Synthetic Aperture Radar (SAR) images of moving targets are often displaced and defocused, making reliable localisation and recognition difficult. The problem becomes more pronounced when target behaviour departs from simple motion assumptions and is better described by more complex kinematics, such as six-degrees-of-freedom (6-DoF) motion. Although many autofocus methods rely on subaperture or full-aperture formulations, such approaches can become restrictive when motion varies nonlinearly over the aperture and finer compensation is required. Multistatic SAR provides additional receiver diversity and, when combined with frequency and polarimetric diversity, it helps constrain this problem; however, such information is not usually exploited explicitly in pulse-by-pulse autofocus. This paper introduces a multistatic, frequency- and polarimetry-adaptive autofocus algorithm (F-Pol) that extends Localised Threshold Sharpness (LTS) into the joint frequency-polarimetric domain by encouraging agreement across frequency sub-bands and polarisation channels. Experimental validation using the Cranfield University Ground-Based SAR system with emulated target motion shows that, across the tested signal-to-noise ratio (SNR) range of -5 to 20 dB, F-Pol gives improved image quality and target motion estimation relative to Intensity-Squared (ISQ) and LTS. Laboratory and supplementary simulation results indicate that joint frequency and polarimetric diversity enhances multistatic SAR autofocus performance.
Radio detectors are widely used in the aerospace field, but they are subject to numerous electronic interferences, especially information interference, which is commonly seen in increasingly complex electromagnetic environments in modern times. Thus, improving information interference resistance is key to ensuring the accurate detection and damage control for radio detectors in such complex electromagnetic environments. Given that jammers intercept the radio detector signal first and then forward the interference, this study focuses on the design of low-probability-of-intercept (LPI) point beams for the radio detector. We construct a new evaluation model to verify the spatial LPI performance. Frequency diverse array-multiple-input multiple-output (FDA-MIMO) beams are employed and key factors influencing beam convergence are determined. A novel FDA-MIMO LPI spot beam design technique based on the adaptive weight-deep deterministic policy gradient (AW-DDPG) algorithm is proposed. A two-stage adaptive weight multi-dimensional reward function is designed for the AW-DDPG algorithm, with the array element frequency offset as the optimization objective. Leveraging the Actor-Critic framework, the gradient ascent method is used to maximise the reward, thereby deriving the array element frequency offset with better beam convergence performance in the current environment. Meanwhile, the AW-DDPG algorithm applies to LPI point beam design across various radio detector fall angles, overcoming the technical bottleneck that the formula method for the array element frequency offset only applies when the radio detector's fall angle is close to vertical. Simulation shows that with the array element frequency offset optimised by the proposed AW-DDPG algorithm, the FDA-MIMO beam exhibits a half-power beam width of 1 m in the distance dimension and 9.9 degrees in the angular dimension. The beam convergence and LPI performance with the proposed method are significantly better than other classical frequency offset calculation methods. Thus, the proposed algorithm represents a new method for array element frequency offset optimization and LPI point beam design, effectively improving the radio detector's LPI performance.
A distributed sensor fusion architecture is preferred in a real target-tracking scenario as compared to a centralized scheme since it provides many practical advantages in terms of computation load, communication bandwidth, fault-tolerance, and scalability. In multi-sensor target-tracking literature, such systems are better known by the pseudonym - track fusion, since processed tracks are fused instead of raw measurements. A fundamental problem, however, in such systems is the presence of unknown correlations between the tracks, which renders a standard Kalman filter (naive fusion) useless. A widely accepted solution is covariance intersection (CI) which provides near-optimal estimates but at the cost of a conservative covariance. Thus, the estimates are pessimistic, which might result in a delayed error convergence. Also, fusion of Gaussian mixture densities is an active area of research where standard methods of track fusion cannot be used. In this article, harmonic mean density (HMD) based fusion is discussed, which seems to handle both of these issues. We present insights on HMD fusion and prove that the method is a result of minimizing average Pearson divergence. This article also provides an alternative and easy implementation based on an importance-sampling-like method without the requirement of a proposal density. Similarity of HMD with inverse covariance intersection is an interesting find, and has been discussed in detail. Results based on a real-world multi-target multi-sensor scenario show that the proposed approach converges quickly than existing track fusion algorithms while also being consistent, as evident from the normalized estimation-error squared (NEES) plots.
