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基于量子理论获取相位参数的导航机制, 理论上可以突破经典物理极限对导航精度的限制. 利用量子零拍探测对相干态光场相位进行测量时, 通常需要相位与之正交的本振光才能使测量精度达到量子标准极限. 由于导航信号相位的高非线性特点, 想要利用传统的线性锁相环获取完全满足条件的本振光具有一定的难度. 为此, 本文设计了一种基于容积准则的非线性锁相环, 实现了在非正交本振光的条件下对相干态相位进行精确测量的功能. 首先, 利用相干态的Wigner函数推导了其相位在量子零拍探测的输出结果, 设计了量子相位估计的非线性数字锁相环框架. 然后基于正交单纯形容积准则设计了非线性滤波算法实现锁相环功能, 该锁相环通过对本振相位进行多次状态更新, 最终实现非线性迭代估计. 实验结果表明, 本文方法突破了本振光相位需与相干态相位正交的局限性, 避免了传统量子锁相环方法引入的线性化误差, 实现了对相干态相位的准确、稳定估计.
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关键词:
- 相干态相位估计 /
- 量子零拍探测 /
- 非线性锁相环 /
- 正交单纯形容积卡尔曼滤波
The navigation mechanism of obtaining phase parameters based on quantum theory can break through the limitation of classical physical limit to navigation accuracy. In order to achieve the accurate estimation of the coherent state phase, it is usually assumed that the local oscillator phase must be orthogonal to the coherent state phase in the method of quantum homodyne detection. However, the coherent state phase is unknown and the hypothesis cannot be guaranteed to be correct in practice. In this paper we design a nonlinear phase-locked loop to solve the problem. Firstly, in order to obtain the Wigner distribution for a coherent state, we start with the Wigner distribution for the vacuum state and analyze the noise characteristics according to Wigner distribution of coherent state, then the output of homodyne detection is derived. Secondly, in order to avoid introducing errors in theory, caused by linearization and cope with the limiting requirement between local oscillator phase and coherent state phase in the phase tracking of coherent state, we design an orthogonal simplex cubature Kalman filter (OSCKF) algorithm to achieve the function of the nonlinear phase-locked loop. The algorithm converges by updating the state of the local oscillator phase multiple times, and then, the accurate coherent phase is obtained. Finally, according to the design of the phase-locked loop, we observe the data of homodyne detection and then verify the correctness of the OSCKF algorithm. The simulation results show that the OSCKF can converge to the real phase after observing 200 sampled data and the accuracy is higher than extended Kalmn filter (EKF) and cubature Kalman filter (CKF), and the real phase can be obtained under different local oscillator phase. In conclusion, the nonlinear phase-locked loop based on OSCKF algorithm breaks the limitation of traditional way in which the initial local oscillator phase is required to be orthogonal to the coherent state phase, and effectively avoid the linearization error and improve the anti-nonlinear ability. It is very significant in theory and application .-
Keywords:
- coherent state phase estimation /
- quantum homodyne detection /
- nonlinear phase-locked loop /
- orthogonal simplex cubature Kalman filter
[1] He R K, Chen S X, Wu H, Xu H, Chen K, Liu J 2019 IEEE Access 7 2686
[2] Zhang Z T, Zhang J S 2010 Chin. Phys. B 19 104601Google Scholar
[3] Furuya K, Nemoto T, Kato K, Shoji Y, Mizumoto T 2016 J. Lightwave Technol. 34 1699Google Scholar
[4] 胡志辉, 冯久超 2011 物理学报 60 070505
Hu Z H, Feng J C 2011 Acta Phys. Sin. 60 070505 (in Chinese)
[5] Leonhardt U, Vaccaro J A, Böhmer B, Paul H 1995 Phys. Rev. A 51 84Google Scholar
[6] Berry D W, Wiseman H M 2002 Phys. Rev. A 65 043803Google Scholar
