Yingquan Li

Localization in wireless networks is traditionally performed through centralized architectures that process measurements at a single node. However, such architectures not only lead to a high computational burden but also suffer from limited robustness, as the failure of the central node terminates the localization operation. They also require extremely high communication bandwidth, as all nodes must transmit their measurements to a central server. Moreover, assuming prior knowledge of each target node's transmit power is impractical, as it varies with battery level, hardware imperfections, and antenna orientation. To address these issues, this paper proposes a Distributed Cooperative Localization technique (DCLU), which jointly estimates the location and transmit power of the nodes. The joint maximum-likelihood formulation is non-convex and discontinuous, and cannot be solved using off-the-shelf optimization techniques. We address this challenge by constructing a convex approximation of the ML objective, which enables efficient distributed estimation using a coordinate descent algorithm. We further establish theoretical guarantees for convergence, including the rate of convergence. Compared with centralized approaches, DCLU offers enhanced scalability and robustness. We also derive the Cramer-Rao lower bound (CRLB) as a performance benchmark. Extensive simulations and on-field experiments demonstrate that DCLU achieves higher localization accuracy while significantly reducing computational complexity relative to existing techniques.

Ultraviolet (UV) localization has attracted significant attention for its ability to operate without line-of-sight. Existing UV localization techniques usually require anchor nodes to be placed in specific geometric configurations, including linear alignment or co-location. However, this requirement is often impractical in real-world scenarios, where obstacles such as walls and equipment prevent anchor nodes from being placed in ideal positions. In this paper, we consider general scenarios without constraints on anchor deployment and propose a UV localization technique, referred to as EIUL (Evolutionary and Invex approximation-based Ultraviolet Localization), to address non-line-of-sight scenarios. We adopt the single-scattering model and the geometric relationship between the transmitting beam and the receiver’s field of view to develop a least-squares-based estimator for jointly estimating the target node’s location, along with the transmitting beam’s azimuth angle and elevation angle. We first apply differential evolution to obtain an initial estimate, which is then used to facilitate invex approximation, thereby formulating an invexified objective function to further refine the location estimate. We derive the Lipschitz constant for the gradient of the invexified objective function and employ the Nesterov’s accelerated gradient descent to achieve the global minimum. Additionally, we present a concise form of the Cramer-Rao lower bound for UV localization based on the single-scattering model, which is straightforward to compute numerically. Extensive simulations demonstrate that EIUL provides more accurate parameter estimation with lower computational complexity compared to existing techniques.

Underwater wireless sensor networks (UWSNs) have received significant attention due to their various applications, with underwater target localization playing a vital role in enhancing network performance. Given the challenges and high costs associated with UWSN deployments, Received Signal Strength (RSS)-based localization offers a viable solution due to its minimal hardware requirements and cost-effectiveness. In this paper, we assign distance-based weights to RSS measurements, providing higher reliability to closer anchor nodes. Using the weighted RSS measurements and generalized trust region subproblem (GTRS), we propose the GTRS-based localization technique with Unknown Transmit Power (GUTP), which can be solved by a simple bisection method. Unlike conventional localization methods that require prior knowledge of the target node's transmit power, GUTP jointly estimates both the location and transmit power of the target node, broadening its practical use. Additionally, we derive the Cramer-Rao lower bounds (CRLBs) for RSS-based underwater localization with known and unknown transmit power, respectively. Extensive simulations demonstrate that GUTP achieves enhanced accuracy and significantly lower computational complexity in estimating the target node's location and transmit power compared to existing semidefinite programming (SDP)-based techniques.

