A great interest has been directed towards high frequency surface wave radar (HFSWR), because it is applied as long‐range early warning and real‐time measurements of sea surface condition tools in maritime surveillance. However, the unwanted reflection from the ionosphere (ionosphere clutter) damages the consistent detection performance of HFSWR. Target detection and environmental information extraction from radar echoes are primarily determined by the extent to which the medium contaminates the transmitted signals. Although the ionospheric signal corruption mechanisms are complex and various, a number of eliminating contamination approaches have been developed in the last 50 years. The purpose of this paper is providing an overview of the past and current clutter theoretical models and clutter suppression techniques for HFSWR system. The experimental results involved in the complex long‐term ionosphere observation and ionosphere echo analysis are first discussed. Special attention is paid to the correlation between the signal degradation sources and eliminating clutter techniques. Furthermore, the clutter suppression methods are classified based on the design idea. The specific clutter suppression methods as well as its operation principle are briefly introduced. Finally, several research trends and open issues are presented, with emphasis on the need for proactively controlling the radar design cost and generally agreed‐upon standards.

An overview of the past and current clutter theoretical models and clutter suppression techniques in the HFSWR system is provided. The experimental results involved in the complex long‐term ionosphere observation are discussed. Special attention is paid to the correlation between the signal degradation sources and eliminating clutter techniques.image

Source enumeration with large snapshots is feasible for a static uniform circular sonar array (UCSA). However, the dimension of the signal subspace in the array coordinate system exceeds the number of signal sources due to the rotation of the UCSA, which makes standard source enumeration ineffective. To address this issue, a spatial focusing approach for source enumeration with rotating UCSAs is presented. The approach is based on the concept of phase‐mode excitation and makes use of the UCSA heading information to achieve spatial focusing. The proposed algorithm provides reliable source enumeration with a high signal‐to‐noise ratio (SNR) when compared to the previous signal‐subspace transformation (SST) method. Simulations demonstrate that the proposed algorithm outperforms the other three methods in the conditions of a low SNR and a small angular separation.

A phase mode excitation‐based spatial focusing algorithm to deal with the challenge of source enumeration using rotating uniform circular arrays is proposed. The algorithm is based on the idea of phase‐mode excitation, which allows for source enumeration with large snapshots.image

In received signal strength (RSS) based radio source localisation, RSS measurements can be converted to the squared distance estimates between the emission source and the sensors to construct a system of pseudolinear equations, allowing for the use of the weighted linear least squares (WLLS) estimators for location estimation. The WLLS estimators are widely applied in practice because of their simplicity and computational efficiency. Nevertheless, the major challenge of this approach lies in estimating the squared distance from RSS measurements governed by the log‐normal shadowing effect. A pseudo conditional distribution (PCD) of the squared distance between the emission source and the sensor is introduced first, given the RSS measurement at each sensor. Then, the authors propose a series of new WLLS location estimators, using three typical statistical characteristics, that is, mean, median, and mode, of the PCD. Analysis of their estimation performance are also provided through performance rankings in terms of their mean square errors and covariances. It is found that estimation performance of the PCD‐induced WLLS estimators heavily depends on the choice of the statistical characteristic of the PCD and different choices lead to estimators with better, worse, or equal performance. Numerical examples show that the proposed mode‐WLLS estimator always performs better than the existing WLLS estimators, and also better than the existing convex optimisation based algorithms in most cases but with much less computations.

A series of new weighted linear least squares (WLLS) location estimators are proposed first for RSS‐based radio source localisation, by introducing a pseudo conditional distribution (PCD) of the squared distance between the emission source and the sensor. Then, analysis of their estimation performance are provided through performance rankings in terms of their mean square errors (MSE) and covariances. It is found that estimation performance of the PCD‐induced WLLS estimators heavily depends on the choice of the statistical characteristic of the PCD and different choices lead to estimators with better, worse, or equal performance.image

A well‐liked maneuvering target tracking algorithm is a variable structure multi‐model (VSMM). One of the crucial elements determining the tracking effect is the successful model set adaptation (MSA). The ability to further enhance tracking accuracy for the conventional VSMM method is constrained by the absence of a mechanism to thoroughly utilise observation and tracking data to optimise MSA. We incorporate the Reinforcement learning (RL) approach into the MSA procedure to address this issue and provide a VSMM algorithm based on Monte Carlo (MC) learning. To formulate the challenge of optimising the number of effective models as a RL problem, we first used the prediction error, the number of effective model sets, and tracking accuracy to build the models of the appropriate state space, decision space, and reward. The number of efficient models was optimised using MC learning, and the entire VSMM algorithm was then created. The proposed approach was compared to the simulated experiment's five maneuvering target tracking algorithms. The outcomes demonstrate that the suggested algorithm has a lower computation scale and accurate tracking accuracy.

We incorporate Monte Carlo learning into the LMS‐VSMM algorithm framework and propose an MC‐LMS‐VSMM maneuvering target tracking algorithm. Firstly, the prediction error, number of effective models, and tracking errors were modelled as reward‐related states, actions, and rewards. Secondly, the number of effective models was then optimised via MC learning. Finally, a comprehensive MC‐LMS‐VSMM tracking algorithm is built.image

Interrupted sampling repeater jamming can produce numerous active false targets with high fidelity, which is a significant challenge for coherent pulse radar. This poses a threat to the effectiveness of anti‐jamming technology, such as time‐frequency analysis and frequency agility for conventional constant pulse repetition interval (PRI) radar, which will render the radar inoperable to detect and track the actual target. A random PRI and intra‐pulse frequency agile radar waveform with low interception properties is proposed, which can effectively restrict the acquisition of radar waveform parameters by the jammer, destroy the coherence of jamming signals, and capture the range‐dimensional centroid feature of the echo. Accordingly, a jamming identification method based on the scale constrained Gaussian mixture model and an entity radar target energe recovery algorithm based on the neighbourhood weighted maximum entropy are proposed for target detection. Theoretical analysis and simulation experiments demonstrate the effectiveness of the proposal.

This paper proposes a random pulse repetition interval and intra‐pulse frequency agile radar waveform and captures the range‐dimensional centroid feature of the echo. Accordingly, a jamming identification method based on the scale constrained Gaussian mixture model (SCGMM) and an entity radar target energy recovery algorithm based on the neighbourhood weighted maximum entropy (NWME) are proposed for target detection. The effectiveness and superiority compared with the existing approaches are demonstrated through simulation experiments.image