Search Results (50)

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

This paper proposes a distance estimation error reduction framework to improve ground node localization accuracy in urban environments using an unmanned aerial vehicle (UAV) and path loss measurements. The primary goal of the framework is to bound distance estimation errors arising from inherent inaccuracies in path loss measurements. A k-means clustering algorithm is first applied to identify the region in which the ground node is located. Then, an analytical approach is used to select UAV waypoints. Moreover, a mean-based exponential smoothing approach is employed to refine the path loss measurements of the selected waypoints to mitigate the effects of multipath components that introduce significant errors in distance estimation. Finally, two estimators, maximum likelihood (ML)-based and semidefinite programming (SDP)-based relaxation, are employed to estimate the ground node’s location, validating the effectiveness of the proposed framework. Evaluations using ray tracing simulation data demonstrate a notable improvement in localization accuracy. The proposed framework effectively bounds the distance estimation errors and significantly reduces overall localization errors compared to conventional unbounded methods. Moreover, both estimators with the proposed framework achieve comparable localization accuracy, highlighting the framework’s capability to address key challenges in ML-based localization. Full article

Wireless localization is a fundamental component of modern sensor networks, with applications spanning environmental monitoring and smart cities. Ensuring accurate and efficient localization is critical for enhancing network performance and reliability, particularly in the presence of signal attenuation and noise. This study proposes a novel two-step localization framework, SD/SOCP-GTRS, to improve the precision of target localization using received signal strength (RSS) measurements. In the first step (SD/SOCP), semidefinite programming (SDP) and second-order cone programming (SOCP)-based convex relaxation are applied to the maximum likelihood (ML) estimator, generating an initial coarse estimate. The second step (GTRS) refines this estimate using weighted least squares (WLS) and the generalized trust region subproblem (GTRS), mitigating performance degradation caused by relaxation. Monte Carlo simulations validate that the proposed SD/SOCP-GTRS approach effectively reduces root mean square error (RMSE) compared to other methods. These findings demonstrate that the SD/SOCP-GTRS framework consistently outperforms existing techniques, approaching the theoretical performance limit and offering a robust solution for high-precision localization in wireless sensor networks. Full article

In this paper, we consider the issue of the physical layer security (PLS) problem between two nodes, i.e., transmitter (Alice) and receiver (Bob), in the presence of an eavesdropper (Eve) in a near-field communication (NFC) system. Notably, massive multiple-input multiple-output (MIMO) arrays significantly increase array aperture, thereby rendering the eavesdroppers more inclined to lurk near the transmission end. This situation necessitates using near-field channel models to more accurately describe channel characteristics. We consider two schemes with imperfect channel estimation information (CSI). The first scheme involves a conventional multiple-input multiple-output multiple-antenna eavesdropper (MIMOME) setup, where Alice simultaneously transmits information signal and artificial noise (AN). In the second scheme, Bob operates in a full-duplex (FD) mode, with Alice transmitting information signal while Bob emits AN. We then jointly design beamforming and AN vectors to degrade the reception signal quality at Eve, based on the signal-to-interference-plus-noise ratio (SINR) of each node. To tackle the power minimization problem, we propose an iterative algorithm that includes an additional constraint to ensure adherence to specified quality-of-service (QoS) metrics. Additionally, we decompose the robust optimization problem of the two schemes into two sub-problems, with one that can be solved using generalized Rayleigh quotient methods and the other that can be addressed through semi-definite programming (SDP). Finally, our simulation results confirm the viability of the proposed approach and demonstrate the effectiveness of the protection zone for NFC systems operating with CSI. Full article

We consider optimal designs for clinical trials when response variance depends on treatment and covariates are included in the response model. These designs are generalizations of Neyman allocation, and commonly employed in personalized medicine where external covariates linearly affect the response. Very often, these designs aim at maximizing the amount of information gathered but fail to assure ethical requirements. We analyze compound optimal designs that maximize a criterion weighting the amount of information and the reward of allocating the patients to the most effective/least risky treatment. We develop a general representation for static (a priori) allocation and propose a semidefinite programming (SDP) formulation to support their numerical computation. This setup is extended assuming the variance and the parameters of the response of all treatments are unknown and an adaptive sequential optimal design scheme is implemented and used for demonstration. Purely information theoretic designs for the same allocation have been addressed elsewhere, and we use them to support the techniques applied to compound designs. Full article

