Search Results (243)

This paper presents a visual active SLAM method considering measurement and state uncertainty for space exploration in urban search and rescue environments. An uncertainty evaluation method based on the Fisher Information Matrix (FIM) is studied from the perspective of evaluating the localization uncertainty of SLAM systems. With the aid of the Fisher Information Matrix, the Cramér–Rao Lower Bound (CRLB) of the pose uncertainty in the stereo visual SLAM system is derived to describe the boundary of the pose uncertainty. Optimality criteria are introduced to quantitatively evaluate the localization uncertainty. The odometry information selection method and the local bundle adjustment information selection method based on Fisher Information are proposed to find out the measurements with low uncertainty for localization and mapping in the search and rescue process. By adopting the method above, the computing efficiency of the system is improved while the localization accuracy is equivalent to the classical ORB-SLAM2. Moreover, by the quantified uncertainty of local poses and map points, the generalized unary node and generalized unary edge are defined to improve the computational efficiency in computing local state uncertainty. In addition, an active loop closing planner considering local state uncertainty is proposed to make use of uncertainty in assisting the space exploration and decision-making of MAV, which is beneficial to the improvement of MAV localization performance in search and rescue environments. Simulations and field tests in different challenging scenarios are conducted to verify the effectiveness of the proposed method. Full article

To address the challenges of complex low-altitude flight environments for UAVs, where numerous obstacles often lead to GPS signal obstruction and multipath effects, this study proposes an integrated inertial navigation and GPS positioning approach for coordinated cross-path planning in drone swarms. The methodology involves the following: (1) discretizing continuous 3D airspace into grid cells using occupancy grid mapping to construct an environmental model; (2) analyzing dynamic flight characteristics through attitude angle variations in a 3D Cartesian coordinate system; and (3) implementing collaborative state updates and global positioning through fused inertial–GPS navigation. By incorporating Cramér–Rao lower bound optimization, the system achieves effective cross-path planning for drone formations. Experimental results demonstrate a 98.35% mission success rate with inter-drone navigation time differences maintained below 0.5 s, confirming the method’s effectiveness in enabling synchronized swarm operations while maintaining safe distances during cooperative monitoring and low-altitude flight missions. This approach demonstrates significant advantages in coordinated cross-path planning for UAV clusters. Full article

To address the problem of accurate localization of high-speed drone swarm intrusions, this paper adopts time difference of arrival (TDOA) and frequency difference of arrival (FDOA) measurements, aiming to improve the performance of estimating the motion state of drone swarms. To this end, a two-step strategy is proposed in this study. Firstly, a small number of sensor nodes with random locations are selected in the wireless sensor network, and the constraint-weighted least squares (CWLS) method is used to obtain the rough position and speed information of the drone swarm. Based on this rough information, the objective function of node optimization is constructed and solved using the randomized semidefinite program (SDP) algorithm proposed in this paper to screen out the sensor nodes with optimal localization performance. Secondly, the sensor nodes screened in the first step are used to re-localize the drone swarm, and the CWLS problem is constructed by combining the TDOA and FDOA measurements, and a deviation elimination scheme is proposed to further improve the localization accuracy of the drone swarm. Simulation results show that the randomized SDP algorithm proposed in this paper has the optimal localization effect, and moreover, the bias reduction scheme proposed in this paper can make the localization error of the drone swarm reach the Cramér–Rao Lower Bound (CRLB) with a low signal-to-noise ratio (SNR). Full article

Underwater radiated noise power estimation is crucial for the quantitative assessment of noise levels emitted by ships and underwater vehicles. This paper therefore proposes a maximum likelihood estimation method for determining the power of underwater radiated noise. The method establishes the probability density function of the hydrophones array received data and derives the minimum variance unbiased estimation of the power through theoretical analysis under the maximum likelihood criterion. Numerical simulations and experimental data demonstrate that this method can significantly reduce the influence of ambient noise on estimation results and improve the estimation accuracy under low signal-to-noise ratio conditions, outperforming commonly used beamforming-based estimation methods. In addition, the estimation variance achieves the Cramér–Rao lower bound, which is consistent with theoretical derivation. When the source position is unknown, this method can simultaneously localize the sound source and estimate its power by searching for the maximum value within a specified region. Full article

