Because of the influence of various factors, such as platform motion, antenna error, clutter, and noise interference, the direction finding (DF) of targets using shipborne high-frequency surface wave radar (HFSWR) becomes extremely difficult, which poses challenges for locating vessels at sea. To achieve more accurate DF for shipborne HFSWR, this letter proposes a method based on correction matrices, dividing the factors that cause DF errors into two categories: platform motion and interference from other sources. After calculating two corresponding correction matrices, a two-step correction is performed on the steering vector of the array to reduce DF errors. Field data experiments validate the performance of the correction matrix-based method for DOA estimation.

1 Abstract-Due to the integrated effect of many factors including non-uniform wave motion and shipboard platform motion, the echo signals received by shipborne high-frequency surface wave radar (HFSWR) often suffer from issues such as sea clutter spreading. A large number of targets are submerged by sea clutter, creating a detection blind area. To address this problem, a novel sea clutter suppression method based on multi-domain information synergy is proposed. The proposed method first identifies the broadening region of sea clutter by its characteristics. The multi-domain spectrum is then constructed using a narrow beam forming method. Afterwards, the Laplace kernel function is employed to screen the sea clutter regions to obtain the plausible region of interest (PROI). Ultimately, we integrate all PROIs and obtain sea clutter suppression results. Field data from shipborne HFSWR and validation results from the automatic identification system (AIS) demonstrate that the proposed method can effectively suppress sea clutter, increase the signal-to-clutter ratio (SCR), and achieve better target detection performance.

Shipborne high-frequency surface wave radar (HFSWR) plays a crucial role in ship target detection due to its mobility and flexibility in marine surveillance. However, accurate direction finding (DF) of shipborne HFSWR targets is extremely challenging due to the complex marine environment, which causes the platform to oscillate on six degrees of freedom (6-DOF) in addition to its forward motion, affecting the DF of the target. To solve this problem, we propose a motion compensation direction finding (MC-DF) method for shipborne HFSWR. In this paper, we model the motion of the platform and analyze the impact of the 6-DOF oscillation motion and forward motion on the target azimuth. We derive the steering vector of the radar array after motion compensation and use digital beamforming to accurately conduct DF of the target. The parameters required for motion compensation are provided by the inertial navigation system on the platform. The effectiveness of this method was verified through in-situ experiments on shipborne HFSWR.

High-frequency surface wave radar (HFSWR) plays an important role in vessel target surveillance. However, HFSWR’s inaccuracy of azimuth estimation caused by wide beams severely limits its detection ability. To solve this problem, a novel direction of arrival (DOA) estimation method based on extreme learning machine optimized by particle swarm optimization (PSO-ELM) is proposed to improve azimuth estimation accuracy for HFSWR. This method can obtain the optimal solution without searching the whole angle range of HFSWR. Specifically, PSO optimizes the input weight and hidden layer bias of ELM to obtain optimal parameters for improving the estimation performance. Based on the optimized parameters, the ELM network can give an optimal azimuth estimation in the sense of least squares and minimal norm. The sample sets used for PSO-ELM training are obtained by matching the points detected by HFSWR with the target points reported by an automatic identification system (AIS) on the range–Doppler (RD) spectra. The performance of DOA estimation is verified by field HFSWR data. The experimental results show that the new method has lower root-mean-square error and higher computational efficiency in comparison to the typical DOA estimation methods, such as digital beam forming (DBF) and multiple signal classification (MUSIC). It also uses the machine learning methods, such as back propagation neural network (BPNN) and support vector regression (SVR).

For onshore high-frequency surface-wave radar (HFSWR), target parameter estimation focuses mainly on distance and velocity demodulation, then on azimuth determination. However, these parameters are difficult to solve accurately for shipborne HFSWR targets due to additional modulation on the echo signal introduced by the forward and six-degree-of-freedom (6-DOF) movement of the platform, causing target points spread and shift in radar Doppler spectra. To overcome this difficulty, the influence of platform motion on target detection is mathematically analyzed in terms of echo signal processing, and then theoretical equations are derived to correct the bias in measurements of the target's state and features. Furthermore, to meet the requirement of shipborne HFSWR installed in limited space, the direction of arrival (DOA) estimation of irregular radar arrays with unequal intervals and arbitrary numbers of antennas is also analyzed. With the derived formulas, the parameters of the shipborne HFSWR target, including the range, radial velocity, and azimuth, can be accurately estimated with the help of inertial navigation system (INS) data. Moreover, both the simulation and the field experiment results validate the theoretical analysis and the derived equations.

As an important weak target detection method, Duffing oscillator is very effective in detecting signals with very low signal-to-noise ratio. However, the accurate discrimination between chaotic and periodic states is a crucial problem and that is the prerequisite for using the Duffing oscillator. Conventionally, the Lyapunov exponent is used as an index to identify different states, but as this indicator has the problem of heavy computation cost, slow convergence rate, and requires a mass of data, its application becomes seriously limits. To solve this problem, a novel method for state identification of the Duffing oscillator based on extreme learning machine (ELM) is proposed. The feature data, as the input of ELM, are extracted from the phase diagram and the time series of the Duffing oscillator. Three effective features are extracted in this letter, i.e., ratio of points in and out of the closed region, average distance, and power spectrum. Computer simulations are presented to validate the proposed method and demonstrate that the state classification performance is superior to other related methods with higher computation efficiency, faster convergence rate, and better accuracy.