Abstract:Clustering Algorithm is a kind of key technique used to reduce energy consumption, which can increase network scalability and lifetime. The main goal of this research is concerning protocols to contain in-motion sensor nodes in large-scale topologies, and constitute a fleet wireless sensor network which can assistant in-flight localization and alignment for carrier-based aircraft. In this paper, we abstract network model according to specific conditions of F-WSN, and select performance indicators to evaluate it. A distributed multiple-weight data gathering and aggregation protocol (DMDG) is proposed, which contains all-sided active clustering scheme and realizes long-range realtime communication by tacical data link under a TDMA/CSMA channel sharing mechanism. An analytical paradigm facilitating the most appropriate choice of the next relay is proposed. Experimental results have shown that DMDG scheme can balance the energy consumption and extend the network lifetime notably, outperform LEACH, PEACH, and DEEC in terms of network lifetime and coverage rate, especially in sparse node density or anisotropic topologies.
In this paper, the navigational state estimation problem is investigated for a class of networked spatial-navigation systems with quantization effects, mixed time-delays, and network-based observations (i.e. complementary measurements and regional estimations). A decentralized moving horizon estimation approach, featuring complementary reorganization and recursive procedure, is proposed to tackle this problem. First, through the proposed reorganized scheme, a random delayed system with complementary observations is reconstructed into an equivalent delay-free one without dimensional augment. Second, with this equivalent system, a robust moving horizon estimation scheme is presented as a uniform estimator for the navigational states. Third, for the demand of real-time estimate, the recursive form of decentralized moving horizon estimation approach is developed. Furthermore, a collective estimation is obtained through the weighted fusion of two parts, i.e. complementary measurements based estimation, and regional estimations directly from the neighbors. The convergence properties of the proposed estimator are also studied. The obtained stability condition implicitly establishes a relation between the upper bound of the estimation error and two parameters, i.e. quantization density and delay occur probability. Finally, an application example to networked unmanned aerial vehicles is presented and comparative simulations demonstrate the main features of the proposed method.
Abstract:The necessity recurrently comes to align a strapdown inertial navigation system (SINS) in a moving vehicle to guarantee the accuracy and efficiency in the long run-off of the inertial system after a take-off or launch command is issued. This in-flight alignment is therefore achieved by integrating SINS data with some external aiding source inlcluding airborne navigation equipments and networking sensors. In this paper, a localization architecture and alignment scheme is presented for aircraft in a three-demensional fleet network, which is based on wireless sensor network. Firstly, a 3D node localization scheme is designed based on weighed-multidimensional scaling, which adopt spherical locating in the initial stage, and adaptively choose source nodes with high relative reliability to achieve position update. Then a robust filter algorithm is applied to compensate time-varying delay error and large azimuth uncertainty in alignment. Extensive simulation shows that the DMDG-3D localization scheme can provide highly accurate and relatively reliable navigation information in real time, and filter algorithm can accelerate convergence and give better estimation of the navigation parameters.
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