Today, Moon exploration is driven by the desire to expand the human presence beyond Earth and to use its resources. This requires the development of reliable navigation systems that can provide positioning information accurately and continuously on the lunar surface and orbits. Initiatives such as Moonlight (by ESA) and the Cislunar Autonomous Positioning System project (by NASA) are underway to address this challenge. The aim is to use ranging signals transmitted by satellites, similar to Earth’s GNSS, for lunar user positioning. This paper proposes a solution that involves local sensors deployed on the Moon surface to enhance the performance of the satellite system. These sensors can serve as differential reference stations, correcting satellite pseudorange measurements obtained by lunar surface receivers. The local sensor can also be used as a pseudolite, transmitting satellite-like signals to improve system availability and accuracy in obstructed areas. Additionally, the local sensor can act as an independent beacon that provides range and angle measurements. Higher navigation performance can be achieved by increasing the complexity of the system, depending on the implemented solution. This paper proposes and shows the concept, the intial design, and a preliminary definition of the protocol for the third solution. The three different solutions are compared in terms of position accuracy by exploiting the Cramér–Rao Lower-Bound formulation and Monte Carlo simulations. Finally, possible implementations for future use on the Moon are discussed. Full article

High-frequency surface wave radar (HFSWR) is unable to measure the target’s altitude information due to its limited antenna aperture in the elevation dimension. This paper focuses on the 3-D localization problem for moving targets within the line of sight (LOS) in multistatic HFSWR. For this purpose, the 1-D space angle (SA) measurement is introduced into multistatic HFSWR to perform 3-D joint localization together with bistatic range (BR) and bistatic range rate (BRR) measurements. The target’s velocity can also be estimated due to the inclusion of BRR. In multistatic HFSWR, commonly used azimuth measurements offer no information about the target’s altitude. Since SA is associated with the target’s 3-D coordinates, combining SA measurements from multiple receivers can effectively enhance localization accuracy, particularly in altitude estimation. In this paper, we develop a two-stage localization algorithm that first derives a weighted least-squares (WLS) coarse estimate and then performs an algebraic error reduction (ER) procedure to enhance accuracy. Both stages yield closed-form results, thus ensuring overall computational efficiency. Theoretical analysis shows that the proposed WLS-ER algorithm can asymptotically attain the Cramér–Rao lower bound (CRLB) as the measurement noise decreases. Simulation results demonstrate the effectiveness of the proposed WLS-ER algorithm and highlight the contribution of SA measurements to altitude estimation in multistatic HFSWR. Full article

The dynamic and heterogeneous nature of marine environments, combined with severely constrained communication and energy resources, presents distinct challenges in constructing underwater Communication, Positioning, Navigation, and Timing (CPNT) systems compared to terrestrial Positioning, Navigation, and Timing (PNT) architecture. To address the inherent limitations of conventional decoupled CPNT systems – including high costs and low efficiency in communication and energy utilization – this study aims to propose a unified underwater CPNT ($U^{2}CPNT$) system that coordinates multi-modal data and resource allocation, thereby optimizing CPNT service performance in harsh underwater conditions. In this study, Cramér-Rao Lower Bound (CRLB) formalization is applied to theoretically analyze the feasibility of $U^{2}CPNT$ system, and the design of $U^{2}CPNT$ system is presented to realize the integrated design of CPNT. To validate the system performance, a real $U^{2}CPNT$ system was built and sea trials were conducted. With $U^{2}CPNT$ architecture, the integrated CPNT service can be provided, the positioning error is lower, the positioning continuity has improved by 7.68%, the velocity estimation error is less than 1 m/s, making $U^{2}CPNT$ a potential solution for underwater CPNT service. Full article

