Search Results (234)

Accurate underwater acoustic localization is a key enabling capability for autonomous underwater vehicles and underwater drones, which cannot rely on satellite positioning while submerged and therefore depend on acoustic methods to determine their position. Localization based on time-of-arrival (TOA) measurements requires precise time synchronization between the source and the receivers, which is difficult to maintain in practical deployments. The time-difference-of-arrival (TDOA) representation removes this requirement but discards part of the absolute timing information, reducing localization accuracy. This study investigates a physics-based feedforward multilayer perceptron (FF-MLP) framework for two-dimensional range–depth underwater localization that learns directly from the arrival-time structure induced by sound-speed variability and multipath, with the receiver-array geometry treated as a central design variable for improving synchronization-free TDOA localization. Using multi-receiver arrival times generated with the BELLHOP beam-tracing model under a representative Mediterranean underwater environment, synchronous TOA, biased TOA, and TDOA measurement representations are compared on a common footing, and the effects of the receiver depth distribution, the number of receivers, and the reference-receiver position are systematically examined through Monte Carlo evaluation. The results show that the receiver-array geometry, rather than the measurement representation alone, is decisive for TDOA-based localization: with an appropriately designed geometry, synchronization-free TDOA localization achieves a median two-dimensional RMSE of 11.28 m, approaching the accuracy attainable with synchronous TOA, which requires precise time synchronization. These findings indicate that careful receiver-geometry design can make synchronization-free TDOA a practical alternative to synchronous TOA for the acoustic localization of autonomous underwater vehicles. Full article

Iterative position estimation algorithms based on Time Difference of Arrival (TDOA) are widely used in passive localization systems, including underwater acoustic networks and wireless sensor networks. A critical but often overlooked factor in their practical deployment is the selection of the initial estimate, which directly determines whether the iterative algorithm converges to the correct solution. This paper presents a case-specific approach to initial estimate selection in passive TDOA-based iterative position estimation algorithms. The study evaluates two proposed methods against a common baseline approach, where the initial guess is placed at the center of the sensor formation. Simulations were conducted in Python for both 2D and 3D scenarios, with sensors arranged in two different geometric configurations. A grid-based analysis over a 2 × 2 km area was used to assess performance under both noise-free and noisy TDOA conditions, with Gaussian-distributed error introduced at varying standard deviations. The results demonstrate that in regions where convergence is sensitive to initialization, the proposed Method 1 significantly improves reliability, especially for asymmetric sensor configurations. These findings highlight the importance of initial estimate selection to enhance position estimation accuracy and robustness, particularly in passive systems with limited prior information. Full article

Urban low-altitude unmanned aerial vehicle (UAV) logistics networks face critical operational bottlenecks due to complex three-dimensional spatial blockages, continuous communication diffraction, and severe vulnerability to information-layer threats such as Automatic Dependent Surveillance—Broadcast (ADS-B) signal anomalies. To address these interconnected challenges, this paper proposes an event-driven, cloud–edge collaborative digital twin framework to guarantee continuous multi-link communication and flight safety. The architecture operates through a dual-tier “Teacher–Student” paradigm. Under secure conditions, a cloud digital twin acts as a high-capacity “Teacher,” employing Density-Based Spatial Clustering of Applications with Noise (DBSCAN) to partition heterogeneous user topologies. It then utilizes an energy-guided stochastic diffusion sampling (EGSDS) method to refine initial macroscopic routing, generating precise, outage-free global trajectories by systematically minimizing non-line-of-sight (NLoS) observation penalties and kinematic regularization costs. To counteract signal anomalies, a distributed Time Difference of Arrival (TDOA) anchor network continuously validates UAV coordinate integrity. If a threshold is breached, control authority is instantly transferred to the UAV’s edge digital twin. This resource-constrained edge tier relies on a localized “Student” network trained via progressive distillation. By compressing the computationally heavy iterative diffusion process into a rapid one-step inference model, the UAV autonomously generates a secure, short-range emergency path that strictly adheres to minimum communication thresholds. Once interference clears, the cloud seamlessly regains control to complete the logistics mission. Experimental results demonstrate that the proposed scheme significantly outperforms conventional heuristic routing methods in cloud-based scenarios. Furthermore, the edge-based distillation mechanism substantially improves the overall trajectory survival rate under signal anomalies, ensuring resilient and continuous logistics operations. Full article

