR. Then, we propose a cascade identification algorithm of ship formation in the clutter edge. The proposed algorithm includes a preprocessing stage and a two-stage formation identification stage. The Faster R-CNN is introduced in the preprocessing stage to locate the clutter regions. In the first stage, we propose an extremum detector based on connected regions to extract suspicious regions. The suspicious regions contain ship formations, single-ship targets, and false targets. In the second stage, we design a network connected by a convolutional neural network (CNN) and an extreme learning machine (ELM) to identify two densely distributed ship formations from inhomogeneous clutter and single-ship targets. The experimental results based on the factual HFSWR background demonstrate that the proposed cascade identification algorithm is superior to the extremum detector combined with the classical CNN algorithm for ship formation identification. Meanwhile, the proposed algorithm performs well in weak formation and deformed formation identification.