Precise point positioning (PPP) technology has garnered extensive attention for its ability to deliver real-time centimetre-level accuracy services. When providing PPP services via satellite navigation signals, it is necessary to increase the data rate to over 1000 bps. Code shift keying (CSK) technology has emerged as a key candidate for satellite-based PPP signal design. It can increase the data rate without requiring additional frequency resources and compromising ranging performance. However, CSK technology faces several challenges. Firstly, it is vulnerable to multipath interference, which can lead to intersymbol interference. Secondly, the complexity of demodulation increases exponentially with an increase in the number of bits per symbol. In this paper, a novel approach for the design and reception of CSK signals with a phase interval mapping of power-of-two chips is proposed. Analyses demonstrate that this method is capable of significantly reducing the demodulation complexity while mitigating the intersymbol interference. The CSK (6, 2) modulation with a phase interval mapping of 128 chips is employed for the BDS PPP-B2b signal. The signal is demodulated using the FFT with a phase interval output of 128 points. The computation can be reduced by approximately 85%, compared to the continuous phase mapping CSK signal demodulated by partial-output FFT.
Due to the stable propagation of magnetic signals in ocean and air, magnetic detection technology has become an effective means for nonacoustic detection. The magnetic anomaly signal and shaft-rate magnetic signal radiated by underwater vehicles are currently the most effective magnetic detection signals. Existing magnetic detection methods primarily focus on studying either magnetic anomaly signal or shaft-rate magnetic signal. However, since a target can generate both of these magnetic signals simultaneously, detecting one type may lead to the neglect of the other, reducing detection accuracy. To overcome the limitations of existing technologies, this paper presents a combined detection method for magnetic anomaly and shaft-rate magnetic signals. The detection process is divided into magnetic anomaly signal detection based on orthogonal basis function (OBF) and shaft-rate magnetic signal detection based on adaptive line spectrum enhancement (ALE). Especially for the detection of magnetic anomaly signal, this paper proposes a preprocessing method based on the LOESS smoothing technique, utilising noise characteristics, and combines it with the CFAR criterion for decision-making. This approach significantly improves the detection accuracy of the magnetic anomaly signal. Finally, the simulation and experimental results show that combining magnetic anomaly and shaft-rate magnetic signals for combined detection can effectively improve the detection accuracy.
Nano-drones are insect-like drones used to provide intelligence through their capability of intrusion and ability to carry small sensors. They pose a defence and security threat and can potentially violate secure establishments and privacy rights. Their rapid emergence and increased availability have made them an existing technology which is affordable and easy to operate. Nano-drones are typically defined as drones smaller than 15 cm. They are light and stealthy in nature and present a very low radar cross-section (RCS) which creates a significant challenge for active Radio Frequency (RF) security systems tasked with detecting potential threats. This paper presents a K-band Frequency Modulated Continuous Wave (FMCW) radar prototype tailored for detecting nano-drones. Operating at 24 GHz and utilising commercial off-the-shelf components, the radar offers a low-cost, flexible and customisable solution with user-selectable frequency and waveform parameters. The system's detection capabilities were tested using low-RCS oscillating metallic spheres ranging from 0.5 to 3.0 cm in diameter. Nano-drone detection was demonstrated using range-Doppler maps and time-frequency spectrograms of a real and small 5 cm nano-drone. The paper provides a detailed overview of the radar design and test methodology, together with a detailed investigation of the radar performance.