Accurate Unmanned Aerial Vehicle (UAV) position estimation is the cornerstone of urban low-altitude safety management systems. Time Difference of Arrival (TDOA) and Remote Identification (Remote ID) are widely used surveillance technologies with complementary characteristics. TDOA provides high-rate updates but suffers from geometry-induced horizontal–vertical anisotropy and multipath effects, while Remote ID supplies absolute state information yet struggles with intermittent sampling and packet loss. Existing fusion schemes typically address these issues in isolation: sequential filtering manages asynchrony but assumes Gaussian noise, robust estimators suppress outliers at the cost of discarding valid data, and coupled-filter architectures allow vertical anomalies to contaminate horizontal estimates through the Kalman gain cross-coupling. No prior framework jointly handles structural TDOA altitude jumps, stochastic Remote ID timing jitter, and the geometric anisotropy between estimation subspaces within a single coherent pipeline. To bridge this gap, we propose a Hybrid Conditional Kalman Filter (HCKF) framework comprising three integrated modules. First, a kinematics-based temporal alignment module maps asynchronous measurements onto a uniform timeline and predicts missing samples, resolving cross-modal time mismatches. Second, a measurement quality evaluation mechanism detects TDOA altitude steps via robust two-layer stratification and scores Remote ID timing irregularity through a confidence mapping, converting these anomalies into dynamic covariance adjustments and weight caps without discarding observations. Third, a Subspace-Decoupled Fusion strategy exploits the physical insight that TDOA horizontal precision derives from hyperbolic intersection geometry, whereas its vertical estimates suffer from weak observability due to near-coplanar ground-station deployment. By applying entropy-guided weighting in the horizontal plane and a conditional Remote ID-dominant rule in the vertical axis, this design prevents cross-dimensional error propagation. The framework was validated using three real-world flight missions at distinct altitudes (255 m, 345 m, and 440 m) totaling 13.51 km of flight distance, with RTK serving as ground truth. HCKF reduces the Root Mean Square Error by over 40% relative to single-source baselines (95% bootstrap confidence interval: [35.2%, 48.7%]), and paired Wilcoxon signed-rank tests confirm statistically significant improvement ($p<0.01$) over standard EKF, Covariance Intersection, and Iterative CI across all three tracks. Full article

This paper proposes a spatial analog of the compass rose, interpreted as a discrete analog of cylindrical coordinates and considered as a basis for direction-based command filtering in Unmanned Aerial Vehicle (UAV) groups. The initial formulation is the problem of determining the direction to a radio signal source using data obtained by a group of four UAVs located at different altitudes. It is shown that, under conditions where the distance to the signal source significantly exceeds the characteristic size of the UAV spatial configuration, the direction to the source is determined much more reliably than the range to it. The results of Monte Carlo simulations confirm that the angular component of the solution remains meaningful under Time Difference of Arrival (TDoA) noise, whereas range reconstruction is substantially less stable. On this basis, a transition from a continuous description to a discrete sector representation of directions is proposed. The spatial compass rose is defined as a partition of the cylinder’s surface into a finite number of elements differing in azimuth and altitude. It is shown that this representation admits a natural algebraization: discrete directions can be one-to-one mapped to elements of finite fields and, therefore, interpreted in terms of multivalued logic. The obtained result creates the basis for simplifying computational procedures related to direction-sector classification and command processing in the on-board systems of UAV groups, provided that the method is interpreted as directional classification rather than complete three-dimensional localization. Full article

