Iet Radar Sonar and Navigation最新文献

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.

This article presents a study on interferometric inverse synthetic aperture radar (ISAR) for three-dimensional imaging and rotational velocity estimation. The study focuses on synchronisation error compensation in a multistatic setup with three ground-based radars and non-orthogonal baselines. The simulation involves CAD models of non-cooperative targets in Keplerian orbits with different orbital parameters, and radar backscattering along the orbit is simulated based on physical optics approximation. The paper also illustrates the signal processing chain for synchronisation error compensation and the generation of interferometric pointclouds for the resident space object models in orbit. The study includes multilabel classification and performance analysis of synchronisation error compensation at varying SNR using Monte Carlo simulations. The interferometric reconstruction and classification accuracy at low SNR conditions is enhanced using multiple receiver or multi channel fusion and the performance of the fusion algorithm is evaluated at varying noise correlation between the fused channels or receivers.

With the intelligent development of electronic countermeasures, the more complex and intense countermeasure environment poses a severe challenge to the anti-jamming ability of radar. Aiming at the scene where radar detects and tracks targets, this paper proposes an optimisation method of a radar intelligent game anti-jamming strategy based on the inference and induction of jamming behaviour with an OODA (observation, orientation, decision and action) loop. Firstly, the strategy selection of the OODA loop decision process of the jammer is optimised so that the jammer can predict radar behaviour. Secondly, the radar is given the ability to perceive the jammer strategy and infer the decision-making ability of the jammer, and the radar behaviour with inductive ability is selected to generate the cover pulse so that the radar can recover the detection in time under the disadvantage situation, and the OODA loop cycle speed is improved under the advantage situation. At the same time, the judgement link of the jammer OODA loop is destroyed and the jammer's decision is induced to meet the radar expectation, and the success probability of the radar game against the intelligent jammer is improved comprehensively.

The use of recurrent waveforms in over-the-horizon radar (OTHR) necessitates techniques for ambiguity resolution and manipulation. This paper provides a number of techniques for manipulating the shape of radar ambiguity functions. A new and simple characterisation of the radar ambiguity function is introduced in terms of twisted convolution. It is shown that the ambiguity function of any waveform can be transformed by any desired area preserving linear transformation of the delay-Doppler plane. Furthermore, given the desired delay-Doppler transformation, the corresponding waveform transformation can be explicitly constructed through the factorisation of $��2�\times �2�$ matrices. Among other applications of this theory, it is shown that the usual OTHR phase and frequency coding techniques used for range-folded spread Doppler clutter mitigation, which induce an approximate Doppler shearing of delay-Doppler plane, can be replaced by chirp modulating the recurrent waveform. These non-recurrent chirped waveforms induce an exact Doppler shearing and lead to simpler and more robust signal processing of the returns.

Passive localisation and tracking of a radio emitter is of significant interest for both civilian and defence applications. Among the existing methods, received signal strength indicator (RSSI)-based localisation is widely used due to its low cost and simplicity. However, most RSSI-based techniques make simplifying assumptions, such as relying on basic path-loss models and presuming the emitter has already been detected, overlooking complex environmental effects like shadowing caused by obstacles. These limitations hinder the practical application of RSSI-based methods. In this paper, we propose an advanced RSSI-based framework for joint detection and tracking (JDT) of a radio emitter, integrating a more accurate propagation model that accounts for both path-loss and shadowing effects. The emitter is modelled as a Bernoulli random finite set (RFS) characterised by its existence probability (EP) and spatial probability density function (SPDF), addressing the challenges of emitter detection uncertainty. A key innovation of this paper is the development of a fully distributed JDT algorithm, which overcomes the computational and communication challenges associated with centralised tracking systems. The proposed algorithm leverages parallel consensus on likelihood and prediction (PCLP), allowing for scalable and efficient operation across sensor networks. Simulation results validate the proposed method's performance in real-time emitter tracking.

To improve the autonomous navigation accuracy of the Mars probe, a navigation method for orbit around mars using an auxiliary satellite and absolute and relative position information of x-ray pulsars/inter-satellite ranging/landmark integrated navigation is proposed in this paper. In this method, the Mars probe and the auxiliary satellite simultaneously observe the same x-ray pulsar, and the difference in pulse arrival time (TDOA) is calculated by comparing their observations. The states of both the spacecraft and the auxiliary satellite are estimated by integrating the prior known position of the auxiliary satellite. To address systematic errors that remain constant over short periods—such as those introduced by the spacecraft's measurement instruments and satellite systems—these constant errors are incorporated into the state model to improve estimation and prediction accuracy. Moreover, to further enhance navigation precision, the approach integrates x-ray pulsar navigation, inter-satellite ranging, and landmark-based navigation thereby improving system robustness. This approach demonstrates a significant reduction in errors, such as pulsar ephemeris inaccuracies and satellite clock drift, compared to traditional pulsar-based navigation methods. Simulation results confirm the effectiveness of the proposed method in enhancing navigation performance.

The paper introduces and discusses the concept and findings of experimental trials focused on the passive multistatic radar localisation of low-Earth orbit (LEO) space objects using terrestrial illuminators and a LOFAR (LOw-Frequency ARray) radio telescope. In the considered solution, commercial terrestrial digital radio transmitters operating in the VHF band served as illuminators of opportunity, whereas the LOFAR radio telescope was employed as a surveillance receiver. The extensive antenna array of the LOFAR radio telescope enables the detection of relatively weak echo signals reflected from space objects moving in LEO. Reference signals were captured using software-defined radio receivers placed near the illuminators of opportunity. By combining bistatic measurement results from three pairs of illuminator-receiver, the position of a space object was estimated in a Cartesian coordinate system. The experimental results validate the feasibility of determining the position of space objects using a passive radar system that employs antenna arrays, such as the one in the LOFAR radio telescope, along with commercial terrestrial transmitters as illuminators of opportunity. The results of the performed simulations confirmed the accuracy of the object position estimation achieved in real-life experiments.

An accurate stochastic model is essential for achieving high-accuracy positioning solutions in the global navigation satellite system (GNSS) precise point positioning (PPP)/ultra-wide band (UWB) tightly coupled (TC) integration. Conventionally, a priori variances are used in the PPP/UWB TC integration to determine the weights of observations. However, a priori variances are difficult to obtain in complex environments since the stochastic characteristics of different observations depend heavily on environmental conditions. By contrast, the variance component estimation (VCE) method can provide a more accurate stochastic model by estimating the measurement uncertainties of different types of observations. Nevertheless, the VCE is susceptible to measurements' outliers and low redundancy in complex observation environments. To address these issues, a robust stochastic modelling approach for PPP/UWB TC integration is proposed by optimising the VCE with a robust estimation strategy and an adaptive moving window filter technique. Two kinematic experiments are conducted in signal-obstructed environments to validate the stochastic modelling approach. Results demonstrate that the three-dimensional (3D) positioning accuracy in the PPP/UWB TC integration is improved by over 47% after VCE optimisation. Compared to the a priori variance-based stochastic model, the robust stochastic modelling approach improves the 3D positioning accuracy by over 27%.