Shengming Chang
Ningbo University
17 Papers
5 Citations
Shengming Chang is an academic researcher from Ningbo University. The author has contributed to research in topics: Estimator & Computer science. The author has an hindex of 6, co-authored 11 publications. Previous affiliations of Shengming Chang include Ningbo University of Technology.
Chat about Author
Papers
Target Localization in Underwater Acoustic Sensor Networks Using RSS Measurements
TL;DR: The problems based on the maximum likelihood (ML) criterion for estimating target localization in cases of both known and unknown transmit power are respectively derived, and fast implementation algorithms are proposed by transforming the non-convex problems into a generalized trust region subproblem (GTRS) frameworks.
48
A Novel Weighted Localization Method in Wireless Sensor Networks Based on Hybrid RSS/AoA Measurements
TL;DR: In this paper, a hybrid RSS/AOA indoor localization method based on error variance and measurement noise weighted least squares (ENWLS) is proposed, which achieves high-precision indoor positioning without increasing its complexity.
32
RSS-Based Cooperative Localization in Wireless Sensor Networks via Second-Order Cone Relaxation
TL;DR: This paper treats the transmit power as a constant and derive a novel non-convex weighted least squares estimator which can be transformed into a second-order cone programming (SOCP) problem for reaching an efficient solution.
30
An Effective Scheduling Algorithm for Coverage Control in Underwater Acoustic Sensor Network.
TL;DR: A coverage-control strategy (referred to as ESACC) that establishes a sleep–wake scheduling mechanism based on the redundancy of deployment nodes that reduces the network energy consumption and takes into account the monitoring coverage of the network.
30
RSS-Based Target Localization in Underwater Acoustic Sensor Networks via Convex Relaxation.
TL;DR: The received signal strength (RSS) based target localization problem in underwater acoustic wireless sensor networks (UWSNs) is considered, and a novel weighted least squares (WLS) estimator is derived and an iterative ML and mixed SD/SOCP algorithm is presented for solving the derived WLS problem.