传感器网络定位的全局解：一种非凸势博弈方法及其分布式实现

A Potential Reduction Method for Canonical Duality, with an Application to the Sensor Network Localization Problem

We propose to solve large instances of the non-convex optimization problems reformulated with canonical duality theory. To this aim we propose an interior point potential reduction algorithm based on the solution of the primal-dual total complementarity (Lagrange) function. We establish the global convergence result for the algorithm under mild assumptions and demonstrate the method on instances of the Sensor Network Localization problem. Our numerical results are promising and show the possibility of devising efficient interior points methods for non-convex duality.

用于目标定位的隐蔽最优距离传感器部署

Stealthy Optimal Range-Sensor Placement for Target Localization

针对需平衡目标定位能力与传感器隐蔽性的开放挑战问题，研究了隐蔽距离传感器部署，提供了二维场景下任意数量传感器定位两个目标的解析解。

On Convexification of Range Measurement Based Sensor and Source Localization Problems

This manuscript is a preliminary pre-print version of a journal submission by the authors, revisiting the problem of range measurement based localization of a signal source or a sensor. The major geometric difficulty of the problem comes from the non-convex structure of optimization tasks associated with range measurements, noting that the set of source locations corresponding to a certain distance measurement by a fixed point sensor is non-convex both in two and three dimensions. Differently from various recent approaches to this localization problem, all starting with a non-convex geometric …

A Less Noise-Sensitive SDP Relaxation in Wireless Sensor Network Localization

There are variety of methods to solve the localization problem and among them semi-definite programming based methods have shown great performance in both complexity and accuracy aspects. In this paper, we introduce a class of less noise-sensitive relaxation to reduce the complexity of SDP-based methods. We apply our relaxation to Edge-based Semi-Definite Programming (ESDP) method and the resulted model is called PESDP. Simulation results confirm that our proposed PESDP method is less noise-sensitive and faster compared to the original ESDP.

入侵检测中联合传感器放置与调度的统一框架

A Unified Framework for Joint Sensor Placement and Scheduling for Intrusion Detection

提出一个统一框架，将传感器放置与方向调度联合优化，通过博弈论设计效用函数并利用弱子模性实现近最优检测性能。

Distributed Localization of Tree-structured Scattered Sensor Networks

Many of the distributed localization algorithms are based on relaxed optimization formulations of the localization problem. These algorithms commonly rely on first-order optimization methods, and hence may require many iterations or communications among computational agents. Furthermore, some of these distributed algorithms put a considerable computational demand on the agents. In this paper, we show that for tree-structured scattered sensor networks, which are networks that their inter-sensor range measurement graphs have few edges (few range measurements among sensors) and can be represented…

On Sensor Network Localization Using SDP Relaxation

A Semidefinite Programming (SDP) relaxation is an effective computational method to solve a Sensor Network Localization problem, which attempts to determine the locations of a group of sensors given the distances between some of them [11]. In this paper, we analyze and determine new sufficient conditions and formulations that guarantee that the SDP relaxation is exact, i.e., gives the correct solution. These conditions can be useful for designing sensor networks and managing connectivities in practice. Our main contribution is twofold: We present the first non-asymptotic bound on the connectiv…

二维和三维中目标定位与跟踪的传感器最优布置

Optimal Sensor Placement for Target Localization and Tracking in 2D and 3D

本文通过统一框架和框架理论，分析了二维和三维中仅方位、仅距离和接收信号强度三种传感器的最优布置问题，给出了最优布置的充要条件及其解析性质，并提出了梯度控制律以数值构造最优布置。

Simple and fast convex relaxation method for cooperative localization in sensor networks using range measurements

We address the sensor network localization problem given noisy range measurements between pairs of nodes. We approach the non-convex maximum-likelihood formulation via a known simple convex relaxation. We exploit its favorable optimization properties to the full to obtain an approach that: is completely distributed, has a simple implementation at each node, and capitalizes on an optimal gradient method to attain fast convergence. We offer a parallel but also an asynchronous flavor, both with theoretical convergence guarantees and iteration complexity analysis. Experimental results establish le…

传感器网络中相对定位随机化算法的几乎必然收敛性

Almost sure convergence of a randomized algorithm for relative localization in sensor networks

针对传感器网络相对定位问题，提出一种基于输入驱动共识动力学和成对gossip通信更新的随机化算法，证明状态时间平均几乎必然收敛到最小二乘解，且无需公共时钟，实现完全分布式异步计算。

DCOOL-NET: Distributed cooperative localization for sensor networks

We present DCOOL-NET, a scalable distributed in-network algorithm for sensor network localization based on noisy range measurements. DCOOL-NET operates by parallel, collaborative message passing between single-hop neighbor sensors, and involves simple computations at each node. It stems from an application of the majorization-minimization (MM) framework to the nonconvex optimization problem at hand, and capitalizes on a novel convex majorizer. The proposed majorizer is endowed with several desirable properties and represents a key contribution of this work. It is a more accurate match to the u…

A dynamic state transition algorithm with application to sensor network localization

The sensor network localization (SNL) problem is to reconstruct the positions of all the sensors in a network with the given distance between pairs of sensors and within the radio range between them. It is proved that the computational complexity of the SNL problem is NP-hard, and semi-definite programming or second-order cone programming relaxation methods are only able to solve some special problems of this kind. In this study, a stochastic global optimization method called the state transition algorithm is introduced to solve the SNL problem without additional assumptions and conditions of …

基于背压的均场类型博弈用于多跳无线传感器网络中的调度

Backpressure-based Mean-field Type Game for Scheduling in Multi-Hop Wireless Sensor Networks

本文提出基于背压的均场类型博弈框架，用于优化多跳无线传感器网络中的调度，通过结合均场项和背压动态，实现局部与全局效率的平衡。

传感器网络中同时自定位与跟踪的分布式最大似然方法

Distributed Maximum Likelihood for Simultaneous Self-localization and Tracking in Sensor Networks

将传感器自定位问题转化为隐马尔可夫模型的静态参数估计问题，并实现递归最大似然和在线期望最大化算法的全分布式版本，以同时进行传感器网络定位与目标跟踪。