Angle-of-arrival (AOA) measurements are often used in underwater acoustical localization. Different from the traditional AOA model based on azimuth and elevation measurements, the AOA model studied in this paper uses bearing measurements. It is also often used in the Ultra-Short Baseline system (USBL). However, traditional acoustical localization needs additional range information. If the range information is unavailable, the closed-form solution is difficult to obtain only with bearing measurements. Thus, a localization closed-form solution using only bearing measurements is explored in this article. A pseudo-linear measurement model between the source position and the bearing measurements is derived, and considering the nonlinear relationship of the parameters, a weighted least-squares optimization equation based on multiple constraints is established. Different from the traditional two-step least-squares method, the semidefinite programming (SDP) method is designed to obtain the initial solution, and then a bias compensation method is proposed to further minimize localization errors based on the SDP result. Numerical simulations show that the performance of the proposed method can achieve Cramer–Rao lower bound (CRLB) accuracy. The field test also proves that the proposed method can locate the source position without range measurements and obtain the highest positioning accuracy. Full article

This paper presents an efficient two-dimensional (2D) direction of arrival (DOA) estimation method, termed as decoupled projected atomic norm minimization (D-PANM), to solve the angle-ambiguity problem. It first introduces a novel atomic metric via projecting the original atom set onto a smoothing space, based on which we formulate an equivalent semi-definite programming (SDP) problem. Then, two relatively low-complexity decoupled Toeplitz matrices can be obtained to estimate the DOAs. We further exploit the structural information hidden in the newly constructed data to avoid pair matching for the azimuth and elevation angles when the number of sensors is odd, and then propose a fast and feasible decoupled alternating projections (D-AP) algorithm, reducing computational complexity to a great extent. Numerical simulations are performed to demonstrate that the proposed algorithm is no longer restricted by angle ambiguity scenarios, but instead provides a more stable estimation performance, even when multiple signals share the same angles in both azimuth and elevation dimensions. Additionally, it greatly improves the resolution, with control of the computation load compared with the existing atomic norm minimization (ANM) algorithm. Full article

In this paper, we propose a secure transmission framework for near-field MIMO-NOMA systems. This architecture integrates beamforming mechanisms for both transmission and reception, allowing the base station to send encrypted information to authorized users, effectively countering eavesdropping attempts in a near-field environment. To optimize the secrecy communication capability in the near field, a two-phase alternating optimization algorithm is introduced. In the first phase, the semidefinite relaxation (SDR) method is used to relax constraints in the problem and convert it into a semidefinite programming (SDP) problem. In the second phase, the successive convex approximation (SCA) algorithm is employed to transform the original non-convex problem into a convex optimization problem, obtaining a locally optimal solution through multiple iterations. Simulation results validate that the proposed near-field communication strategy exhibits superior secrecy communication capabilities under various parameter settings compared to far-field communication strategies. Full article

This paper investigates joint beamforming in a secure integrated sensing and communications (ISAC) system assisted by reconfigurable intelligent surfaces (RIS). The system communicates with legitimate downlink users, detecting a potential target, which is a potential eavesdropper attempting to intercept the downlink communication information from the base station (BS) to legitimate users. To enhance the physical-layer secrecy of the system, we design and introduce interference signals at the BS to disrupt eavesdroppers’ attempts to intercept legitimate communication information. The BS simultaneously transmits communication and interference signals, both utilized for communication and sensing to guarantee the sensing and communication quality. By jointly optimizing the BS active beamformer and the RIS passive beamforming matrix, we aim to maximize the achievable secrecy rate and radiation power of the system. We develop an effective scheme to find the active beamforming matrix through fractional programming (FP) and semi-definite programming (SDP) techniques and obtain the RIS phase shift matrix via a local search technique. Simulation results validate the effectiveness of the proposed methods in enhancing communication and sensing performance. Additionally, the results demonstrate the effectiveness of introducing the interference signals and RIS in enhancing the physical-layer secrecy of the ISAC system. Full article

The model-based polarimetric synthetic aperture radar (PolSAR) target decomposition decodes the scattering mechanism of the target by analyzing the essential scattering components. This paper presents a new general three-component scattering power decomposition method by establishing optimization problems. It is known that the existing three-component decomposition method prioritizes the contribution of volume scattering, which often leads to volume scattering energy overestimation and may make double-bounce scattering and odd-bounce scattering component power negative. In this paper, a full parameter optimization method based on the remainder matrix is proposed, where all the elements of the coherency matrix will be taken into account including the remaining T13 component. The optimization is achieved with no priority order by solving the problem using semi-definite programming (SDP) based on the Schur complement theory. By doing so, the problem of volume scattering energy overestimation and negative powers will be avoided. The performance of the proposed approach is demonstrated and evaluated with AIRSAR and GF-3 PolSAR data sets. The experimental results show that by using the proposed method, the power contributions of volume scattering in two sets of data were reduced by at least 2.6% and 3.7% respectively, compared to traditional methods. And the appearance of negative power of double-bounce scattering and odd-bounce scattering are also avoided compared with those of the existing three-component decomposition. Full article

