Iet Radar Sonar and Navigation最新文献

Radar work mode recognition is crucial to analyse radar behaviour and intention. There are some challenges limiting the recognition of long sequences with multiple mode classes. First, the performance of recognition method relies on precise segregation of intercepted sequence, which is often unfeasible in reality. Second, the states at the boundaries of adjacent modes may create extraneous mode samples that intervenes the recognition. Third, current methods fail to deal with the scenarios where multiple modes share the same state sequence. To address these problems, a novel forward matching method (FMM) is proposed, comprising a shortest path method (SPM) for intra-mode recognition, a matching strategy, and an adjustment mechanism. SPM is to provide potential recognition for short fragments of the given long sequence. The matching strategy is to assess the availability of current recognition. The adjustment mechanism tunes the segregation and improves the subsequent recognition. FMM offers several distinct advantages. First, the model can explicitly characterise the mode transition probability and is totally interpretable. Second, FMM can distinguish intentional ambiguities, alleviate mosaic ambiguity and probability deviation associated with inter-mode recognition. Third, FMM is extendable to integrate with other intro-mode recognition methods to cater to various scenarios.

Main lobe deceptive jamming always causes the serious degradation of signal detection ability and angle measurement precision of monopulse radar. In recent years, the Blind Source Separation (BSS) method has been adopted to suppress the main lobe jamming. However, the separation results of BSS have the problem of amplitude ambiguity, which will cause the radar using monopulse angle measurement fail to measure the angle parameters after jamming suppression. To tackle this problem, a novel main lobe jamming suppression method is proposed based on BSS and energy detection. Firstly, the models of target echoes and jamming in the sum and difference beam receiving channels are derived. Secondly, the target echoes and interferences are separated by the Joint Approximate Diagonalisation of Eigenmatrices (JADE) algorithm, and then the unperturbed signal segments in the mixed signal are extracted by energy detection, thereby obtaining the precise ratio of the sum and difference channels to complete the angle measurement. Performance of the method was verified by numerical simulation. The results show that the proposed method can achieve interference suppression while accurately estimating the angle parameter of the target.

Recognising suppressive jamming signals is crucial for radar systems to counteract this type of jamming, highlighting the importance of research in this area. Current deep learning-based methods for identifying suppressive jamming signals suffer from reduced effectiveness with limited training samples and issues related to high parameter counts and computational complexity. To address these challenges, the authors propose a jamming recognition method based on an efficient multi-perspective jamming feature perception network. This method extracts features from the time-frequency spectrum of jamming signals from multiple perspectives, including local, multi-scale, cross-space, and global, to obtain more robust and discriminative jamming features and improve recognition under limited training sample conditions. Additionally, the authors design efficient modules for local jamming feature extraction, multi-scale jamming feature down-sampling, and global jamming feature representation. The lightweight design of these modules enables the proposed method to maintain excellent jamming recognition performance while reducing parameters and computational complexity. Simulation experiment outcomes highlight the exceptional effectiveness of the proposed technique across multiple metrics compared to eight other approaches. Furthermore, the proposed method exhibits significantly fewer parameters and lower computational complexity than its deep learning-based counterparts.

To address the challenges of large-aperture sonar systems passive localisation, this paper proposes the application of sparse Bayesian learning (SBL) for passive target localisation in the wideband beam domain. The proposed algorithm aims to overcome the issues of massive computational requirements for two-dimensional SBL scanning and increased localisation errors due to interference energy leakage. The wideband beam domain SBL focusing localisation algorithm is developed by constructing an azimuth-range two-dimensional transformation matrix to preprocess array data, which effectively reduces the computational load of SBL processing while suppressing strong interference energy leakage in passive sonar operating environments, thus improving the range resolution and parameter estimation accuracy of focusing localisation. Simulation and sea trial data analyses demonstrate the feasibility of the proposed algorithm, with results indicating its superior performance compared to existing algorithms.

In this paper, we propose two random matrix based extended target tracking models, which apply to the trajectory-aligned and drifting target motions. The trajectory-aligned model is specifically designed to handle targets moving along the direction of their extent orientations, while the drift model is tailored to targets whose trajectories deviate from their orientations in time. We utilise the well-known variational Bayes method to perform inference and obtain posterior densities via computationally efficient, analytical, iterative steps. Through comprehensive experiments conducted on simulated and real data, our methods have demonstrated superior performance compared to previous approaches in scenarios involving both drifting and trajectory-aligned targets. These results highlight the efficacy of our proposed models in accurately tracking targets and estimating their extent.

