Iet Radar Sonar and Navigation最新文献

Effective extraction of target features has always been a key issue in target recognition technology in the field of signal processing. Traditional deep learning algorithms often require extensive data for pre-training models to ensure the accuracy of feature extraction. Moreover, it is challenging to completely remove noise due to the complexity of the underwater environment. A Time-Delay Autoencoder (TDAE) is employed to extract ship-radiated noise characteristics by leveraging the strong coherent properties of line spectrum. This approach eliminates the need for previous data to adaptively develop a nonlinear model for line spectrum extraction. The test data was processed using three distinct approaches, and plots of recognition accuracy curves at various signal-to-noise ratios were made. On the dataset utilised in the research, experimental results show that the proposed approach achieves over 75% recognition accuracy, even at a signal-to-noise ratio of −15 dB.

Attributed scattering problems have been found to be helpful in inverse synthetic aperture radar (ISAR) imaging and target recognition problems. In this model, the scattering centres are divided into two categories: localised and distributed. Localised scattering centres are those that are concentrated in a small area, while distributed scattering centres are spread out over a larger area. Several methods have been proposed to estimate the scattering centres which aim to accurately identify the location and characteristics of the scattering centres. However, detecting a distributed scattering centre remains a challenging task. A novel technique is proposed based on sparse signals to improve the detection of distributed scattering centres from localised ones. This technique takes advantage of the sparsity of the signals to accurately identify the location of the distributed scattering centres. Experimental results demonstrate the superiority of algorithm in detecting distributed scattering centres. This improved detection capability has significant implications for ISAR imaging and target recognition problems.

Specific emitter identification (SEI) is a technique for identifying emitters based on the principle that the hardware chain is not ideal, causing the emitted signal to contain emitter-specific information. However, the receiver is also non-ideal, which affects recognition accuracy and introduces receiver-specific information that makes SEI difficult to generalise across receiving systems. In this work, a new multi-receiver receiving and processing system (MR-SEI) scheme is proposed to mitigate the influence of receivers based on the analysis of receiver distortion models. After receiving and processing in a specific manner, recognition performance can be enhanced. Therefore, extracted features can be shared among different receivers and platforms, and can even be applied to newly added receivers. The concept of common waveform (CW) is first defined, referring to the received signal without receiver distortions. Different receiving devices are working synchronously, and the CW is estimated using multiple copies of the signal obtained from multiple receivers through the iterative reweighted least squares (IRLS) method. For each receiver, a maximum linear correlation algorithm is proposed to calculate the received signal without being affected by distortions. Experimental results show that the proposed scheme can enhance identification performance. With the increase in the number of receivers, the improvement is more noticeable. Using 10 distorted receivers operating under an SNR of 25 dB, the proposed algorithm can significantly improve the identification performance, achieving over 95% and approaching the ideal scenario of no receiver distortion. Meanwhile, influences caused by receiver distortions can be effectively eliminated, and the database can be shared with new receivers, overperforming other SEI methods that eliminate the receiver.

Radar systems are a topic of great interest, especially due to their extensive range of applications and ability to operate in all weather conditions. Modern radars have high requirements such as its resolution, accuracy and robustness, depending on the application. Noise Radar Technology (NRT) has the upper hand when compared to conventional radar technology in several characteristics. Its robustness to jamming, low Mutual Interference and low probability of intercept are good examples of these advantages. However, its signal processing is more complex than that associated to a conventional radar. Artificial Intelligence (AI)-based signal processing is getting increasing attention from the research community. However, there is yet not much research on these methods for noise radar signal processing. The aim of the authors is to provide general information regarding the research performed on radar systems using AI and draw conclusions about the future of AI in noise radar. The authors introduce the use of AI-based algorithms for NRT and provide results for its use.

Aiming at the problems of slow speed and poor accuracy of traditional millimeter wave sparse imaging, a sparse imaging algorithm based on graph convolution model is proposed from the perspective of sparse signal recovery. The graph signal model is constructed by combining the low-rank and piecewise smoothing(LRPS) regular terms, based on which the proximal operator is replaced by the denoising graph convolution network, and the graph convolution sparse reconstruction network LRPS-GCN is constructed, and the recovered target image is obtained by iterating with the optimal non-linear sparse variation. For the proposed algorithm, simulation experiments are carried out using synthetic datasets under different target densities, iteration times and noise environments, and compared with the traditional graph signal reconstruction algorithm and the deep compressed sensing reconstruction algorithm, and then use the measured data with varying degrees of sparsity to validate. The experimental results show that the reconstructed images by this algorithm have better performance in terms of normalised mean square error, target to background ratio, reconstruction time and memory usage.

