Single-observer DOA/TDOA Registration and Passive Localization Based on Constrained Total Least Squares

Yan ZUO; Zhimeng CHEN; Liping CAI

doi:10.11999/JEIT180655

Volume 41 Issue 6

Jun. 2019

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Article Navigation > Journal of Electronics & Information Technology > 2019 > 41(6): 1317-1323

Yan ZUO, Zhimeng CHEN, Liping CAI. Single-observer DOA/TDOA Registration and Passive Localization Based on Constrained Total Least Squares[J]. Journal of Electronics & Information Technology, 2019, 41(6): 1317-1323. doi: 10.11999/JEIT180655

Citation:

Yan ZUO, Zhimeng CHEN, Liping CAI. Single-observer DOA/TDOA Registration and Passive Localization Based on Constrained Total Least Squares[J]. Journal of Electronics & Information Technology, 2019, 41(6): 1317-1323. doi: 10.11999/JEIT180655

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Single-observer DOA/TDOA Registration and Passive Localization Based on Constrained Total Least Squares

doi: 10.11999/JEIT180655

School of Automation, Hangzhou Dianzi University, Hangzhou 310018, China

Funds: The National Natural Science Foundation of China (61673146), The Zhejiang Provincial Natural Science Foundation (LY16F030009)

Received Date: 2018-07-04
Rev Recd Date: 2018-12-25

Available Online: 2019-01-18

Publish Date: 2019-06-01

Abstract

Abstract

The system biases degrade seriously the location precision for the multi-static passive radar system. A joint registration and passive localization algorithm based on Constrained Total Least Squares (CTLS) using Direction Of Arrival (DOA) and Time Difference Of Arrival (TDOA) measurements is developed to address the multi-static radar localization problem under the influence of system biases. Firstly, the nonlinear DOA and TDOA measurement equations are linearized by introducing auxiliary variables. Considering the statistical correlation properties of the noise matrix in the pseudo-linear equations, a joint biases registration and passive localization problem is formulated as a CTLS problem and the Newton’s method is applied to solving the CTLS problem. Moreover, a dependent least squares algorithm is designed to improve the target position estimation using the relationship between auxiliary variables and target position. An iterative post-estimate procedure is exploited to enhance further the estimation accuracy of the system biases. Finally, the theoretical error of the proposed algorithm is derived. Simulations demonstrate that the proposed algorithm can effectively estimate the system biases and target position.
- Passive localization,
- Registration,
- Multi-static passive radar,
- DOA and TDOA,
- Constrained Total Least Squares (CTLS)

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References(16)

References

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