Symmetric extensions are essential in quantum mechanics, providing a lens through which to investigate the correlations of entangled quantum systems and to address challenges like the quantum marginal problem. Though semi-definite programming (SDP) is a recognized method for handling symmetric extensions, it struggles with computational constraints, especially due to the large real parameters in generalized qudit systems. In this study, we introduce an approach that adeptly leverages permutation symmetry. By fine-tuning the SDP problem for detecting k-symmetric extensions, our method markedly diminishes the searching space dimensionality and trims the number of parameters essential for positive-definiteness tests. This leads to an algorithmic enhancement, reducing the complexity from $O(d^{2k})$ to $O(k^{d^2})$ in the qudit k-symmetric extension scenario. Additionally, our approach streamlines the process of verifying the positive definiteness of the results. These advancements pave the way for deeper insights into quantum correlations, highlighting potential avenues for refined research and innovations in quantum information theory. Full article

This paper develops a collaborative trajectory planning and resource allocation (CTPRA) strategy for multi-target tracking (MTT) in a spectral coexistence environment utilizing airborne radar networks. The key mechanism of the proposed strategy is to jointly design the flight trajectory and optimize the radar assignment, transmit power, dwell time, and signal effective bandwidth allocation of multiple airborne radars, aiming to enhance the MTT performance under the constraints of the tolerable threshold of interference energy, platform kinematic limitations, and given illumination resource budgets. The closed-form expression for the Bayesian Cramér–Rao lower bound (BCRLB) under the consideration of spectral coexistence is calculated and adopted as the optimization criterion of the CTPRA strategy. It is shown that the formulated CTPRA problem is a mixed-integer programming, non-linear, non-convex optimization model owing to its highly coupled Boolean and continuous parameters. By incorporating semi-definite programming (SDP), particle swarm optimization (PSO), and the cyclic minimization technique, an iterative four-stage solution methodology is proposed to tackle the formulated optimization problem efficiently. The numerical results validate the effectiveness and the MTT performance improvement of the proposed CTPRA strategy in comparison with other benchmarks. Full article

This paper investigates threshold-constrained joint waveform optimization for an integrated sensing and communication (ISAC) system. Unlike existing studies, we employ mutual information (MI) and sum rate (SR) as sensing and communication metrics, respectively, and optimize the waveform under constraints to both metrics simultaneously. This provides significant flexibility in meeting system performance. We formulate three different optimization problems that constrain the radar performance only, the communication performance only, and the ISAC performance, respectively. New techniques are developed to solve the original problems, which are NP-hard and cannot be directly solved by conventional semi-definite programming (SDP) techniques. Novel gradient descent methods are developed to solve the first two problems. For the third non-convex optimization problem, we transform it into a convex problem and solve it via convex toolboxes. We also disclose the connections between three optimizations using numerical results. Finally, simulation results are provided and validate the proposed optimization solutions. Full article

Optimization approaches that determine sensitive sensor nodes in a large-scale, linear time-invariant, and discrete-time dynamical system are examined under the assumption of independent and identically distributed measurement noise. This study offers two novel selection algorithms, namely an approximate convex relaxation method with the Newton method and a gradient greedy method, and confirms the performance of the selection methods, including a convex relaxation method with semidefinite programming (SDP) and a pure greedy optimization method proposed in the previous studies. The matrix determinant of the observability Gramian was employed for the evaluations of the sensor subsets, while its gradient and Hessian were derived for the proposed methods. In the demonstration using numerical and real-world examples, the proposed approximate greedy method showed superiority in the run time when the sensor numbers were roughly the same as the dimensions of the latent system. The relaxation method with SDP is confirmed to be the most reasonable approach for a system with randomly generated matrices of higher dimensions. However, the degradation of the optimization results was also confirmed in the case of real-world datasets, while the pure greedy selection obtained the most stable optimization results. Full article

A spherical antenna array (SAA) is an array-designed arrangement capable of scanning in almost all the radiation sphere with constant directivity. It finds recent applications in aerospace, spacecraft, vehicular and satellite communications. Therefore, estimation of the direction-of-arrival (DoA) of electromagnetic (EM) waves that impinge on an SAA with unknown mutual coupling called for research attention. This paper proposed a spherical harmonic atomic norm minimization (SHANM) for DoA estimation using an SAA configuration. The gridless sparse signal recovery problem is considered in the spherical harmonic (SH) domain in conjunction with the atomic norm minimization (ANM). Because of the unavailability of the Vandermonde structure in the SH domain, the theorem of Vandermonde decomposition that is the mathematical basis of the traditional ANM methods finds no application in SH. Addressing this challenge, a low-dimensional semidefinite programming (SDP) approach implementing the SHANM method is developed. This approach is independent of Vandermonde decomposition, and directly recovers the atomic decomposition in SH. The numerical experimental results show the superior performance of the proposed method against the previous methods. In addition, accounting for the impacts of mutual coupling, an experimental measured data, which is the generally accepted ground of testing any method, is employed to illustrate the efficacy and robustness of the proposed methods. Finally, for achieving DoA estimation with sufficient localization accuracy using a SAA, the proposed SHANM-based method is a better option. Full article