Reinforced concrete (RC) slabs, as the core load-bearing components in construction engineering, are prone to internal damage induced by impact loads, and accurate positioning of impact locations is a key task in structural health monitoring. The proposed method was developed for typical RC slabs such as building floors, bridge decks, and road slabs. Traditional acoustic emission (AE) positioning methods suffer from low positioning accuracy and a tendency to fall into local optimum when applied to RC slabs, which is attributed to the material’s heterogeneity, the complex propagation characteristics of stress waves and ambient noise interference. In this study, a GA-PSO hybrid algorithm is proposed, which integrates the global search capability of the Genetic Algorithm (GA) with the superior local convergence performance of the Particle Swarm Optimization (PSO) algorithm. The premature convergence issue of the traditional PSO algorithm is alleviated by adopting strategies including tournament selection, α hybrid crossover, boundary-constrained mutation, and linearly decreasing inertia weight. Based on the Time Difference of Arrival (TDOA) principle, the root mean square error between the theoretical and measured time differences is taken as the fitness function, and a boundary penalty mechanism is incorporated to ensure the physical validity of positioning results. AE data were acquired through drop weight impact tests to verify the performance of the proposed algorithm. Compared with traditional TDOA grid search, pure GA, and pure PSO methods under the same conditions, the proposed GA-PSO algorithm achieves an average localization error of only 54.95 mm, which is 61.0% lower than that of pure GA, while reducing the error standard deviation from approximately 114 mm to 24.87 mm. The average positioning error for all impact sources on the RC slab is within 100 mm, with the error in the central area as low as 42.97 mm. These results demonstrate that the GA-PSO algorithm significantly outperforms existing methods in terms of accuracy, stability, and maximum error control, verifying its high potential for impact source localization in complex heterogeneous materials. Full article

This study evaluates the feasibility of airborne acoustic source localisation (ASL) for in situ crack localisation in industrial laser-based directed energy deposition (DED-LB/M) fabricated structures. A four-microphone array combined with a Generalised Cross-Correlation with Phase Transform (GCC-PHAT) algorithm was used to estimate crack positions from time differences of arrival (TDOAs) extracted from raw acoustic emissions during multi-layer single-track fabrication. Prior to experimentation, the microphone array geometry was numerically optimised under industrial placement constraints by introducing controlled TDOA perturbations and minimising three-dimensional localisation uncertainty using alpha-shape volume analysis. Experimental validation was performed on six-layer single-track structures, with estimated crack positions compared against post-process microscopic measurements. Localisation errors ranged from 12 to 68 mm in the X-direction, 0.7–32 mm in the Y-direction, and 5–100 mm in the Z-direction. While horizontal localisation demonstrated centimetre-scale accuracy for most cracks, depth estimation exhibited greater variability. The results confirm that airborne ASL can provide meaningful spatial information regarding crack formation during DED-LB/M. However, localisation performance remains sensitive to TDOA estimation accuracy, microphone array constraints, and the complex acoustic environment inherent to the process. This work demonstrates the industrial feasibility of ASL for in situ crack investigation while highlighting the need for further advancements in array design and signal processing to achieve robust three-dimensional defect localisation in additive manufacturing systems. Full article

Optical Wireless Sensor Networks (OWSNs) have emerged as a promising solution for energy-efficient and secure data collection in free-space optical (FSO) environments. A key challenge in such networks is minimizing the decision error rate (DER) during decision aggregation at the central entity (CE). Building on earlier Time-Difference-of-Arrival (TDoA) reporting methods, this paper introduces an Angle-of-Arrival (AoA) framework for decision gathering. In the proposed scheme, sensor nodes equipped with Corner Cube Retro-reflectors (CCRs) passively communicate their local decisions, while the CE identifies such decisions based on AoA estimation. A closed-form expression for the DER is derived, incorporating false-alarm and missed-detection probabilities, and is validated through Monte Carlo simulations. Comparative evaluation against TDoA, Single Wavelength Parallel (SWP), and Multiple Wavelength Series (MWS) schemes shows that the AoA-based approach achieves consistently lower DERs, particularly in high-SNR regimes and larger node counts, closely approaching the theoretical lower bound. These results highlight AoA as a practical and scalable alternative to conventional decision-gathering methods in OWSNs. Full article

In wireless sensor networks (WSNs), angle of arrival (AOA) and time difference of arrival (TDOA) localization systems relying on distributed sensor measurements degrade significantly under high-order time-varying noise. Although traditional zeroing neural networks (ZNNs) handle dynamic localization tasks, their insufficient robustness against such high-order noise often compromises convergence stability and accuracy. To address this limitation, this paper proposes a polynomial anti-noise compensation ZNN (PAC-ZNN) incorporating a polynomial anti-noise compensation (PAC) term and a logarithmic mapping activation function (LMAF). Specifically, the PAC term mitigates the adverse effects of cumulative high-order noise, while the LMAF further enhances the convergence speed and stability of the system. The global convergence and robustness of the proposed PAC-ZNN are rigorously proven based on Lyapunov stability theory. Simulation results demonstrate that when applied to AOA and TDOA-based dynamic localization tasks, the proposed PAC-ZNN outperforms traditional ZNN-based solutions in terms of anti-noise capability, convergence efficiency, and localization precision under high-order noise conditions. Furthermore, it maintains robust tracking performance even under complex multipath environments, verifying its superior performance and practical application value. Full article