This work investigates the performance of distributed beamforming in Wireless Sensor Networks (WSNs), focusing on the impact of node position errors. A comprehensive simulation testbed was developed to assess how varying network topologies and position uncertainties impact system performance. Our results reveal that distributed beamforming in the near-field is highly sensitive to position errors, resulting in a noticeable degradation in performance, particularly in terms of Bit Error Rate (BER). Cramer–Rao Lower Bound (CRB) was used to analyse the theoretical limitations of position estimation accuracy and how these limitations affect beamforming performance. These findings underscore the critical importance of accurate localisation techniques and robust beamforming algorithms to fully realise the potential of distributed beamforming in practical WSN applications. Full article

A two-stage fusion filtering positioning algorithm based on prediction residuals and gain adaptation is proposed to address the problems of disturbance and modeling errors in the application of distance-based positioning algorithms in wireless sensor networks, as well as inaccurate initial filtering values leading to large estimation errors or even divergence. Firstly, based on parameterization methods, a pseudo linear equation is constructed from the time of arrival (TOA) and multipath delay. The weighted least squares (WLS) method is applied to obtain the initial value of target position resolution, and its performance approaches the Cramér–Rao lower bound (CRLB). Secondly, the exact position of the target is obtained using the reconstructed Gaussian white noise statistics and the Kalman filtering algorithm. The simulation results show that compared with other initial positioning algorithms, the average positioning accuracy of the proposed algorithm is improved by 28.57%, and it has a better filtering performance. Full article

This paper proposes a dynamic unmanned aerial vehicle (UAV) clustering model for multi-target localization in complex 3D environments, where mobility-aware cluster formation is integrated to enhance collaborative localization accuracy. We derive the Cramér–Rao lower bound (CRLB) for localization performance analysis under measurement and motion-induced uncertainties. To solve the NP-hard clustering problem, we develop the MDQPSO-ASA algorithm, which combines multi-swarm discrete quantum-inspired particle swarm optimization with adaptive simulated annealing, incorporating a repair mechanism to satisfy spatial and cardinality constraints. Simulation results demonstrate the algorithm’s superiority in localization accuracy, computational efficiency, and adaptability to varying UAV/target scales compared to baseline methods. The developed algorithm provides an effective solution for resource-constrained collaborative localization tasks in practical scenarios. Full article

This study presents an improved methodology for celestial triangulation optimization in spacecraft navigation, addressing limitations in existing approaches. While current methods like Linear Optimal Sine Triangulation (LOST) provide statistically optimal solutions for position estimation using multiple celestial body observations, their performance can be compromised by suboptimal measurement pair selection. The proposed approach, called the Improved-LOST algorithm, introduces a systematic method for evaluating and selecting optimal measurement pairs based on a Cramér–Rao Lower-Bound (CRLB) analysis. Through theoretical analysis and numerical simulations on translunar trajectories, this study demonstrates that geometric configuration significantly influences position estimation accuracy, with error variances varying by orders of magnitude depending on observation geometry. The improved algorithm outperforms conventional implementations, particularly in scenarios with challenging geometric configurations. Simulation results along a translunar trajectory using various celestial body combinations show that the systematic selection of measurement pairs based on CRLB minimization leads to enhanced estimation accuracy compared to arbitrary pair selection. The findings provide valuable insights for autonomous navigation system design and mission planning, offering a quantitative framework for assessing and optimizing celestial triangulation performance in deep space missions. Full article

The synthetic aperture passive positioning (SAPP) method has attracted the attention of researchers due to its high positioning resolution. However, there are still key technical issues regarding SAPP methods, such as residual frequency offset (RFO) coupling at Doppler frequency leading to decreased positioning accuracy, and non-periodic discontinuous signals emitted by unknown radiation sources (NRSs) causing positioning algorithm failure. Therefore, this paper proposes an ultra-short baseline SAPP method based on interferometer assistance. Firstly, conjugate multiplication is applied to the received signals of the interferometer’s dual antennas to obtain a single frequency received signal corresponding to the straight-line distance. Subsequently, the proposed step search (SS) algorithm based on cross-correlation analysis is used to obtain the receiving frequency of the single frequency signal, and the initial positioning distance is calculated using the corresponding mapping relationship based on this frequency. Finally, NRS positioning is completed in the two-dimensional coordinates of azimuth and range by combining with the signal arrival angle. The positioning results of this method are insensitive to RFO, and even if NRS emits non-periodic discontinuous signals, the proposed method can successfully locate them. In addition, the Cramer–Rao lower bound (CRLB) of the localization for this method is derived. The simulation and unmanned aerial vehicle (UAV) experimental results demonstrate the effectiveness and feasibility of this method. Full article

