Iet Radar Sonar and Navigation最新文献

With the increasing prevalence of complex electromagnetic threats, the antijamming capability of satellite navigation signals has become a critical factor in signal design. However, existing signal designs still exhibit inherent limitations against varying jamming patterns, restricting their applicability in heavily jammed environments. This paper proposes a navigation signal structure based on wideband frequency hopping (WFH) modulation, aiming to enhance both antijamming capabilities and measurement accuracy. Since frequency hopping induces nonstationary characteristics in the received jamming spectrum, the conventional carrier-to-noise ratio (CNR) becomes inapplicable. To address this, we propose the nonstationary effective carrier-to-noise ratio (NSCNR) model to characterise performance under dynamic spectral conditions. In addition, the impacts of various hopping parameters, specifically ionospheric effects, synchronisation errors and dwell time, on signal accuracy and antijamming performance are thoroughly analysed. Simulation results demonstrate that wideband frequency hopping provides robust resilience against both narrowband and wideband jamming.

High manoeuvring target tracking remains a challenging problem due to complex motion patterns, frequent model switching and strong nonlinear relationships between system states and observations. The interacting multiple model (IMM) algorithm integrated with the unscented Kalman filter (UKF) is widely used for such scenarios. However, its performance is significantly limited by reliance on a fixed or heuristically designed transition probability matrix (TPM), which leads to model switching lag and degradation in tracking accuracy during abrupt manoeuvres. Moreover, existing deep learning-assisted IMM methods often fail to effectively fuse spatiotemporal features and suppress noise. To solve these gaps, an adaptive interacting multiple model unscented Kalman filter (IMM-UKF) algorithm based on a convolutional neural network and long short-term memory network (CNN-LSTM) fusion architecture is proposed. A multi-dimensional feature space including state estimation, observation data and model probability is constructed by the algorithm, which is then used as input to the neural network model. Latent spatial features are extracted by the CNN module and subsequently processed by the LSTM network to capture temporal dynamic characteristics, which achieve real-time dynamic estimation and adaptive optimisation of the model TPM. In addition, a sliding average buffer mechanism is introduced to smooth the prediction outputs and reduce the impact of disturbances on estimation performance. Simulation results show that the proposed algorithm outperforms the IMM-UKF algorithm. In the periodic motion scenario, the proposed algorithm reduces position and velocity root mean square error (RMSE) by 11.2% and 19.96%, respectively. In the compound manoeuvring motion scenario, the proposed algorithm reduces the position and velocity RMSE by 14.87% and 21.50%, respectively. The proposed algorithm effectively improves model switching accuracy, increases the probability of matching the dominant model and significantly enhances tracking performance under high-manoeuvrability conditions.

Time difference of arrival (TDOA) estimation is a critical parameter estimation stage of TDOA localisation in wireless monitoring networks, and its result directly determines the positioning accuracy of the signal source. However, the existing TDOA estimation methods based on orthogonal matching pursuit (OMP) only consider the scenario of circular signal and are prone to the degradation of estimation performance due to atom misselection under small samples and low signal-to-noise ratio (SNR). To address this issue, this paper proposes a loop matching pursuit multistation TDOA estimation algorithm based on noncircular signal. Firstly, by combining the noncircular characteristic of the radiation source signal, an extended data model is constructed by using multistation received data and their conjugate. Then, based on the time-domain sparsity of the time difference, an extended time-domain dictionary set is built by discretising the time grid. In addition, the support set is optimised via atom loop deletion and addition to reconstruct the sparse coefficients. Finally, the TDOA estimation is obtained from the correspondence between the time difference value and the sparse coefficient. Furthermore, the Cramer–Rao bound (CRB) for TDOA estimation of a noncircular source is derived, thereby providing a quantitative theoretical lower bound for the estimation accuracy of the new algorithm. The simulation experiment results verify the superiority of the proposed algorithm under small samples and low SNR.

This paper focuses on the design of synthetic aperture radar (SAR) deceptive jamming waveforms with spectral compatibility. We formulate an optimisation problem to minimise the matching error between the designed and the intercepted signal, under the constraint that the interference of the jammer to the nearby friendly radiators is controlled. Additionally, to enhance the effective radiated power of the jammers, we enforce a peak-to-average power ratio (PAPR) constraint on the waveforms. To deal with the formulated problem, we propose an iterative algorithm based on alternating direction multiplier method (ADMM). Numerical results demonstrate the fast convergence of the ADMM algorithm. Moreover, the jamming waveform designed by the algorithm exhibits high similarity $��\left(��>�0.95��\right)�$ to the waveform transmitted by the hostile radar. In addition, the waveform forms deep notches over the frequency band of the friendly radiator such that the interference is reduced. By using the synthesised jamming waveform, successful deception against the hostile SAR system is demonstrated by generating false targets with high fidelity in both range and azimuth.

To address the challenges of ‘high-density interference’ and ‘multi-mode parameter agility’ in Radar Emitter Identification (REI) under complex electromagnetic environments, as well as the limitations of existing models-pulse sequence models have weak anti-noise capability, whereas statistical feature models are prone to multi-mode parameter confusion-this paper proposes an REI method based on Typical Parameter Sequences (TPS). First, a three-level ‘operational mode-beam dwell-pulse group’ signal model is constructed to clarify the hierarchy of key radar features and lay a foundation for both the rationality of TPS and sliding windows design in TPS extraction. A Pulse Repetition Interval (PRI) probability model under pulse interference and loss is also established, providing a theoretical basis for noise suppression. Second, Hierarchical Bayesian Nonparametrics (HBNP) clustering and hierarchical denoising extract local typical parameters, which are concatenated into global TPS (retains temporal information, anti-noise, data compression) via sliding windows. Finally, Long Short-Term Memory (LSTM) realises emitter identification. Simulation experiments show that: In strong noise environments, the proposed model's accuracy is significantly higher than that of pulse sequence models after accumulating a limited number of beam dwells; in multi-mode switching scenarios, its accuracy is much higher than that of statistical feature models, helping alleviate multi-mode parameter confusion.

