Iet Radar Sonar and Navigation最新文献

This paper presents a comprehensive review of advancements in road surface classification technology utilising automotive microwave sensors, covering both active radar and passive radiometry, along with data analysis techniques. Accurate knowledge of road surface type and condition is crucial for improving driving safety, especially in the pursuit of fully autonomous driving. The paper begins with a comparative analysis of different sensing technologies, including microwave, optical, LIDAR and sonar sensors. It subsequently highlights the distinct advantages of microwave sensors, particularly in scenarios with low visibility, where other sensing methods are not sufficiently effective. The analysis of road surface classification methods using radar or radiometer data includes both technical aspects (signal parameters, sensor type, position and number of antennas, signal polarisation, etc.) and classification algorithms. These include analysing backscattered or emitted signal parameters based on specific criteria and making decisions based on this analysis or using statistical classification methods (e.g., k-nearest neighbours, support vector machines, neural networks). The paper also discusses the current state of the field and explores future directions and potential advancements in surface classification technology.

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.

Radar signal deinterleaving is challenging due to dense pulse interleaving and diverse PRI modulations. This work reframes it as a multi-class classification problem, treating each emitter as a distinct class. Existing methods suffer from error accumulation in sequential processing or fail to integrate parallel classifier outputs effectively. To address these flaws, we propose OvR-C-MC, a complete one-vs.-rest (OvR) decomposition framework. Key innovations include (1) true multi-class decomposition: parallel binary classifiers maintain the OvR paradigm's theoretical guarantees, avoiding error propagation in sequential binary classifiers. We integrate classifier outputs with a prioritisation mechanism to resolve conflicts, ensuring a more robust and accurate classification process than existing methods. (2) HMC-based OvR classifier: hidden Markov chains (HMCs) form the basis of each binary classifier, enabling support for any regularised PRI modulation types through state transition property and providing a more comprehensive solution. Experimental results demonstrate that the proposed method significantly outperformed existing approaches, particularly in dense interleaved scenarios, whereas maintaining compatibility with diverse PRI modulation types. Thus, the proposed systematic perspective for radar signal deinterleaving provides robust support for radar signal reconnaissance in complex electromagnetic environments.

Large-scale sensor scheduling for multisource localisation is a critical technology in wireless communication control and navigation systems. Most existing heuristic algorithms face challenges in adapting to large-scale sensor systems. To overcome this limitation, we utilise the self-learning capabilities of deep reinforcement learning (DRL) to enable multisource localisation. This paper proposes a large-scale sensor scheduling algorithm based on the multiagent proximal policy optimisation (LSS-MAPPO) framework. We develop a multisource localisation model based on time difference of arrival (TDOA) and design a reward function grounded in the Cramér–Rao lower bound (CRLB). Our approach integrates multihead attention layers into MAPPO to improve the performance of the algorithm. In large-scale sensor scheduling systems, multihead attention mechanisms can effectively handle the high-dimensional state space associated with multisource localisation in multiagent environments. Experimental results under different environments show that LSS-MAPPO improves localisation accuracy compared to the baseline in large-scale sensor scheduling. Notably, it maintains robust performance under partial observability, addressing critical gaps in large-scale dynamic sensor scheduling.

This paper addresses the measurement quality optimisation problem in multiple resolvable group target tracking (MRGTT), proposing an improved MRGTT algorithm based on rigid-body similarity model and optimal measurement fusion. Firstly, a unified framework for group target motion and measurement description is established by introducing the rigid-body similarity model. Secondly, an optimal measurement fusion scheme derived from the minimum variance criterion is proposed, which achieves 2.5–2.8 times faster convergence speed compared to traditional equal-weight methods. Furthermore, a complete algorithm flowchart integrating group structure construction, measurement optimisation and intensity update is designed. The proposed method demonstrated exceptional adaptability across different random finite set (RFS) filtering frameworks, including Gaussian mixture probability hypothesis density (GM-PHD) and Poisson multi-Bernoulli mixture (PMBM). Simulation results show that the proposed method achieves significant improvements in OSPA distance over traditional algorithms, with 45% improvement in the GM-PHD implementation and robust performance across diverse scenario complexities in the PMBM framework, including large-scale manoeuvring scenarios. This framework-agnostic approach provides a versatile solution for resolvable group target tracking in complex scenarios such as group splitting, merging and high-clutter environments.

The utilisation of staggered pulse repetition intervals (PRIs) in pulse-Doppler (PD) radar systems is instrumental in expanding the unambiguous Doppler interval, which is traditionally confined by the constraints of uniform PRIs, and in enhancing the electronic countermeasures capabilities of the radar. However, the presence of high Doppler sidelobes emerges as a principal impediment to the practical deployment of such systems. In this paper, we introduce a novel approach to accurately estimate the Doppler frequencies of targets in PRI-staggered radar systems, which integrates gradient descent and orthogonal matching pursuit algorithms to effectively reconstruct the Doppler profiles of targets with arbitrary velocities. The simulation results demonstrate the method’s effectiveness and robustness, indicating its promising suitability for practical application within PD radar systems.

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.