Current radar signal deinterleaving methods primarily utilise direction of arrival, radio frequency and pulse repetition interval to separate signals from different radars. However, their effectiveness becomes limited in scenarios with highly overlapping radar parameters. Amplitude can provide supplementary discrimination for many deinterleaving problems, especially for mechanically scanning radars. The amplitude of signals intercepted from such radars exhibits a continuous parabolic-like variation characteristic. Leveraging this, we construct a function-adapted Gaussian mixture model to characterise the joint distribution of pulse time-of-arrival and amplitude for interleaved pulse trains, thereby transforming radar signal deinterleaving into a parameter estimation and clustering problem. Furthermore, we employ active function cross-entropy clustering (afCEC) to solve the problem and innovatively embeds the sequential andom sampling consensus within the afCEC framework to mitigate its sensitivity to initial values and avoid local optima. This achieves preliminary clustering of the time-amplitude data, effectively decomposing the originally pulse point cloud into multiple subclusters conforming to mixture model components. Building upon this over-segmentation result, we design a merging strategy based on pulse cluster continuity, enabling automatic deinterleaving without prior knowledge of radar quantity. Simulation results demonstrate that the proposed method achieves superior deinterleaving performance in complex electromagnetic scenarios, outperforming state-of-the-art approaches.
In some ballistic target tracking applications, the target travels to the destination with a constant horizontal heading in the reentry phase, whose states are subjected to a destination constraint. If the prior information on the destination can be acquired and effectively utilised, a significant enhancement of performance can be expected. In this paper, a three-dimensional (3D) constrained motion model is established to describe the target motion in the reentry phase. For different cases where the prior destination information is accurately known or contaminated by noise, the horizontal heading angle or the destination position is augmented into the state vector to formulate the accurate constraint relationships in the horizontal plane. Based on the augmented state vectors and the existing 2D model for reentry targets in the vertical plane, accurate state equations are derived to describe the ballistic target motion in the 3D space. Corresponding filtering methods, which employ the unscented Kalman filter to deal with the strong nonlinearity in the augmented state equation, are proposed. Simulation results of Monte Carlo experiments verify the effectiveness of the proposed constrained estimation methods. It is demonstrated that the incorporation of extra destination constraint information leads to superior tracking performance compared with the unconstrained method.
Microwave computational imaging (MCI) combined with programmable metasurface (PMS) has seen significant advancements in recent years. This new microwave imaging technology performs multiplexed measurements by manipulating the radiation pattern of PMS and acquires the spatial resolution. Compared with the traditional real aperture microwave imaging and synthetic aperture microwave imaging, PMS-based MCI (PMS-MCI) not only reduces the cost of the imaging system, but also significantly improves imaging efficiency. As a typical inverse scattering problem, PMS-MCI is nonlinear. To address this nonlinearity, the Born approximation or physical optical (PO) approximation is often used. Additionally, the limited number of independent PMS radiation patterns makes PMS-MCI an ill-posed problem. The ill-posedness of PMS-MCI is mostly overcome through a regularisation scheme which leverages sparse prior information. However, the imaging performance of these existing sparsity-regularised methods can degrade significantly if the sparsity of the probed scene decreases. In some scenarios, one only seeks to reconstruct the shape of a metallic object, which can be parameterised with a binary local shape function (LSF). This binary prior information of LSF can also be exploited to tackle the ill-posed problem. Therefore, a method incorporating such a priori binary information will be introduced into PMS-MCI for recovering the shape of metallic objects in this work. Specifically, a prior model is first constructed to enforce the binary characteristics of the unknowns. Then, Bayesian inference is performed using the variational expectation maximisation (EM) algorithm, integrated with the damped generalised approximation message passing (GAPM) algorithm. Numerical examples are presented to demonstrate the accuracy, efficiency and robustness of the proposed PMS-MCI method.
