CAJ | 학술논문

针对海底集矿车长基线声学定位受工作噪声干扰以及航位推算精度受打滑干扰等问题,基于附加打滑参数的履带车运动学模型和根据湖试数据对导航系统过程噪声与测量噪声的描述,并提出了一种自适应时滞扩展卡尔曼滤波方法.该方法利用新息序列实现噪声统计特性自适应,然后考虑测量数据时延带来的影响,通过卡尔曼滤波器将长基线定位信息与航位推算信息进行融合,得到集矿车的位置估计.仿真结果证实,该自适应卡尔曼滤波器能有效地适应过程噪声与测量噪声统计特性的变化,比常规卡尔曼滤波器具有更好的海底集矿车定位效果.
침대해저집광차장기선성학정위수공작조성간우이급항위추산정도수타활간우등문제,기우부가타활삼수적리대차운동학모형화근거호시수거대도항계통과정조성여측량조성적묘술,병제출료일충자괄응시체확전잡이만려파방법.해방법이용신식서렬실현조성통계특성자괄응,연후고필측량수거시연대래적영향,통과잡이만려파기장장기선정위신식여항위추산신식진행융합,득도집광차적위치고계.방진결과증실,해자괄응잡이만려파기능유효지괄응과정조성여측량조성통계특성적변화,비상규잡이만려파기구유경호적해저집광차정위효과.
Since long base line ( LBL) based sonar localization systems of seabed mining vehicles are seriously affected by working environment noises, and their dead reckoning (DR) accuracy is seriously affected by vehicle slippage, this paper proposes an adaptive time delay Kalman filtering method based on a kinematic model for mining vehicles with sliding parameters and the description of the process and measurement noises based on the experiment data collected from a lake. The method uses the innovation sequence to achieve the adaptive statistics features of both the process noise and measurement noise, takes account of the influence of measurement data delay, and then obtains the localization estimate of a seabed mining vehicle through the fusion of the LBL data and the DR data by the Kalman filter. The simulation results prove that the adaptive Kalman filter can deal with the changing statistics features of process noise and measurement noise very well, and has the better localization estimation of the seabed mining vehicle than a normal Kalman filter.