With the continuous evolution of mobile communication technology and the booming development of the Internet of Things, positioning has become one of the important functions of 5G. Label positioning in wireless networks often adopts a time difference of arrival (TDOA)-based trilateral positioning scheme, whose accuracy is related to the time synchronisation information between base stations. Therefore, time synchronisation and localisation calculation are two key technologies for 5G wireless network localisation, but they are usually treated as two independent stages. This work proposes a unified framework model that utilises the TDOA measurements of all base stations in 5G wireless positioning networks, while estimating the clock deviation inside the base stations and the position of the tags to be located, in order to save system resources and improve positioning performance. Firstly, a TDOA ranging and positioning model based on clock offset is proposed. For the solution of this model, a joint time synchronisation and localisation algorithm based on alternating optimisation and alternating maximum likelihood is proposed. For the selection of initial iteration values, the maximum volume indexed ellipsoid solution is introduced to significantly improve convergence. The results show that this scheme achieves a positioning accuracy of 2 m and a synchronisation accuracy of 4 ns.

In this work, we propose a method for tracking multiple extended targets or unresolvable group targets in a clutter environment. First, based on the random matrix model (RMM), each target's joint kinematic–extent state is modelled as a gamma Gaussian inverse Wishart (GGIW) distribution. Considering the uncertainty of measurement origin caused by the clutters, we adopt the idea of probabilistic data association and describe the joint association event as an unknown parameter in the joint prior distribution. Then, variational Bayesian inference (VBI) is used to approximate the intractable posterior distribution. To improve practicality, we propose two lightweight schemes to reduce computational complexity. The first is clustering-based and effectively prunes joint association events. The second simplifies the variational posterior by using marginal association probabilities. Finally, we demonstrate effectiveness on simulations and real-data experiments and show that the method outperforms state-of-the-art baselines in accuracy and adaptability.

Distributed aperture radar (DAR) systems encounter significant computational challenges in near-field target detection because of the large synthesised equivalent apertures which drastically increase scan cell dimensionality and processing complexity. To address this, we propose a novel near-field processing method based on ‘Virtual Array-Observer (VAO)’ role reciprocity. Our approach constructs a radar virtual array at the target location, which inversely observes the distributed radar deployment. Crucially, we design the equivalent aperture of this virtual array to satisfy far-field electromagnetic transmission conditions. This enables the derivation of a spatial steering vector model that compensates for and aligns range/azimuth units into regularly arranged array data. By leveraging a fast Fourier transform (FFT)-based rapid computation model for efficient steering vector calculation, we establish a universal computational framework for accelerated target detection. Simulations demonstrate that the proposed method achieves multi-node echo synthesis with energy loss under 8$��\%�$ compared to theoretical values, while simultaneously reducing computational burden substantially. This framework provides an efficient solution for real-time, large-scale DAR implementations, effectively bridging the gap between coherent signal synthesis requirements and computational feasibility in near-field scenarios.

In this paper, a convolutional neural network (CNN) based algorithm for joint estimation of direction of arrival (DOA) and polarisation parameters is introduced. Previous algorithms for the joint estimation of DOA and polarisation parameters frequently exhibit performance degradation under low signal-to-noise ratio conditions and with a limited number of snapshots, which imposes significant constraints on their practical applicability. To address these limitations, this paper proposes a CNN-based algorithm for the joint estimation of DOA and polarisation parameters. This method splits the joint estimation of DOA and polarisation parameters into two steps. In the first step, the upper triangular array of array output covariance matrices is used as data input to CNN to realise DOA estimation from signal sources. The second step establishes the polarisation and spatial domain spectral functions, followed by a spectral peak search to estimate the polarisation parameters of the signal. Simulation results demonstrate that the proposed algorithm achieves a significantly higher estimation accuracy compared to classical estimation methods under the conditions of low signal-to-noise ratios and a limited number of snapshots. The analysis of the simulation results shows that the proposed algorithm leverages a CNN, which fully exploits the spatial characteristics of the array output covariance matrix and consequently reduces the computational complexity.

There is no ultimate intelligence-gathering discipline. Intelligence products almost always require multiple sources for further analytical processing. However, signals intelligence represents a unique gathering discipline since information is derived from both communication and noncommunication electromagnetic emissions; it operates passively and is usually highly reliable. A subdivision of signals intelligence dedicated solely to the noncommunication emission is electronic intelligence. It focuses mainly on emitters such as radars, telemetry systems and radio navigation systems. Over-the-horizon radars are part of systems for long-range detection. This paper presents a comprehensive evaluation of three distinct direction-finding methods implemented within a unified three-channel coherent system for the bearing estimation of over-the-horizon (OTH) radar transmitters. The key contribution lies in the detailed simulation-based assessment of these methods under varying signal-to-noise ratio (SNR) conditions using realistic waveform parameters derived from measured real OTH radar signals. The results quantitatively characterise the operational strengths and limitations of each method, offering a practical foundation for algorithm selection in electronic intelligence applications and enabling future integration of machine learning models to enhance direction-finding performance in a dynamic operational environment.