In this paper, a broadband multi-carrier linear frequency modulation (LFM) signal reception method with subcarrier frequency offset deramp processing is proposed and investigated. The proposed frequency offset deramp processing is implemented by mixing the multi-carrier LFM radar echo with a multi-carrier LFM reference that has a different subcarrier frequency interval. With this design, the sampling rate of the radar receiver is remarkably reduced and crosstalk-free separation of different subcarrier signals is easily conducted in the frequency domain. To fuse the multiple subcarriers and fill the frequency gaps, a sparse reconstruction method is employed to obtain the broadband response, which is essential for achieving high range resolution detection. The effectiveness of the proposed method is validated through an experiment in which the reception of an 8-carrier LFM signal is conducted and a total bandwidth of 6 GHz after multi-carrier fusion is demonstrated. An inverse synthetic aperture radar imaging experiment is also conducted with the results verifying the good potential of the proposed method in practical applications.

Based on frequency of arrival (FOA) and angle of arrival (AOA) measurements, the localisation of a moving source using a number of stationary stations is discussed. A closed-form solution and bias reduction method using FOA and AOA measurements will be presented to quickly and accurately estimate target parameters, including location and velocity. The closed-form solution is implemented using two-stage weighted least squares, which constructs a pseudolinear equation by introducing auxiliary variables to perform linear estimation. In the process of linearisation, the authors utilise AOA measurements to simplify the FOA pseudolinear equation and reduce the number of auxiliary parameters. This means that fewer stations are needed to estimate the target parameters. However, the use of the computationally attractive pseudolinear formulation will introduce a non-ignorable localisation bias if the measurements are not sufficiently accurate. To solve the above problem, a quadratic constraint on least squares minimisation is considered in the bias reduction method. Under moderate Gaussian noise, theoretical analysis and simulation results show that the root mean square error of proposed method can significantly reduce positioning deviation and asymptotically approach the Cramer–Rao Lower Bound. Keywords Radar, Radar detection, Doppler shift, Parameter estimation.

Airborne warning and control systems (AWACS) serve as critical command and control centres in air combat operations, making them prime targets for strategic attacks. These enemy attacks typically rely on the accurate determination of AWACS combat positions using different direction-finding devices. In particular, passive monopulse angle measurement systems locate AWACS by measuring the angles of signals emitted by AWACS radiation sources, thus rendering them vulnerable to attacks. Given the criticality of the airborne radars of AWACS in battle command and control operations, they must function continuously to monitor air and sea targets. Hence, AWACS cannot effectively evade electronic reconnaissance systems through tactics such as radar shutdown. To explore alternative measures, the authors investigate the deception mechanisms of an integrated frequency diverse array AWACS (FDA‒AWACS) against passive monopulse angle measurements. Using an established FDA signal model, the principles underlying two common monopulse angle measurement methods are first outlined. Subsequently, the angle estimation formulae typically used by these monopulse angle measurement systems to interpret received FDA radiation signals are derived. Additionally, the transmit beampatterns, amplitude patterns, and angle measurement deception capabilities of several typical FDAs are examined. Simulation results indicate that the FDA‒AWACS can theoretically deceive passive monopulse angle measurement systems to a certain extent. However, one-dimensional uniform linear FDAs and other arrays using sinusoidal frequency offsets exhibit limited deception abilities. In contrast, arrays utilising cubic and quartic frequency offset achieve angle measurement errors exceeding 2° in far-field scenarios.

The authors consider the problem of adaptive target detection in the background of interference and heterogeneous disturbance, for a multiple-input multiple-output (MIMO) radar system equipped with widely separated distributed antennas. The signal of interest and the interference lie in two corresponding subspaces that are mutually linearly independent. Therein, both the coordinates of two subspaces are unknown. For MIMO radar system, the disturbances of transmit-receive pairs are heterogeneous, namely, each transmit-receive pair has different statistics. And the disturbance in one transmit-receive pair is non-homogeneous. Therefore a double-hierarchy heterogeneous disturbance is proposed. By resorting to a two-step procedure, a double-hierarchy heterogeneous structural detector (MIMO-GLRTdh) is proposed in accordance with generalised likelihood ratio test for distributed MIMO radar to suppress interference and disturbance. In real scenarios, the certain structure property, existing in the covariance matrix of disturbance, such as the structural persymmetry or Toeplitz property, may be useful. Furthermore, the authors incorporate the structural persymmetry or Toeplitz property to design two structural detectors (PerMIMO-GLRTdh and ToeMIMO-GLRTdh), and study the impact of structure on detectors. Simulation results show that the presented detectors can acquire better detection performance and strong anti-interference ability. In addition, the results indicate that the detection performance of distributed MIMO radar can be improved by the structure in training-limited situations.