As an alternative to global navigation satellite systems (GNSS), ground-based positioning systems (GBPS) can achieve high-precision positioning based on carrier phase measurements in GNSS-denied environments. However, due to a large dynamic range of the received signal power in GBPS scenarios, the receiver front-end is susceptible to saturation when processing strong signals from nearby base stations (BS). In such cases, conventional tracking technique may suffer from a significant carrier phase tracking error and degraded ranging performance, which results in a limited carrier phase positioning range and reduced practicality of GBPS. To this extend, this paper proposes an antisaturation carrier tracking technique based on Fourier series (FS) decomposition, which comprises a saturation detector, a multiharmonic predetection filter and a multiharmonic carrier phase discriminator. Compared to the conventional technique, it can extract sufficient carrier phase information from those excessively strong GBPS signals distorted by front-end saturation. Numerical simulation results demonstrate that, except in cases with low saturation levels and minimal carrier Doppler frequencies, a superior carrier tracking performance can always be obtained. Results of a wireless experimental test further validate the effectiveness of the proposed technique. Therefore, more accurate and reliable carrier phase ranging and positioning can be achieved when the receiver is very close to a BS, thereby expanding the usable positioning range of GBPS and enhancing its practicality and robustness.
To address the limitations of existing UAV-mounted LiDAR systems, this paper proposes an innovative real-time transmission and rendering technology for massive point cloud data. The technology utilises an onboard LiDAR placed on the front of the UAV to collect 3D spatial point cloud data, which is transmitted to an onboard Intel NUC for critical processing steps such as data analysis and lossy compression. The compressed data are then transmitted via the UAV's video transmission link to a mobile control app, enabling real-time transmission and rendering of massive point cloud data on Android terminals. This approach effectively overcomes the portability drawbacks of traditional methods that rely on bulky computers, requiring only an Android mobile terminal. A well-designed lossy compression strategy significantly improves data transmission efficiency and reduces computational pressure for point cloud rendering on low-memory mobile devices. Integrated with the SLAM (simultaneous localisation and mapping) algorithm on a flight test platform composed of a DJI M350 RTK UAV, Velodyne VLP16 LiDAR, Intel NUC onboard computer and DJI RC Plus Android controller, the system achieves high-precision 3D point cloud real-time transmission and rendering, enabling real-time Beyond Visual Line of Sight (BVLOS) UAV control. Experimental results demonstrate that this technology can process millions of point cloud data per second on the UAV mobile controller, exhibiting excellent real-time data transmission and rendering performance under various environmental conditions, including low latency and high frame rates, meeting stringent requirements for high precision and real-time responsiveness.
This paper presents a scalable solution for the coordinated control of swarms of UAVs operating in complex three-dimensional environments with no-fly zones and obstacles. The proposed approach is based on a priority-driven behaviour structure implemented using the null-space behavioural (NSB) technique. Each UAV dynamically adapts its behaviour according to a predefined task hierarchy including collision avoidance, obstacle-avoidance, formation maintenance and target achievement. By projecting lower priority control actions into the null space of higher priority tasks, the method ensures conflict-free execution of tasks with respect to the fulfilment of the overall mission. The control architecture has a fully decentralised structure and is designed to maintain performance and scalability as the number of UAVs increases. The results of several experimental tests have demonstrated the effectiveness of the proposed method in maintaining formation and achieving mission objectives in constrained environments.
Global navigation satellite system (GNSS) is widely recognised to be vulnerable to spoofing attacks. A sophisticated form of GNSS spoofing, termed distributed spoofing, transmits each spoofing signal through dedicated antennas. This technique poses significant implementation difficulty in practical scenarios, owing to challenges including diverse propagation paths, inter-node clock synchronisation and transmit-receive isolation. This article proposes a feedback node aided distributed spoofing (FNA-DS) system for executing GNSS time synchronisation attacks, enabling flexible implementation of distributed spoofing. Leveraging observations from a feedback node, the time biases and drifts of each spoofing node are estimated in real time and compensated during spoofing signal generation, ensuring false position, velocity and timing (PVT) solution accuracy and high pseudorange consistency. By dedicating the feedback node exclusively to signal reception and the spoofing nodes solely to signal transmission, the requirement for transmit-receive isolation is relaxed. To comprehensively characterise the distributed spoofing threat, a detailed performance analysis of the FNA-DS system is conducted, quantifying the impact of node position errors and time parameter estimation errors. Field experiments using a self-developed distributed spoofing prototype validate the FNA-DS system's effectiveness and expose limitations in existing direction of arrival (DoA) based anti-spoofing techniques. Collectively, this work expands the capability frontier of GNSS spoofing, advances understanding of distributed spoofing and underscores its significance as a practical GNSS security threat.
