A Direct Position Determination Algorithm Based on Multi Arrays in the Presence of Unknown Nonuniform Noise[J]. 2015, 49(10): 136-142.
DOI:
A Direct Position Determination Algorithm Based on Multi Arrays in the Presence of Unknown Nonuniform Noise[J]. 2015, 49(10): 136-142.DOI: 10.7652/xjtuxb201510022.
A Direct Position Determination Algorithm Based on Multi Arrays in the Presence of Unknown Nonuniform Noise
UN-ML-DPD算法能够提高多阵列在低信噪比下的定位精度。仿真结果表明:UN-ML-DPD算法在-15 dB的低信噪比下估计误差小于15 km
与DPD算法相比定位精度提高15%以上; 能较为准确地估计各阵列噪声协方差矩阵
在标准噪声功率小于30 W时估计误差小于3.5 W; 在高信噪比下定位精度能够逼近克拉美罗下界。
Abstract
A multi-array based maximum likelihood direct position determination(DPD)algorithm in the presence of unknown nonuniform noise(UN-ML-DPD)is proposed and an expression of Cramér-Rao lower bound(CRLB)is derived to solve the problem that the position accuracy of the DPD algorithm decreases due to dispersed multi-array distribution and nonuniform noise power distribution caused by difference among array elements in passive localization systems. The covariance matrixes of the array data are calculated and the results are transmitted to the process center. Then
the power of the nonuniform noise and the position of the targets are co-estimated simultaneously using the maximum likelihood method with iteration
and the converged result is taken as the target position estimation
so that the effect of the nonuniform noise is reduced. The alternative projection method is utilized to lower the algorithm complexity when multi targets exist. Compared with the traditional method
the UN-ML-DPD algorithm can improve the position accuracy under low signal-to-noise ratio(SNR). Simulation results show that the estimation error is lower than 15 km for SNR is -15 dB
and the accuracy of the position estimation is increased by 15% compared with the DPD method. The noise covariance matrix is well estimated
and the estimation error is less than 3.5 W when the standard power of the noise is lower than 30 W. Moreover
the position accuracy approaches the CRLB when the SNR are high.
WANG Ding, ZHANG Li, WU Ying. The structured total least squares algorithm research for passive location based on angle information [J]. Science in China: Series F Information Science, 2009, 39(6): 663-672.
WANG Yunlong, WU Ying. An improved direct position determination algorithm with combined time delay and Doppler [J]. Jounal of Xi'an Jiaotong University, 2015, 49(4): 123-129.
WEISS A J. Direct position determination of narrowband radio frequency transmitters [J]. IEEE Signal Processing Letters, 2004, 11(5): 513-516.
AMAR A, WEISS A J. Advances in direct position determination [C]∥ Proceedings of 2004 Sensor Array and Multichannel Signal Processing Workshop. Los Alamitos, CA, USA: IEEE Computer Society, 2004: 584-588.
WEISS A J, AMAR A. Direct position determination of multiple radio signals [J]. EURASIP Journal on Applied Signal Processing, 2005(1): 37-49.
AMAR A, WEISS A J. Direct position determination in the presence of model errors: known waveforms [J]. Digital Signal Processing, 2006, 16: 52-83.
DEMISSIE B, OISPUU M, RUTHOTTO E. Localization of multiple sources with a moving array using subspace data fusion [C]∥ 11th International Conference on Information Fusion. Piscataway, NJ, USA: IEEE, 2008: 1-7.
OISPUU M, NICKEL U. Direct detection and position determination of multiple sources with intermittent emission [J]. Signal Processing, 2010, 90: 3056-3064.
ZHANG Min, GUO Fucheng, ZHOU Yiyu. A single moving observer direct position determination method using a long baseline interferometer [J]. Acta Aeronautica et Astronautica Sinica, 2013, 34(2): 378-386.
PESAVENTO M, GERSHMAN A B. Maximum-likelihood direction-of-arrival estimation in the presence of unknown nonuniform noise [J]. IEEE Transactions on Signal Processing, 2001, 49(7): 1310-1324.