Online Input Estimation and Noise Identification for Maneuvering Target Tracking

• Main Authors: Kuo-Guan, 吳國光
• Other Authors: Chi-Min Liu
• Format: Others
• Language: zh-TW
• Published: 1999
• Online Access: http://ndltd.ncl.edu.tw/handle/40367730358073797494

博士 === 國立交通大學 === 資訊工程系 === 87 === The existing target tracking algorithms mostly rely on prior selection of system parameters: the input exciting target maneuver and the parameters of the measurement noise distributions. However, these parameters are actually unknown and time-varying. To obtain more accurate tracking results, online identification is then necessary. In maneuvering target tracking, the existing algorithms mainly use the multiple-filter approach. This approach simultaneously run multiple tracking filters, designed based on pre-selected maneuver input values, to estimate the state of a maneuvering target. When applying this approach to track a highly maneuverable target, such as a tactical fighter, a large number of tracking filters will be required which results in high computational complexity. A possible method to reduce complexity is to online estimate the maneuver input, and adjust the setting of tracking filters. In this way, the tracking filters can be made adaptive with target maneuvers and hence less tracking filters will be required. On the other hand, due to the random wandering of the radar reflection center, the measurement noise presents non-Gaussian behavior. This type of noise is referred to as glint and its distribution is heavy-tailed. The statistics of glint noise change with target aspect and motion making it a non-stationary process. Although nonlinear tracking algorithms have been developed to solve the problem, knowledge of the noise distribution model has to be known. Thus, online noise identification is required. In this thesis, we propose algorithms for online maneuver input estimation and noise identification for tracking maneuvering targets. For the problem of online maneuver input estimation, we derive a Bayesian method for the Gaussian measurement noise and a trimmed least-squares method for the glint measurement noise. The Bayesian method is derived based on a Gaussian-mixture model for the maneuver input distribution. This method obtains the input estimate from a weighted combination of the means of the mixture components. By considering the transition among the mixture components as a Markov process, our method can respond more quickly to the abrupt change of maneuver values than the least-squares method. To reduce the effect of measurement noise, we propose a pre-filtering scheme using a reduced-gain Kalman filter. When the measurement noise is non-Gaussian, we propose to estimate the input by fitting a second-order polynomial to the position measurements. A trimmed least-squares method is used to find the solution. This method can reduce the effect of the glint spike achieving higher accuracy than the conventional least-squares method. As to the problem of online identifying the non-Gaussian measurement noise, we propose a batch-processing and a recursive-processing algorithm. Since measurement noise is usually unavailable, we first extract measurement noise from target position measurements. The proposed noise extraction method uses a first- or second-order differentiator and a order statistic filter. In the first algorithm, we perform identification using the maximum-likelihood (ML) method. The results show that the parameter estimates are close to those obtained from exact knowledge of the measurement noise. Since the ML method has high computational complexity and cannot react immediately with the change of the noise statistics, we thus propose a recursive algorithm, which uses the stochastic-gradient-descent (SGD) method. We analyze its convergence property and derive closed-form expressions for sufficient step size bounds. It is shown that the identified parameters using the simpler SGD method can converge fast and the accuracy is comparable to that of the ML method. Using the sufficient step size bounds, the change of the noise statistics can be well tracked. The online identified parameters can be directly fed into the tracking algorithm making it adapt to the change of the noise statistics.