Iet Radar Sonar and Navigation最新文献

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.

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.

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.

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.

Unmanned aerial vehicles (UAVs) heavily rely on GPS, a system vulnerable to signal interference in complex urban environments. Although radar systems offer a robust alternative due to their ability to effectively penetrate adverse weather and operate in darkness, a key challenge remains: reliably identifying static architectural landmarks from sparse and noisy radar echoes. This paper proposes a novel method for creating spatio-probabilistic models (SPMs) of radar echoes from high-rise urban landmarks, enabling independent, radar-based UAV localisation. We employ kernel density estimation on real radar data, acquired with a custom-designed X-band ENAVI radar, focusing on large arena buildings and slender spires. These SPMs are then used to detect and identify landmarks by calculating the divergence between the probability distributions of the real-time received echoes and the preestimated reference models. Our evaluation, using probabilistic divergence metrics on Wrocław's Centennial Hall and Iglica, shows that this method effectively preserves the statistical properties of the radar data, generating high-fidelity SPMs. This approach significantly improves landmark identification compared to classical correlation methods, paving the way for more robust and resilient UAV navigation systems.

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.

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 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.

This study presents a deep neural network (DNN) model for multi-octave-band direction-finding (MOB-DF) estimation using a broadband DF-array and multi-layer perceptron (MLP). The model leverages randomly placed array elements to generate unique array steering vectors (ASVs) for directions within a cone-shaped field-of-view. By directly linking ASVs and signal frequency to direction via an MLP, it eliminates reliance on the signal covariance matrix, a common component in many 2D neural network-based DF methods. The DNN-based MOB-DF model is structured into sub-bands, each utilising a trained 16 × 1024 MLP. Simulations with 3-, 4-, and 5-element DF models, trained and validated on datasets with signal-to-noise ratios (SNRs) of 10, 20, and 100 dB respectively, reveal several key findings: (1) MLPs trained at 10 dB SNR can achieve better estimation performance across varying SNR levels, where estimation performance is defined as the probability of direction estimation error ≤ 1°. (2) Increasing array elements expands MOB coverage. (3) The 5-element model attains probabilities of 50% and 90% for ≤ 1° estimation errors at approximately −20 and −10 dB SNR respectively within 2–20 GHz. (4) Average prediction time per direction is on the microsecond scale. (5) The model shows resilience to frequency estimation uncertainties.

Conventional Loran-C is mainly used for low-altitude users; however, when the Loran-C signal station or receiving point is at a higher altitude, the ranging error caused by the elevation change cannot be ignored. The traditional groundwave path correction method for high altitude regions idealises the complex groundwave path as a smooth, extensive elliptic line. However, this is a rough and inaccurate correction value $��\left(��\Delta �S��\right)�$ for the groundwave path. In this paper, based on the Huygens–Fresnel principle, we analyse the Loran-C groundwave path, and propose the Groundwave path accumulation (GPA) method, which calculates the complex terrain groundwave transmission paths in segments, to solve the problem of the low accuracy of $��\Delta �S�$ in the traditional method. With the opening of high-altitude Loran-C stations in western China, the algorithm in this paper can improve the accuracy of Loran-C users' packet positioning to a certain extent in central and western China, Central Asia, and South Asia. The article analyses the correction value of the GPA algorithm to the Loran-C ground wave transmission distance between two points with elevation, and the ground wave path correction value is 46.918 m in the elevation difference of 2500.000 m and no elevation distance of 414,306.538 m.