The success of multi-sensor data fusion requires an important step called sensor registration, which involves estimating sensor biases from sensors' asynchronous measurements. There are two difficulties in the bias estimation problem: one is the unknown target states which serve as the nuisance variables in the estimation problem, the other is the highly nonlinear coordinate transformation between sensors' local and common coordinate frames. In this work, we focus on the 3-dimensional scenario and propose a new nonlinear least squares (LS) formulation which avoids estimating target states. The proposed LS formulation eliminates the target states by exploiting the nearly-constant velocity property of the target motion. To address the intrinsic nonlinearity, we propose a block coordinate descent (BCD) scheme for solving the formulation which alternately updates various bias estimates. Specifically, semidefinite relaxation technique is introduced to handle the nonlinearity brought by angle biases. Furthermore, two BCD algorithms with different block picking rules are proposed. Finally, the effectiveness and the efficiency of the proposed BCD algorithms are demonstrated in the numerical simulation section.
|Original language||English (US)|
|Title of host publication||2018 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2018 - Proceedings|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||5|
|State||Published - Feb 20 2019|
|Event||2018 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2018 - Anaheim, United States|
Duration: Nov 26 2018 → Nov 29 2018
|Name||2018 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2018 - Proceedings|
|Conference||2018 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2018|
|Period||11/26/18 → 11/29/18|
Bibliographical noteFunding Information:
This work is supported by the National Natural Science Foundation of China (No. 61731018, No. 61571384), the Leading Talentsof Guang Dong Province Program (No. 00201501), and the Development and Reform Commission of Shenzhen Municipality.
© 2018 IEEE.
- Block coordinate descent algorithm
- Nonlinear least squares
- Semidefinite relaxation
- Sensor registration problem