Search Results (350)

Orthogonal frequency division multiplexing (OFDM) has been regarded as an attractive waveform for integrated sensing and communication (ISAC). However, suffering from its high peak-to-average power ratio (PAPR), sensitivity to phase noise (PN), and spectral efficiency saturation, the performance of OFDM in ISAC is limited. Against this background, this paper proposes a constellation-optimized index-modulated OFDM (CO-IM-OFDM) framework that leverages neural networks to design a constellation suitable for subcarrier activation patterns. A correlation model between index modulation and constellation is established, enabling adaptive constellation mapping in IM-OFDM. Then, Adam optimizer is employed to train the constellation tailored for ISAC, enhancing spectral efficiency under PN and PAPR constraints. Furthermore, a weighting factor is defined to characterize the joint communication–sensing performance, thus optimizing the overall system performance. Simulation results demonstrate that the proposed method can achieve improvements in bit error rate (BER) by over 4 dB and in Cramér–Rao bound (CRB) by 2% to 8% compared to traditional IM-OFDM constellation mapping. It overcomes fixed constellation constraints of conventional IM-OFDM systems, offering theoretical innovation waveform design for low-power communication–sensing systems in highly dynamic environments. Full article

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

More than seventy-five years ago, R. Hanbury Brown and R. Q. Twiss performed the first experiments in quantum optics. At the outset, their results showed great promise for the field of astronomical science, featuring inexpensive hardware, immunity to atmospheric turbulence, and enormous interferometry baselines. This was put to good use for the determination of stellar diameters up to the present time. However, for two-dimensional imaging with faint objects, the integration times are prohibitive. Recently, in a sequence of papers, the present author developed a stochastic search algorithm to remove this roadblock, reducing millions of hours to minutes or seconds. Also, the author’s paper entitled “The Rise of the Brown-Twiss Effect” summarized the search algorithm and emphasized the mathematical proofs of the algorithm. The current algorithm is a sequence of six lines of code. The goal of the present article is to streamline the algorithm in the form of a discrete-time dynamic system and to reduce the size of the state space. The previous algorithm used initial conditions that were randomly assorted pixel intensities. The intensities were mutually statistically independent and uniformly distributed over the range $\left[0,\delta \right)$, where $\delta$ is a (very small) positive constant. The present formulation employs a transformation requiring the uniformly distributed phase of the fast Fourier transform of the cross correlations of the data as initial conditions. We shall see that this strategy results in the simplest discrete-time dynamic system capable for exploring the alternate features and benefits of compartmental nonnegative dynamic systems. Full article

This paper addresses the problem of target localization in a distributed waveform diverse array radar system, exploiting the technique of sparse reconstruction. At the configuration stage, the distributed radar system consists of two individual Frequency Diverse Array Multiple-Input Multiple-Output (FDA-MIMO) radars and one single Element-Pulse Coding MIMO (EPC-MIMO) radar. To obtain the angle and incremental range (i.e., the range offset between the sampling point and actual position within the range bin) of the targets in each local radar, two sparse reconstruction-based algorithms, including the grid-based Iterative Adaptive Approach (IAA) and gridless Atomic Norm Minimization (ANM) algorithms, are implemented. Furthermore, multiple sets of local statistics are fused at the fusion center, where a Weighted Least Squares (WLS) method is performed to localize targets. At the analysis stage, the estimation performance of the proposed methods, encompassing both IAA and ANM algorithms, is evaluated in contrast to the Cramér–Rao Bound (CRB). Numerical results and parametric studies are provided to demonstrate the effectiveness of the proposed sparse reconstruction methods for target localization in the distributed waveform diverse array system. 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

In this paper, we establish criteria for the design of small aperture antenna arrays for Direction of Arrival (DOA) estimation. We define a small aperture antenna array as one consisting of a few elements with an average interelement spacing less than or equal to half a wavelength. We use the spatial covariance matrix of the antenna array to derive the design criterion. It is well known that the DOA estimation performance of an antenna array is strongly related to the amount of information in this matrix. Also, the Cramer-Rao Bound of the estimated DOA is closely related to this matrix. We establish and demonstrate that, for optimal DOA estimation performance, a small aperture antenna array should have non-uniformly spaced and dissimilar antenna elements. Since mutual coupling between antenna elements makes their in situ responses dissimilar, instead of mitigating mutual coupling, one should include mutual coupling in the DOA estimation process to enhance the DOA estimation performance of antenna arrays. 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

