Close-Loop System Identification Using Over-sampling Scheme and Its Estimate Accuracy Analysis

A new identification method for a linear discrete-time closed-loop system is proposed based on an output over-sampling scheme. When the system outputs are over-sampled the new output sequences would contain more information about the plant structure. Using general least squares method （GLS） the plant over-sampled model should be recognized. Then the original plant model should be obtained by its relationship with the over-sampled model. Compared with conventional approaches the advantage of the new method is that even if the ordinary identifiability conditions are not satisfied, a close-loop system can be identified by using the oversampled output without utilizing any external test signal. Accuracy analysis shows the relationship between the estimation error and the over-sampling rate. Numerical simulation illnstrates its effectiveness.

Improving Accuracy of Estimating Two-Qubit States with Hedged Maximum Likelihood

As a widely used reconstruction algorithm in quantum state tomography, maximum likelihood estimation tends to assign a rank-deficient matrix, which decreases estimation accuracy for certain quantum states. Fortunately, hedged maximum likelihood estimation （HMLE） [Phys. Rev. Lett. 105 （2010）200504] was proposed to avoid this problem. Here we study more details about this proposal in the two-qubit case and further improve its performance. We ameliorate the HMLE method by updating the hedging function based on the purity of the estimated state. Both performances of HMLE and ameliorated HMLE are demonstrated by numerical simulation and experimental implementation on the Werner states of polarization-entangled photons.

A novel range-Doppler imaging algorithm with OFDM radar

Traditional pulse Doppler radar estimates the Doppler frequency by taking advantage of Doppler modulation over different pulses and usually it requires a few pulses to estimate the Dop- pler frequency. In this paper, a novel range-Doppler imaging algorithm based on single pulse with orthogonal frequency division multiplexing （OFDM） radar is proposed, where the OFDM pulse is composed of phase coded symbols. The Doppler frequency is estimated using one single pulse by utilizing Doppler modulation over different symbols, which remarkably increases the data update rate. Besides, it is shown that the range and Doppler estimations are completely independent and the well-known range-Doppler coupling effect does not exist. The effects of target movement on the performances of the proposed algorithm are also discussed and the results show that the algo- rithm is not sensitive to velocity. Performances of the proposed algorithm as well as comparisons with other range-Doppler algorithms are demonstrated via simulation experiments.

Adaptive cubature Kalman filter with the estimation of correlation between multiplicative noise and additive measurement noise

Mobile robots are often subject to multiplicative noise in the target tracking tasks,where the multiplicative measurement noise is correlated with additive measurement noise.In this paper,first,a correlation multiplicative measurement noise model is established.It is able to more accurately represent the measurement error caused by the distance sensor dependence state.Then,the estimated performance mismatch problem of Cubature Kalman Filter(CKF)under multiplicative noise is analyzed.An improved Gaussian filter algorithm is introduced to help obtain the CKF algorithm with correlated multiplicative noise.In practice,the model parameters are unknown or inaccurate,especially the correlation of noise is difficult to obtain,which can lead to a decrease in filtering accuracy or even divergence.To address this,an adaptive CKF algorithm is further provided to achieve reliable state estimation for the unknown noise correlation coefficient and thus the application of the CKF algorithm is extended.Finally,the estimated performance is analyzed theoretically,and the simulation study is conducted to validate the effectiveness of the proposed algorithm.