The characteristic polarisation states form a second layer feature set by reflecting shape attributes about that target, enabling better identification performance of the resonance signature. These shape factors reflect a structure's curvature extent, dihedral degree between corners, and the axial ratio between principal axes by determining two characteristic angles associated with the null polarisation state and a ratio of the optimum maximum and minimum received powers, respectively. However, the accuracy of the shape factors degrades with a poorly estimated resonance signature caused by noise, missing resonance due to occlusion or ambiguity in late time onset. Thus, the authors aim to reduce the effect of these problems using an ensemble average to filter noise and enhance the signal strength, properly selecting a modal order to ensure modal consistency of the signature and decay sum (DS) to select the late time onset properly to avoid missing resonance within the polarisation matrix. Finally, a paradigm of two jetfighters validated the factors' discriminative potential across an azimuth plane of low depression angle. The results showed that a DS around 0.4 improves the estimated factors over most resonance modes and azimuth directions. At most target aspects, the first-order shape factors consistently predicted a dominant parallel wedge-shaped structure, while the second-order shape factors consistently predicted a trough-shaped structure; finally, the third-order factors revealed wedge-shape attributes at forward look aspects but trough-shaped attributes at backward look aspects.

Virtual aperture extension of small aperture array has attracted wide attention in high-frequency surface wave radar (HFSWR). A biologically inspired coupled (BIC) system is employed to virtually extend the array aperture. However, the existing researches on BIC only consider the array signal processing model and do not combine it with actual radar signal principle. To indeed apply the BIC system to HFSWR, two detailed methods which combine the BIC and HFSWR at signal level are proposed. A three-dimensional signal model of HFSWR considering array processing was established and the entire signal processing was derived. Then, two combination methods, namely fast-time domain (FTD)-BIC and slow-time domain (STD)-BIC are proposed. The former implements the BIC before fast-time processing, while the latter implements the BIC before slow-time processing. The authors demonstrate that they can virtually extend the array aperture without affecting the target detection. Meanwhile, their capabilities in multi-target scenarios are analysed and satisfactory conclusions are obtained. By numerical simulations and experiments, the array aperture and range-Doppler (RD) spectrum of the standard HFSWR and BIC-HFSWR are compared. The results show that while the performance of their RD spectrum is almost the same, BIC-HFSWR has an enlarged virtual aperture than standard HFSWR.

Lloyd's mirror effect is a spatial interference phenomenon that results from the coherent combination of direct and surface-reflected propagation paths. The higher-order vertical sound intensities of the interference sound field contain source depth information, and the relationship between these higher-order sound intensities can be employed to estimate the source depth. A method for source depth estimation and qualitative binary source depth discrimination using the 0th-order sound pressure, as well as the 1st- and 2nd-order sound intensities, was proposed. The numerical simulation results confirmed the ability of the proposed method to approximate the source depth and discriminate between surface and submerged sources without requiring long-term tracking or knowledge of the ocean environment.

The application of the hybrid extended Kalman filter (HEKF), hybrid unscented Kalman filter (HUKF), hybrid particle filter (HPF), and hybrid extended Kalman particle filter (HEKPF) is discussed for hybrid non-linear filter problems, when prediction equations are continuous-time and the update equations are discrete-time, and also the discrete extended Kalman filter (DEKF), discrete unscented Kalman filter (DUKF), discrete particle filter (DPF), and discrete extended Kalman particle filter (DEKPF) for discrete-time non-linear filter problems, when prediction equations and update equations are discrete-time. In order to assess the performance of the filters, the authors consider the non-linear dynamics for a re-entry vehicle. The filters are used in two hybrid and discrete states to estimate the position, velocity, and drag parameter associated with the re-entry vehicle. Theoretical topics concerning estimating the drag parameter of a vehicle in re-entry phase have been dealt with. Drag parameter estimation is carried out using a combination of hybrid filters and discrete filters as an effective estimator and fixed value, forgetting factor, and Robbins-Monro stochastic approximation methods as the noise covariance matrix adjuster of the parameter.