Iet Radar Sonar and Navigation最新文献

Low-correlation sidelobes are critical for spectrally compatible waveforms in multiple-input multiple-output (MIMO) radar systems. This study presents a novel algorithm for designing spectrally compatible waveforms for MIMO radar with low auto- and cross-correlation sidelobes to enhance weak target detection capability. We adopt the minimum auto- and cross-correlation-weighted integrated sidelobe level (ACWISL) as the objective function. Under spectral and constant modulus constraints, we formulate a nondeterministic polynomial time (NP)-hard problem. To solve this problem, we combine the block successive upper-bound minimisation (BSUM) and majorisation-minimisation (MM) algorithms to develop the BSUM-MM algorithm. The original problem is decomposed into several independent subproblems, which are iteratively solved using the MM algorithm. We also employ the fast Fourier transform (FFT) to significantly accelerate the calculation. Simulation results demonstrate that the proposed algorithm is superior in terms of computational efficiency and sidelobe performance.

The existing radar emitter modulation recognition methods typically assume that the data distribution across different types is balanced. But in reality, the number of signals of various kinds often follows a long-tail distribution, leading to model overfitting for the head classes and underfitting for the tail classes. As a result, the overall recognition performance of models under such data imbalances is suboptimal. A long-tail distribution automatic modulation recognition method based on decoupled training is proposed to address this issue. Based on the ResNeXt network, the proposed method decouples the model training process into two stages: a feature extraction phase under the imbalanced dataset and the classifier learning stage under a balanced dataset. The classifier boundary is fine-tuned by $��\tau �$-normalization method. Compared to existing radar emitter modulation recognition frameworks, the proposed method achieves an overall recognition accuracy of 86.8% when the data imbalance factor is 0.01, surpassing the baseline model by 5%, and improves the performance of radar emitters modulation recognition in the real environment.

For radar target recognition in high-resolution range profiles (HRRP) under low signal-to-noise ratio (SNR) conditions, traditional methods typically involve denoising followed by recognition. However, these methods struggle with complex noise. To enhance HRRP information extraction, this paper proposes an integrated approach combining noise reduction and recognition. First, the short-time Fourier transform (STFT) is improved with a complex Gaussian window to enhance time-frequency resolution. Then, multi-scale analysis is applied by introducing scale values to better capture detailed target features. Differential operations are used to highlight scattering points and edges, improving recognition accuracy. A convolutional neural network (CNN) is employed to extract multi-level features for target recognition. Experimental results on a simulated HRRP dataset from the U.S. Air Force Research Laboratory (AFRL) demonstrate the proposed method's superior performance. It outperforms traditional methods in both accuracy and robustness, offering stronger noise resistance and better utilisation of HRRP's rich features, providing an effective solution for radar target recognition tasks.

A joint power and waveform resource allocation algorithm is proposed for netted radar integrated search and tracking tasks with low probability of intercept. For the search and tracking performance, the detection probability and the posterior Cramér-Rao lower bound of the target are adopted separately. The optimization problem of joint resource allocation is solved by controlling the radar node selection, power allocation, waveform selection, and pulse duration, to minimise the total power of the netted radar while meeting the search and tracking performance for a given target. The intelligent optimization methods are used to solve the problem, and the effectiveness of the proposed method is verified by simulation.

Ionospheric scintillations, arising from variations in phase/amplitude of radio signals traversing the ionosphere, pose significant challenges to Global Navigation Satellite System (GNSS) positioning, particularly in low-latitude regions. This paper proposes a Rao-Blackwellized Particle Filter (RBPF) integrated with a Markov chain model to comprehensively characterise and mitigate the impact of ionospheric scintillation on GNSS positioning. Unlike traditional methods, the Markov-RBPF framework offers enhanced versatility in assessing scintillation dynamics both spatially and temporally, allowing for precise modelling of scintillation evolution over varying nighttime hours and months of the year. Through simulations, the authors demonstrate the superior performance of the proposed Markov-RBPF compared to conventional Extended Kalman Filters (EKF), with position root-mean-square errors below 2 m in a scenario of strong scintillation events in October 2014. This showcases its robustness and versatility in improving GNSS positioning accuracy amidst challenging ionospheric conditions.