Received signal strength (RSS)-based localization techniques rely on the transmit power of nodes, with many existing approaches assuming full knowledge of this parameter. However, transmit power depends on various factors like battery levels, antenna orientation, and component aging. To address this issue, existing techniques typically employ semidefinite programming (SDP), which exhibits significantly high computational complexity. In this paper, we present a cooperative RSS-based localization technique named CR-CA (Convex Relaxation with Centralized Armijo optimization), which jointly estimates nodes’ locations and transmit power. CR-CA transforms the unconstrained maximum likelihood (ML) of the RSS-based localization problem into a constrained optimization problem using a convex approximation of the non-convex and discontinuous objective function. We demonstrate that the relaxed ML objective function possesses a Lipschitz continuous gradient. We solve the relaxed ML problem using the augmented Lagrange multiplier method and provide the theoretical proof of its convergence. Additionally, we derive the Cramer-Rao Lower Bound (CRLB) for RSS-based cooperative localization under scenarios where transmit power is unknown. We conduct extensive simulations and real-world experiments to verify the effectiveness of CR-CA, showcasing its superior accuracy in estimating nodes’ locations and transmit power. Simulations and experiments further validate that CR-CA exhibits linear computational complexity with the number of wireless links, thus making it suitable for large-scale networks.

Received Signal Strength (RSS)-based localization can be highly effective when the target node’s transmit power is known. However, in practical scenarios, factors including antenna orientation and battery level may introduce variability, making accurate power estimation a critical challenge. This paper addresses localization with unknown transmit power using RSS difference (RSSD) measurements. A framework is proposed to first derive an initial location estimate via linear least squares (LLS) and then refine it by solving a weighted least squares (WLS) problem using semidefinite programming (SDP). Unlike existing SDP-based techniques, our technique meets a rank-one constraint to prevent accuracy loss. A compact expression for the Cramér-Rao lower bound (CRLB) in RSSD-based localization is derived. To validate our approach, extensive experiments using 13 nodes deployed in a 164×77 m2 outdoor arena are conducted. Experimental results and numerical simulations demonstrate that the proposed technique outperforms existing methods with respect to localization accuracy and computational efficiency.

Signal strength-based cooperative localization has gained significant interest due to its low complexity and cost-effectiveness. The conventional techniques often use Taylor expansion to address the non-convex, non-linear, and discontinuous characteristics of the maximum likelihood (ML) objective function. However, this results in a considerable residual error that exponentially grows with noise. To address this issue, we propose a cooperative (and non-cooperative) localization technique using received signal strength (RSS) measurements, named C-UA (and NC-UA), which employs a relative error-based estimator. We then use weighted non-linear least squares (NLS) to formulate a semidefinite programming (SDP) problem. C-UA and NC-CA can jointly estimate the location and transmit power of target nodes while considering the uncertainty in anchor nodes' locations. We also derive the Cramer-Rao lower bound (CRLB) involving unknown transmit power and anchor location uncertainty. We perform extensive outdoor real-world tests in an open field (640 m × 180 m), using 50 Bluetooth transceivers to collect RSS measurements. We record the location of each node using two devices: a real-time kinematic (RTK)-GPS system, providing centimeter-level accuracy, and a standard GPS device, offering meter-level accuracy. The RTK-GPS measurements are considered the true positions, while the standard GPS measurements, with lower precision, are used to represent the uncertainty in the locations. Through extensive simulations and experimentation, we demonstrate the superior performance of C-UA and NC-CA over existing localization techniques.

In this article, we present the limitations of traditional localization techniques, such as those using Global Positioning Systems (GPS) and life detectors, in localizing victims during disaster rescue efforts. These techniques usually fall short in accuracy, coverage, and robustness to environmental interference. We then discuss the necessary requirements for developing GPS-independent localization techniques in disaster scenarios. Practical techniques should be passive, with straightforward hardware, low computational demands, low power, and high accuracy, while incorporating unknown environmental information. We review various implementation strategies for these techniques, categorized by measurements (time, angle, and signal strength) and operation manners (non-cooperative and cooperative). Case studies demonstrate trade-offs between localization accuracy and complexity, emphasizing the importance of choosing appropriate localization techniques based on resources and rescue needs for efficient disaster response.