This paper presents an uncertainty quantification analysis of the first fix in a time-differenced carrier phase (TDCP) observation model. TDCP is a widely used method in GNSS-based odometry for precise positioning and displacement estimation. A key point in the TDCP modeling is the assumption that the GNSS receiver’s initial position is perfectly known, which is never exactly the case in real-world applications. This study assesses the impact of initial position errors on estimated displacement by formulating a correct TDCP model and a misspecified one, where the first position is not correct. Theoretical derivations provide a generic framework of estimation under the misspecified model and its associated mean squared error (MSE), as well as estimation performance bounds through the misspecified Cramer Rao bound (MCRB) for the considered case. These theoretical considerations are then used to build an estimator of the receiver’s displacement, with comparisons to the MCRB for performance evaluation. Extensive simulations using realistic GNSS geometry assess the influence of a first-fix error under various conditions, including different time intervals, first-fix error norms, and first-fix error direction. As an example, it is shown that for the considered geometry, if a TDCP of $t_2-t_1=1$ s is built with an initial first fix error norm $\parallel \mathsf{\Delta }{\mathit{r}}_1\parallel =10 \mathrm{m}$, then it introduces an estimation of the displacement, with an error of norm equal to $1.3 \mathrm{m}\mathrm{m}$, at most. The results indicate that the displacement estimation error is linearly related to the initial position error and the time interval between observations, highlighting the importance of accurate first-fix estimation for reliable TDCP-based odometry. The findings contribute to highlighting the order of magnitude of errors on solutions as a function of the error on parameters. 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

This article presents a Synthetic-Aperture Distributed Phased Array (SADPA) framework to address emitter localization challenges in dynamic environments. Building on Distributed Synthetic-Aperture Radar (DSAR) principles, SADPA integrates distributed phased arrays with motion-induced phase compensation, enabling coherent aperture synthesis beyond physical array limits. By analytically modeling and compensating nonlinear phase variations caused by platform motion, we resolve critical barriers to signal integration while extending synthetic apertures. An improved MUSIC algorithm jointly estimates emitter positions and phase distortions, overcoming parameter coupling inherent in moving systems. To quantify fundamental performance limits, the Cramer–Rao bound (CRB) is derived as a theoretical benchmark. Numerical simulations demonstrate the SADPA framework’s superior performance in multi-source resolution and positioning accuracy; it achieves 0.012 m resolution at 10 GHz for emitters spaced 0.01 m apart. The system maintains consistent coherent gain exceeding 30 dB across both the 1.5 GHz communication and 10 GHz radar bands. Monte Carlo simulations further reveal that the MUSIC-DPD algorithm within the SADPA framework attains minimum positioning error (RMSE), with experimental results closely approaching the theoretical CRB. Full article

A significant advantage of off-grid direction-of-arrival (DOA) estimation algorithms using difference coarrays is their ability to resolve more sources than the number of physical sensors. Current coarray-based off-grid DOA estimation algorithms experience a significant decline in estimation accuracy with limited snapshots. Moreover, most existing DOA estimation techniques exhibit a high computational complexity, limiting their practical implementation in real-time systems. To address these limitations, in this work, we propose a novel coarray-based off-grid DOA estimation algorithm that achieves a computationally efficient performance while maintaining a high estimation accuracy under snapshot-constrained conditions. The proposed algorithm first performs DOA estimation through coarray-augmented spatial smoothing multiple signal classification (SS-MUSIC), followed by noise suppression via multiplication with a constructed selection matrix. The off-grid angular deviations are sequentially refined based on the iterative correction mechanism. The disadvantage of a large number of snapshots requirement is overcome thanks to the combination of noise elimination and sequential angle refinement. Theoretical performance bounds are established through Cramér–Rao bound (CRB) analysis, while comprehensive simulations validate the estimation accuracy of the proposed algorithm and the robustness in off-grid scenarios. Full article