There is significant interest in multistatic SAR image formation, due to the increased development of satellite constellations and UAV swarms for remote sensing applications. The exploitation of the finer resolution and wider coverage of these geometries has been shown to reduce the often-impractical data collection requirements of 3D SAR imagery; this offers advantages such as improved target identification and the removal of layover artefacts. This paper presents a novel polarimetric generalisation of the SSARVI algorithm, which was previously developed to exploit sparse aperture multistatic collections for 3D SAR image formation. The new algorithm presented here, named the PolSSARVI algorithm, combines polarimetrically weighted interferograms for determining the 3D scatterer locations from sparse aperture polarimetric collections. The bistatic generalised Huynen fork polarimetric parameters are then determined for the multistatic PolSSARVI 3D SAR renderings. This new approach was tested on both simulated and experimental data. Experimental imagery was formed using measurements from the Cranfield GBSAR laboratory.

A multifunction radar (MFR) can operate in multiple modes and perform various tasks such as surveillance, detection, fire control, search and tracking. Recognising an MFR's operating mode is critical in electronic warfare and intelligence reconnaissance, aiding practical threat assessment and countermeasure tasks. However, current recognition methods face challenges such as overlapping parameters among working modes and suboptimal recognition accuracy under conditions with parameter errors, missing pulses and false pulses. Spurred by these concerns, this paper proposes an entropy-enhanced spatial-deformable hybrid multiscale group network (E-SDHGN) to recognise the operating mode of an MFR and address these challenges. E-SDHGN employs multidimensional entropy computations to construct robust features and integrates deformable convolution and positional encoding to enhance the model's ability to capture complex features. Additionally, it enhances feature extraction and fusion within the dynamic shared residual network (DSRN) module by integrating KAN modules and hybrid weight-sharing strategies. Additionally, an adaptive margin feature module based on attention mechanisms improves classification accuracy in overlapping parameter conditions. Experimental results demonstrate that E-SDHGN achieves superior recognition accuracy and robustness, even under challenging parameter errors, missing pulses and false pulses. This underscores its value for applications in complex electromagnetic environments.

For the effective deployment of countermeasures against drones, information on their intent is crucial. There are several indicators for a drone's intent, for example, its size, payload and behaviour. In this paper, a method is proposed to estimate two or more of the following four indicators: a drone's wing type, its number of rotors, the presence of a payload and its mean rotor rotation rate. Specifically, three multitask learning (MTL) approaches are analysed for the simultaneous estimation of several of these indicators based on radar micro-Doppler spectrograms. MTL refers to training neural networks simultaneously for multiple related tasks. The assumption is that if tasks share features between them, an MTL model is easier to train and has improved generalisation capabilities as compared to separately trained single-task neural networks. The proposed MTL approaches are validated with experimental data and in a variety of combined classification and regression tasks. The results show that MTL approaches can provide improvement in several tasks compared with conventional approaches.

A commonly invoked concept in radar and communications theory is that of a hypothetical three-dimensional (3D) omnidirectional isotropic transmission antenna for which the output radiative power depends only on the spherical radial distance from the subject antenna to a given observation point and is independent of the spherical angular coordinates. In the present investigation, a similar transmitter-receiver antenna system is developed for which the collected power of a linearly polarised receiver antenna depends only on the spherical radial distance from a specially designed transmission antenna to this receiver antenna and is independent of the spherical angular coordinates. This system design capitalises on the radiative properties of a particular spherical transmission antenna that is characterised by azimuthal rotation of the radiative fields and power pattern. This property of 3D isotropic power reception applies exactly in the near field, far field and all intermediate ranges from the spherical transmitter to the linearly polarised receiver. Likewise, this 3D isotropic receive power property is applicable for all radio frequency (RF) wavelengths, both larger and smaller than the radius of the spherical transmission antenna. This proposed antenna system concept could offer utility in multiple applications, including communications beaconing and radar surveillance.