A Localization and In-flight Alignment Protocol for Airborne SINS Based on Flight-vehicle Wireless Sensor Networks

Abstract

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.

Decentralized state estimation for networked spatial-navigation systems with mixed time-delays and quantized complementary measurements: The moving horizon case

“…It is obtained from Figure 6(b) that the number of ONs at each step is approximately more than seven, which imply the sufficiency of CMs in the geometric localization of 3D space. 31,32 Figure 6 is obviously smaller than that of C-DMHE, due to that N-DMHE mitigates the affect of the TDs through complementary reorganization. RDLE and N-DMHE apply the similar reorganization scheme, thus both acquire the smaller convergence errors than that of C-DMHE; however, N-DMHE outperforms RDLE on a more robust convergence process, which is due to the usage of the observations in a finite-time interval instead of that of one step in RDLE, but at the price of more expensive computational load.…”

“…More specifically where scriptK denotes the weighted matrix, g(y~τ¯ [i](s),x~τ¯ [i](s)) is the relative measure function. Several typical deductions of scriptK and g(·) can be found in Davey et al., 27 Goh et al., 31 and Gao et al., 32 and is omitted here due to the limitation of length. Thus, the local measurements y[ƛ](s)∈ℜ ny in equation (25) can be rewritten in linear space scriptL{Yt-N-τ¯|t t-N-1(τ…”

“…three for 2D space, and four for 3D space). 31,32 Thus, through complementary reorganization, the multi-step delay in system (13) with high dimension is transformed into the data missing in equation (26) with low dimension.…”

“…In each simulation experiment, the networked UAVs broadcast cooperative packet based on DDC-MAC 28 and FMCQR. 1 The initial topology is shown in Figure 3(b), wherein the nodes are clustered into six groups based on DMDG, 32 i.e. Sink1 and Normali| i=1,2,…,5.…”

“…It is obtained from Figure 6(b) that the number of ONs at each step is approximately more than seven, which imply the sufficiency of CMs in the geometric localization of 3D space. 31,32 Figure 6(c) and (d) reports the estimation errors of RDLE, N-DMHE, and C-DMHE on location and velocity, respectively. The estimation error of the N-DMHE is obviously smaller than that of C-DMHE, due to that N-DMHE mitigates the affect of the TDs through complementary reorganization.…”