Cooperative motion parameter estimation using RSS measurements in robotic sensor networks. (15th June 2019)

• Record Type: Journal Article
• Authors: Wu, Xiaoping; Zhu, Xuefen; Wang, Shenghui; Mo, Lufeng
• Is Part Of: Journal of network and computer applications. Volume 136(2019)
• Page Start: 57
• Page End: 70
• Publication Date: 2019-06-15
• Subjects: Robotic sensor network -- Semidefinite programming -- Mobile target -- Motion parameter estimation; Microcomputers -- Periodicals; Computer networks -- Periodicals; Application software -- Periodicals; Micro-ordinateurs -- Périodiques; Réseaux d'ordinateurs -- Périodiques; Logiciels d'application -- Périodiques; Application software; Computer networks; Microcomputers; Periodicals; 004.05; 004
• DOI: 10.1016/j.jnca.2019.04.007
• Languages: English
• ISSNs: 1084-8045

Abstract

Abstract: When multiple mobile targets start from initial positions at constant velocities in a sensor network, a cooperative motion parameter estimation method is proposed by using the received signal strength (RSS) measurements. Firstly, the nonconvex optimization model is built to estimate the initial positions and velocities of the mobile targets. Then the unconstrained semidefinite programming (USDP) is designed to estimate the positions of the mobile targets by converting the nonconvex optimization model into the convex problem, when the transmit powers of the mobile targets are assumed to be known. To reduce the estimation error, the constrained semidefinite programming (CSDP) is also proposed by exploiting the constraint conditions of motion equation. A new convex semidefinite programming (SDP) with known transmit powers (SDP-KTP) is proposed to directly estimate the initial positions and the velocities of the mobile targets. The USDP, CSDP and SDP algorithms are extended to the situation of unknown transmit power. The simulations and real experiments show that the SDP algorithm provides better performance than the USDP or the CSDP by availing of the movement equation. The proposed USDP, CSDP and SDP algorithms perform better with the increasing of samples, but the computational complexity of the proposed algorithms is higher.