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.

In radar array signal processing, weak nonstationary polarization signal direction of arrival (DOA) estimation is a challenging and significant issue when both strong and weak nonstationary signals coexist. Time-frequency (T-F) analysis is an effective method to deal with nonstationary signals. In the last decade, spatial time-frequency distributions (STFDs) have been proposed for multiple dual-polarization antenna arrays and efficaciously used for nonstationary signal DOA estimation. In this article, we introduced a novelty means for estimating weak nonstationary polarization signal DOA by utilizing spatial polarimetric time-frequency distributions (SPTFDs) of cross terms when there are strong nonstationary polarization interference signals and additive Gaussian white noise. The cross terms’ SPTFDs are considered via a replaceability matrix for data covariance in multiple signal classification (MUSIC) and the estimation of signal parameters using the rotational invariance technique (ESPRIT). Combining the STFDs of cross terms with polarization information about weak nonstationary signals improves signal DOA estimation accuracy. The combined MUSIC and ESPRIT are used in the algorithm to further ensure the success probability and accuracy of DOA estimation. Through simulation analyses, the proposed algorithm is more suitable for the required application scenarios than other algorithms and is superior to other algorithms.

High-Frequency Surface Wave Radar (HFSWR) faces severe challenges from complex interference in maritime monitoring, hindering weak target detection and false alarm control. To address the model mismatch inherent in traditional Constant False Alarm Rate (CFAR) detectors and the limited perception and lack of sensitivity adjustability in existing encoder-decoder deep neural network methods, this paper proposes an RD Spectrum-Aware Network with Controllable Detection Sensitivity (RDSAN-CDS). The network adopts an end-to-end encoder-decoder architecture, integrating Transformer modules at the deep feature extraction stage to capture the global long-range dependencies between targets and the environment within the Range- Doppler (RD) spectrum. Furthermore, a gating mechanism driven by a preset false alarm probability is designed as an auxiliary input channel. This mechanism dynamically adjusts multi-scale feature responses during decoding, endowing the network with flexible detection sensitivity control during inference. Experimental results on field-measured radar data demonstrate that RDSAN-CDS outperforms existing mainstream methods such as Residual Regression Network (RRN) and DeepLabv3 in complex clutter backgrounds, achieving an effective balance and precise control of the detection rate and false alarm rate under diverse mission requirements.

The range-Doppler-only high frequency surface wave radar utilizes multiple range-Doppler measurements to achieve target detection, significantly enhancing the flexibility and angle estimation accuracy of the radar system. When the receiver is mounted on amobile platform, the flexibility of the radar system can be further improved; however, the motion of the receiving platform induces an additional Doppler shift, necessitating reconsideration of the target detection method. This paper proposes a target bearing estimation method for range-Doppler-only HFSWR under linear motion conditions. First, the initial target bearing estimation is derived through cross localization. Subsequently, a GST-based time frequency analysis technique is employed to separate the composite received signals, enabling the extraction of interested target signals in the time domain. Finally, a virtual aperture technology is utilized based on the platform motion, thereby facilitating the refined target receiving angle estimation through digital beamforming (DBF) processing. Simulation results validate the effectiveness of the proposed method.

A spatially polarized time‐frequency distribution (SPTFD) based on dual‐polarized double‐fed antenna arrays is adapted to deal with polarization‐unstable signals. A linear time‐frequency (TF) representation was used for an instantaneous frequency (IF) estimate, primarily due to its simplicity and immunity to cross‐interference. Using a set of linear TF transformations using Gaussian windows and Fourier oscillation kernels, the IF estimated window widths of multiple unstable signals are obtained. This paper introduces a new method for estimating the direction of arrival (DOA) of polarized waves using adaptive linear time‐frequency transforms. In this paper, a narrowband far‐field point source on the receiving array is analyzed. The source signal is split into two orthogonally polarized components. The optimal window is determined by the first derivative of the IF; for this purpose, we take a simple algorithm for solving the derivative and optimize it. In developing TF‐adaptive and fully automatic TF display technology, the first method is to use the time‐adaptive window for minimizing the IF estimate mean square error (MSE) sum at each moment, while the second procedure is to adjust according to time and frequency and minimize estimate MSE sum at each position in the TF region. Due to its combination with signal polarization, the spatial time‐frequency distribution (STFD) gains more freedom and thus perfects the phonon space estimation of noise and signal. On the SPTFD platform, polarized time‐frequency multiple signal classification (PTF‐MUSIC) is used for the estimation of signal direction of arrival, which outperforms conventional time‐frequency MUSIC. Using the example of a synthesized signal, this method outperforms conventional techniques in DOA estimation.