Passive reconnaissance solutions receive increased interest as unjammable fibre-optic drones represent a large number of UAVs in recent military conflicts. In order to equip critical infrastructure with an early warning system against drone attacks, it seems obvious to use the local communication infrastructure as illuminator for a passive radar. In Germany and other European countries, a blackout resistant LTE network in the 450 MHz band for critical infrastructure sites is currently rolled outed or already planned. In this contribution, we provide a proof of concept and present experimental results with a LTE450-based single- and multichannel passive radar for drone detection. To ease the signal processing while achieving a clearer ambiguity-function, only the reference elements contained in the OFDM symbols are used. For removing the dominant direct path contribution from the illuminator, an ad hoc approach is used which exploits the space-time structure of the received OFDM reference elements.
Degrees of freedom (DOF) serves as a critical metric for evaluating the design of sparse arrays. Developing novel sparse arrays with enhanced degrees of freedom and mathematically expressible structures constitutes a significant research direction in the field of direction of arrival (DOA) estimation. In this paper, an innovative V-shaped sparse sensor array is proposed through the strategic adjustment of sensor positions within the array which is called V-shaped fourth-order linear array (VFLA). Compared to existing V-shaped sparse arrays, the proposed configuration demonstrates superior degrees of freedom when exploiting the covariance of received signals for DOA estimation. Furthermore, relative to commonly used V-shaped arrays and other sparse arrays, the VFLA exhibits not only higher degrees of freedom but also a larger array aperture, thereby enhancing the accuracy of two-dimensional (2-D) DOA estimation. Finally, simulation experiments validate the outstanding performance of the VFLA in 2-D DOA estimation.
In this work, we propose an alternative distributed tracking approach for extended target with time-varying orientation in a sensor network. Within the random matrix framework, we employ a Gaussian prior for the orientation, the inverse Gamma priors for the diagonal elements of the extent matrix, and a Gamma prior for the measurement rate. Using the Gamma Gaussian Inverse Gamma Gaussian (GGIGG) state model, we derive a centralised tracking approach based on the variational Bayesian technique. Subsequently, we introduce a distributed variational measurement update that leverages convex combination fusion. Closed-form expressions for the unknown variables are derived under a consensus scheme. The resulting algorithm efficiently computes approximate posterior densities for the kinematic state, extent, orientation, and measurement rate in a distributed manner. The effectiveness of the proposed distributed tracking method is validated through numerical experiments, with results showing that the proposed algorithm outperforms existing method based on the multiplicative error model.
This article discusses the problem of tracking a single 3D extended target (or widely separated targets) with convex polytope shape when the target may only be partially visible. An extended target (as opposed to a point target) may generate multiple measurements in a single frame. With the advent of high-resolution sensors (such as LiDAR), the targets need to be considered as extended targets and their shape as well as kinematics need to be estimated. The extended target may only be partially visible (self-occlusion) and the measurements occur only from the visible parts of the target. In this work, different parts of a single extended target are assumed to be different targets constrained by the rigid body motion of the whole target, and the multitarget tracking framework is used to handle the tracking. The target shape is described using a convex hull represented by its vertices and a Delaunay triangulation. The point target PMHT is modified to develop an extended target PMHT (ET-PMHT) joint association and filtering by assuming that the face triangulations are separate targets. Face management is incorporated into the algorithm to delete erroneous faces and the algorithm is able to add new faces to refine the shape estimate. The framework can handle self-occlusion (partial visibility) by associating measurements only to the visible parts of the target. The algorithm's performance is compared with the 3D Gaussian Process under various scenarios, and RMSE of the centre, velocity and IoU metrics are used to quantify the performance. The proposed algorithm is able to outperform the 3D Gaussian Process in the centre RMSE metric by about 40% while achieving an IoU of 0.6 (on average) even when the target is only partially visible.
