# A Novel UWB Positioning Method Based on a Maximum-Correntropy Unscented Kalman Filter
## 1 Introduction
Meanwhile, the same study constructed an error-compensation method to achieve positioning and improved the ranging and positioning accuracy of UWB positioning in practical applications. A third study in the literature, [7], estimated location information based on [PERSON] and [PERSON]'s collaborative location method via setting a threshold value for the residual of estimated results to identify the NLOS environment, calculating location results using the Kalman method for qualified measurement data, weighting the residual and moving averages of the location results and completing an update of the final location. Reference [8] proposed a robust-volume Kalman filter (CKF) algorithm with a noise-time-varying estimator. The robust equivalent covariance matrix was constructed via use of the predicted residual factor to control the influence of observation outliers on the filter-parameter solution. The Sage Husa algorithm was used to estimate and correct the statistical characteristics of system noise in real time, improve filtering accuracy and stability and achieve high-precision positioning. Reference [9] proposed a robust Student's t-based Kalman filter, which provided a Gaussian approximation of posterior distribution. In order to avoid deviation of UWB position estimation caused by linearization, some researchers used an unscented Kalman filter (UKF) for UWB position estimation, estimated the position through traceless transformation of sigma points and obtained the UWB position estimation algorithm based on the UKF [10; 11]. However, the algorithms based on the extended Kalman filter (EKF) and the UKF both assume that the state noise and the measurement noise will obey Gaussian distribution; therefore, they are not suitable for non Gaussian noise. However, in the UWB indoor-positioning process, non Gaussian noise, such as the noise of mobile cars, pulse noise caused by electromagnetic interference and communication system faults or defects, does exist objectively [12; 13]. Aiming at the problem of measurement-information abnormal-error and nonlinear filtering for UWB positioning, this paper proposes a maximum cross-correlation entropy-based unscented Kalman filter algorithm based on the maximum cross-correlation criterion on the basis of a nonlinear filter algorithm. The algorithm first obtains a predictive state estimate and a covariance matrix through traceless transformation and then reconstructs observation information using the nonlinear regression method based on the maximum cross-correlation entropy criterion, which enhances robustness of the unscented Kalman filter algorithm. This paper is arranged as follows: Section 2 introduces the UWB positioning method based on nonlinear filtering, including the principle of UWB positioning and the UWB positioning method based on the unscented Kalman filter. Section 3 introduces the pro posed maximum cross-correlation entropy UKF algorithm, including the maximum cross-correlation entropy criterion and the proposed improved UKF algorithm. In Section 4, the effectiveness of the proposed method is demonstrated through simulation and an experiment. The fifth section is the conclusion.