The current self-calibration approaches based on sparse Bayesian learning (SBL) demonstrate robust performance under uniform white noise conditions. However, their efficacy degrades significantly in non-uniform noise environments due to acute sensitivity to noise power estimation inaccuracies. To address this limitation, this paper proposes an orientation estimation method based on variational Bayesian inference to combat non-uniform noise and gain/phase error. The gain and phase errors of the array are modeled separately for calibration purposes, with the objective of improving the accuracy of the fit during the iterative process. Subsequently, the noise of each element of the array is characterized via independent Gaussian distributions, and the correlation between the array gain deviation and the noise power is incorporated to enhance the robustness of this method when operating in non-uniform noise environments. Furthermore, the Cramér–Rao Lower Bound (CRLB) under non-uniform noise and gain-phase deviation is presented. Numerical simulations and experimental results are provided to validate the superiority of this proposed method. Full article

The high-precision estimation of multi-dimensional parameters for spatial targets based on high-resolution range profiles is crucial for target recognition. However, existing estimation methods face difficulties in resolving the strong coupling between the target shape and the micro-motion parameters, as well as in fully utilizing micro-motion information under complex modulation characteristics. To address these challenges, this paper proposes a multi-dimensional parameter-estimation method for spatial targets based on micro-range decomposition. A micro-range model of the target is first constructed, and the micro-range modulation characteristics are analyzed. Then, micro-range coefficients are selected based on their Cramér–Rao lower bound (CRLB), and the correlation between these coefficients and target parameters is exploited for scattering center matching. An optimization model is further built for multi-dimensional parameter estimation, enabling the accurate estimation of parameters such as precession frequency, precession angle, and structural dimensions under both single-view and multi-view conditions. The experimental results show that in the dual-view case, all parameters are estimated with relative errors (REs) below 1.15% and root mean square error (RMSE) values below 0.05. In the single-view case, key parameters are estimated with REs under 15%. Compared with conventional methods, the proposed method achieves lower RMSE and significantly improved robustness and stability. These results demonstrate the effectiveness and practical potential of the proposed method for spatial target parameter estimation. Full article

In this paper, the problem of source localization using only frequency difference of arrival (FDOA) measurements is considered. A new FDOA-only localization technique is developed to determine the position of a narrow-band source. In this scenario, time difference of arrival (TDOA) measurements are not normally useful because they may have large errors due to the received signal having a small bandwidth. Conventional localization algorithms such as the two-stage weighted least squares (TSWLS) method, which jointly exploits TDOA and FDOA measurements for positioning, are thus no longer applicable since they will suffer from the thresholding effect and yield meaningless localization results. FDOA-only localization is non-trivial, mainly due to the high nonlinearity inherent in FDOA equations. Even with two FDOA measurements being available, FDOA-only localization still requires finding the roots of a high-order polynomial. For practical scenarios with more sensors, a divide-and-conquer (DAC) approach may be applied, but the positioning solution is suboptimal due to ignoring the correlation between FDOA measurements. To address these challenges, in this work, we propose a Bayesian approach for FDOA-only source positioning. The developed method, referred to as the Gaussian division method (GDM), first converts one FDOA measurement into a Gaussian mixture model (GMM) that specifies the prior distribution of the source position. Next, the GDM assumes uncorrelated FDOA measurements and fuses the remaining FDOAs sequentially by invoking nonlinear filtering techniques to obtain an initial positioning result. The GDM refines the solution by taking into account and compensating for the information loss caused by ignoring that the FDOAs are in fact correlated. Extensive simulations demonstrate that the proposed algorithm provides improved performance over existing methods and that it can attain the Cramér–Rao lower bound (CRLB) accuracy under moderate noise levels. Full article