High-frequency (HF) skywave propagation exploits ionospheric reflection for beyond-line-of-sight transmission, making time-difference-of-arrival (TDOA)-based geolocation a primary technique for localizing non-cooperative HF emitters. However, reliable TDOA estimation remains challenging due to time-varying ionospheric conditions, wideband multipath dispersion, and low signal-to-noise ratio (SNR). This paper proposes an integrated framework coupling realistic channel synthesis, deep learning-based TDOA estimation, and convex optimization-based localization. Three contributions are made. First, an improved wideband ionospheric channel model is constructed by integrating the International Reference Ionosphere (IRI) with region-specific calibration and a stochastic perturbation module, yielding time-varying multipath responses for physics-consistent waveform generation. Second, a convolutional neural network (CNN)-based TDOA estimator is designed to jointly exploit time-domain complex-baseband in-phase/quadrature (I/Q) waveforms, multi-weight generalized cross-correlation (GCC) feature maps, and channel-state information (CSI) within a unified regression network, achieving robust delay estimation under severe noise and multipath conditions. Third, the geolocation problem is formulated as a bias-regularized constrained least-squares problem with unknown ionospheric excess-delay surrogates, and a semidefinite programming (SDP) relaxation is derived to yield a tractable solution without prescribing a fixed virtual reflection height. Simulations show that the proposed estimator consistently outperforms competing algorithms across a wide SNR range and narrows the gap to the Cramér–Rao lower bound (CRLB) at high SNR. On field-recorded signals, the estimator reduces the mean absolute TDOA deviation by 51% relative to GCC with phase transform (GCC-PHAT), and the end-to-end pipeline achieves a mean geolocation error of 19.67 km across 100 field segments, outperforming all compared baselines. Full article

In this work, we propose a localization method based on an exogenous radar with multi-base station and the synchronization signal block (SSB) in 5G downlink signals. We combine physical cell identities (PCIs)-based identification with the extensive cancellation algorithm (ECA) to reconstruct and cancel the present strongest SSB signal, thereby obtaining reference signal receiving power (RSRP) values of them in descending order of strength. Then, we designed a two-stage localization method. Firstly, we determined the target’s coarse location based on the directional characteristics of different SSB beams. Subsequently, we compared the RSRP values extracted from the actually received signals against those pre-obtained when the target is at various reference points. The reference point corresponding to the closest match was selected as the estimated target position. We conducted simulations under various signal-to-noise ratio (SNR) levels, reference point densities, and signal jitter conditions. The simulation results demonstrate that the method outperforms techniques such as Fang’s method for time difference of arrival (Fang-TDOA) and observed time difference of arrival (OTDOA). Full article

We present PIRATE (Precision Imaging Real-time Autonomous Tracker and Explorer), a fully autonomous unmanned surface vehicle designed to enable self-operating data collection and persistent tracking of mobile underwater targets through the tight integration of acoustic localization, onboard visual perception, and closed-loop navigation. PIRATE employs a single mobile acoustic receiver to estimate target position using time-difference-of-arrival (TDoA) measurements acquired at different times and locations through planned autonomous motion and uses these estimates to drive adaptive vehicle behavior and activate fine-grained visual sensing in real time. This architecture enables sustained target-driven operation, in which navigation, acoustic monitoring, and visual processing are dynamically coordinated based on mission context and localization uncertainty. The system integrates real-time AI-based visual detection and tracking with automatic mission control, allowing visual perception to operate opportunistically within an acoustically guided tracking loop rather than as a standalone sensing modality. Field experiments in a shallow-water environment demonstrate reliable autonomous navigation, single-receiver acoustic localization with meter-scale accuracy, and stable onboard visual inference under sustained operation. By enabling coupled acoustic tracking and onboard visual perception in a fully autonomous surface platform free of external infrastructure, PIRATE provides a practical foundation for fine-scale behavioral observation, adaptive marine monitoring, and long-duration studies of mobile underwater organisms. We demonstrate this advantage with two possible applications. Full article