[7] Joo J, Park k, Jeong H, Munro W J, Nemoto K, Spiller T P 2012 Phys. Rev. A 86 043828420
[8] Caves C M 1981 Phys. Rev. D 23 1693Google Scholar
[9] Bondurant R S, Shapiro J H 1984 Phys. Rev. A 30 2548Google Scholar
[10] Yuen H P, Chan V W S 1983 Opt. Lett. 8 177Google Scholar
[11] Jin X L, Su J, Zheng Y H, Chen C Y, Wang W Z, Peng K C 2015 Opt. Express 23 23859Google Scholar
[12] Wiseman H M 1995 Phys. Rev. Lett. 75 4587Google Scholar
[13] Armen M A, Au J K, Stockton J K, Doherty A C, Mabuchi H 2002 Phys. Rev. Lett. 89 133602Google Scholar
[14] Berry D W, Wiseman H M 2006 Phys. Rev. A 73 063824Google Scholar
[15] Tsang M, Shapiro J H, Lloyd S 2008 Phys. Rev. A 78 053820Google Scholar
[16] Tsang M, Shapiro J H, Lloyd S 2009 Phys. Rev. A 79 053843Google Scholar
[17] Tsang M, Shapiro J H, Lloyd S 2009 International Quantum Electronics Conference Baltimore Maryland, May 31–June 5, 2009 p6
[18] Wu H, Chen S X, Yang B F, Chen K 2016 J. Guid. Control. Dynam. 39 1866
[19] Zhang L, Li S, Zhang E, Chen Q 2017 IET Sci. Meas. Technol. 11 929Google Scholar
[20] Wu H, Chen S X, Yang B F, Luo X 2016 IET Sci. Meas. Technol. 10 370Google Scholar
[21] Li S E, Xu S, Kum D 2016 Neurocomputing 177 363Google Scholar
[22] Liu H, Wu W 2017 Sensors 17 741Google Scholar
[23] Chang L, Hu B, Li A 2013 IEEE Trans. Autom. Control 58 252Google Scholar
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[1] He R K, Chen S X, Wu H, Xu H, Chen K, Liu J 2019 IEEE Access 7 2686
[2] Zhang Z T, Zhang J S 2010 Chin. Phys. B 19 104601Google Scholar
[3] Furuya K, Nemoto T, Kato K, Shoji Y, Mizumoto T 2016 J. Lightwave Technol. 34 1699Google Scholar
[4] 胡志辉, 冯久超 2011 物理学报 60 070505
Hu Z H, Feng J C 2011 Acta Phys. Sin. 60 070505 (in Chinese)
[5] Leonhardt U, Vaccaro J A, Böhmer B, Paul H 1995 Phys. Rev. A 51 84Google Scholar
[6] Berry D W, Wiseman H M 2002 Phys. Rev. A 65 043803Google Scholar
[7] Joo J, Park k, Jeong H, Munro W J, Nemoto K, Spiller T P 2012 Phys. Rev. A 86 043828420
[8] Caves C M 1981 Phys. Rev. D 23 1693Google Scholar
[9] Bondurant R S, Shapiro J H 1984 Phys. Rev. A 30 2548Google Scholar
[10] Yuen H P, Chan V W S 1983 Opt. Lett. 8 177Google Scholar
[11] Jin X L, Su J, Zheng Y H, Chen C Y, Wang W Z, Peng K C 2015 Opt. Express 23 23859Google Scholar
[12] Wiseman H M 1995 Phys. Rev. Lett. 75 4587Google Scholar
[13] Armen M A, Au J K, Stockton J K, Doherty A C, Mabuchi H 2002 Phys. Rev. Lett. 89 133602Google Scholar
[14] Berry D W, Wiseman H M 2006 Phys. Rev. A 73 063824Google Scholar
[15] Tsang M, Shapiro J H, Lloyd S 2008 Phys. Rev. A 78 053820Google Scholar
[16] Tsang M, Shapiro J H, Lloyd S 2009 Phys. Rev. A 79 053843Google Scholar
[17] Tsang M, Shapiro J H, Lloyd S 2009 International Quantum Electronics Conference Baltimore Maryland, May 31–June 5, 2009 p6
[18] Wu H, Chen S X, Yang B F, Chen K 2016 J. Guid. Control. Dynam. 39 1866
[19] Zhang L, Li S, Zhang E, Chen Q 2017 IET Sci. Meas. Technol. 11 929Google Scholar
[20] Wu H, Chen S X, Yang B F, Luo X 2016 IET Sci. Meas. Technol. 10 370Google Scholar
[21] Li S E, Xu S, Kum D 2016 Neurocomputing 177 363Google Scholar
[22] Liu H, Wu W 2017 Sensors 17 741Google Scholar
[23] Chang L, Hu B, Li A 2013 IEEE Trans. Autom. Control 58 252Google Scholar
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