Geosynchronous synthetic aperture radar (GEO SAR) provides extensive beam coverage and strong continuous observation capabilities, making it a research focus in the remote sensing. Based on the analysis of the GEO SAR illuminated scene characteristics, it is found that the nonplanar and undulating elevation of the ground surface leads to significant azimuth and range spatial variance in the echo signals. To attain precise, geometrically undistorted fast back-projection (BP) SAR images, we analyse the impact of elevation errors on the signal's quadratic phase. Then, a fast back-projection imaging method for GEO SAR geometric distortion calibration based on dynamic selection of local sub-aperture images using a coarse digital elevation model (DEM) is proposed. Firstly, the elevation information of the imaging grid is obtained through coarse DEM interpolation. Secondly, the coordinates of the dynamically selected local sub-aperture images can be approximated by a fully expanded quadratic coordinate polynomial. And the compression function of the two-stage spectrum compression method is modified by utilising this polynomial. Ultimately, the full-aperture SAR image is produced by conducting multi-level fusion operations and mosaic techniques. Additionally, a detailed flowchart is provided. The efficacy of the suggested approach is verified through simulated echo data.
To address the issues of poor detection performance under low signal-to-noise ratios (SNRs) and high computational complexity in existing visibility graph-based spectrum sensing algorithms, this article proposes a novel algorithm based on the Euclidean norm of the horizontal visibility graph (HVG) adjacency matrix. The algorithm begins by computing the block summation of the observed signal's power spectrum. The squared modulus of its autocorrelation function is subsequently calculated, normalised and quantised to form the new sequence, which is then transformed to the HVG and defined as the graph signal. The one-hop graph filter is constructed from the graph signal and the adjacency matrix, and its Euclidean norm serves as the detection statistic. This statistic is compared against a predefined threshold to determine the presence of the primary user signal. To theoretically analyse detection performance, the weak submajorisation order is introduced to evaluate the statistical differences between graph signals under the two hypotheses. Additionally, data exploration demonstrates that the proposed statistic approximately follows a Burr distribution under the null hypothesis, allowing for an approximate analytical expression for the detection threshold is derived. Simulation results show that the proposed algorithm outperforms existing graph-based algorithms at low SNRs while maintaining moderate computational complexity.
In electronic warfare (EW) systems, accurate time-of-arrival (TOA) prediction for radar signals is critical for effective jamming. TOA depends on both pulse repetition interval (PRI) and radar scan patterns, which are increasingly complex due to technological advancements. Unlike prior research focusing solely on one factor, this paper proposes a machine-learning model that leverages both PRI and scan patterns to predict subsequent radar pulse TOA. The system demonstrates superior prediction accuracy and robust performance in noisy environments and under varying probabilities of detection (POD). This is achieved by separating the PRI sequence and the radar scan interval, an approach that can be applied to different system designs. The proposed method applies a filtering algorithm that separates PRI and scan sequences, feeding them into distinct LSTM models, with a splitting technique addressing missing pulses. Importantly, the model integrates the radar antenna main lobe and side lobe information to enhance jamming effectiveness. Simulation results also demonstrate that the main design concept—considering both PRI and scan type—can be used for different techniques, such as a decision tree. This approach significantly improves TOA estimation, handles diverse radar patterns and represents a valuable contribution to radar technology for improved situational awareness and operational efficiency.