Time Difference of Arrival (TDOA)-based localization is widely used for its stability and high accuracy. However, in multi-source scenarios, TDOA measurements from multiple sources become entangled, making it difficult to separate and correctly associate them for accurate localization. To address this challenge, this paper proposes a cascaded DOA-TDOA-based multi-source weighted localization algorithm that leverages the strengths of Direction of Arrival (DOA)-based methods for separating multi-source signals and the high precision of TDOA-based methods for single-source localization. The proposed method first estimates the DOAs of multiple sources and performs DOA matching based on geometric consistency to obtain initial coarse position estimates. Subsequently, it applies wideband spatial filtering to wideband signals using the Minimum Variance Distortionless Response (MVDR) to separate multi-source signals, enhance the signal-to-noise ratio (SNR), and thereby guide the selection of the reference station and the performance of TDOA estimation. Then, TDOA estimation is performed, while the weights are assigned based on the difference in GDOP (D-GDOP), computed from the initial coarse estimate, and a weighted least-squares (WLS) method is applied to obtain the refined estimate. Finally, the D-GDOP of the refined estimate can be computed and used to reassign weights, yielding more accurate position estimate. Simulation results validate the effectiveness of the proposed method, showing superior estimation accuracy and robustness relative to existing approaches. Full article

This paper investigates Direction-of-Arrival (DOA) estimation of Long-Range Navigation-C (Loran-C) signals using an Ultra-Short Baseline (USBL) receiving array. Two least-squares angle estimation approaches based on inter-element delay measurements are examined, including Correlation-based Least-Squares (Corr-LS) and a Zero-Crossing-based Least Squares (ZC-LS). In both methods, relative delays are extracted only within the local array and subsequently mapped to azimuth through a geometric least squares formulation; the approach is, therefore, distinct from distributed time difference-of-arrival (TDOA) localization. For comparison, the Multiple Signal Classification (MUSIC) algorithm is implemented as a covariance-based DOA estimator that operates without explicit delay extraction. Experiments were conducted using Loran-C transmissions from the Xuancheng, Xi’an, and Rongcheng stations, with 100 valid pulse groups collected for each station. Statistical analysis using boxplots shows that Corr-LS exhibits the largest variance due to broadened or shifted correlation peaks, particularly under skywave–groundwave interference. ZC-LS reduces both variance and bias by exploiting the deterministic zero-crossing structure of the Loran-C waveform. MUSIC produces the most concentrated azimuth estimates but requires a well-conditioned covariance matrix and substantially higher computational costs. The results demonstrate that ZC-LS achieves a favorable balance among angular accuracy, robustness, and real-time feasibility, making it suited for compact Loran-C receivers and complementary navigation applications in GNSS-challenged environments. Full article

Time difference of arrival (TDOA) localization enables high-accuracy positioning by analyzing arrival-time differences of target signals at distributed radar nodes, whose performance strongly depends on radar node topology. However, existing studies tend to focus more on improving localization accuracy, while overlooking the impact of radar geometric layout and surveillance coverage on localization performance. To this end, this paper proposes a topology optimization method for a distributed radar system based on an improved non-dominated sorting multi-objective particle swarm optimization (NS-MOPSO) algorithm. A geometric localization model is developed for a distributed TDOA radar system. Based on this model, three optimization objectives are formulated, including minimizing geometric dilution of precision (GDOP), maximizing target coverage, and improving the geometric balance of node placement. These three objective functions are incorporated into the NS-MOPSO framework to achieve a more reasonable radar geometric distribution. To enhance the optimization performance, a series of strategies are adopted, such as non-dominated sorting for Pareto-based solution selection, an improved crowding-distance scheme to encourage balanced multi-objective optimization, and Gaussian mutation to increase solution diversity and reduce the risk of premature convergence. To validate the proposed method, both simulation studies and real-world experiments were conducted under different node deployment scenarios. The results show that the optimized topology achieves a 6.4% reduction in RMSPE and a 4.3% increase in the proportion of high-quality localization regions compared with the best-performing comparative method, while also demonstrating faster convergence and improved stability. These findings confirm the effectiveness and robustness of the proposed approach in enhancing localization accuracy, expanding effective coverage, and improving overall system performance. Full article