基于重力和环境特征的水下导航定位方法研究
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摘要
惯性导航系统可以满足需要长期执行水下潜航任务的潜艇及其它水下航行器对导航系统自主性和隐蔽性方面的要求,但是其精确性却不尽人意,需要其它辅助导航系统来校正其众所周知的随时间不断增长的定位误差。海洋地球物理导航方法由于具有定位精度高、无需浮出水面等优点,恰好可以弥补惯性导航系统在定位精度方面的不足,是潜艇等水下航行器理想的水下辅助导航定位手段。本论文即着重研究基于重力和环境特征的水下地球物理导航定位方法。
鉴于我国现阶段尚没有实测重力梯度数据的客观情况,利用直角矩形棱柱法由海洋地形图构造了重力梯度图,并根据两片真实海域的地形图生成了相应的梯度图,作为重力梯度匹配算法研究和测试的基础。针对大部分现有重力匹配算法在惯导位置误差较大情况下不可用的缺点,在深入研究概率神经网络原理基础上,利用神经网络在分类问题上的优势,提出基于概率神经网络的重力梯度匹配算法,并讨论了不同梯度分量组合作为网络输入对算法匹配成功概率的影响,以解决惯导系统的大位置误差校正问题。仿真试验证明该梯度匹配算法无需惯导信息即可有效降低导航系统的位置误差,可用于潜器位置误差较大的情况下。
从重力仪测量原理入手,深入分析了在正常重力计算过程中较大的惯导位置误差会为匹配导航系统引入数值上无法忽略的观测误差的机理,提出将重力仪测量和重力异常数据库均转化为重力后再进行匹配的策略,从根本上消除了这种观测误差带来的不利影响。充分利用惯导系统短时高精度的特点,提出另一种在惯导位置误差较大情况下适用的基于价值函数最优化的等值线匹配算法,在先验重力图上通过搜索、跟踪和决策过程寻找与惯导航迹形状最相似的航迹作为匹配航迹。在模拟重力图和真实重力图上进行的仿真试验均表明,在正常海况和中等精度惯导情况下,等值线匹配算法即可获得较高的定位精度,适用于惯导大位置误差的水下校正。
为进一步实现对惯导系统的速度、姿态误差的校正,在重力匹配导航系统提供的位置校正信息基础上,以惯导系统误差模型为系统方程,重力匹配算法提供的位置与惯导输出位置之间的差值为观测量,构建了重力／惯性组合导航系统,通过卡尔曼滤波实现对惯导位置、速度和姿态误差的精确估计。分析惯导位置航向误差与陀螺漂移之间的关系,以重力匹配算法得到的匹配位置为位置重调值,应用三次定位校正法对陀螺常值漂移进行估计,并通过仿真试验证明了利用重力辅助导航结果对陀螺漂移估计的可行性和有效性。
在那些尚未测绘的或重力变化过于平缓的海区,重力辅助导航系统将无法工作,此时可采用基于海洋环境特征的同步构图定位算法作为重力辅助导航的替代方法,用于降低导航误差的增长速度。论文对同步构图定位算法的基本原理进行了深入研究,并针对水下载体的非线性系统模型,讨论了系统的预测、观测和状态扩充过程,利用扩展卡尔曼滤波技术对算法进行了实现。区域探索和航渡两组仿真试验的结果均证明,基于扩展卡尔曼滤波的同步构图定位算法可以有效减缓潜艇等水下航行器的误差增长速度,提高导航系统的定位精度。针对现有数据关联方法在计算复杂度和关联稳定性方面的不足,提出了一种改进数据关联方法,并利用仿真试验证明了该算法可在较小的计算复杂度下获得良好的关联效果,可作为同步构图定位算法的在线数据关联方法使用。
Inertial navigation system (INS) can meet the requirements of autonomous and covert navigation system for submarines and other underwater vehicles over long periods in blue water without exposure, however, the precision of INS is not satisfactory due to its well-known errors which increase with time. As a result, other aiding navigation systems are needed to correct such errors. Due to the characteristics of high precision and no need to surface, the marine geophysical navigation could make up for the aforementioned INS deficiencies in positioning precision, thus is the ideal underwater aiding navigation means for submarines and other underwater vehicles. Therefore, the dissertation will focus on the study of underwater geophysical navigation methods based on gravity and environmental features.
Since there are no measured gravity gradient data in our country nowadays, the right rectangular prism method is used to construct the gravity gradient map from marine terrain map, and two gradient maps are generated from actual marine terrain maps to be used as the study and test basis of gradient matching algorithm. Since most of current gravity matching algorithms failed in the event of large INS position error, based on the further study of the principle of probabilistic neural network (PNN), a PNN based gravity gradient matching algorithm is proposed to correct large INS position errors by taking advantages of fine classification ability of PNN. When taking different combinations of gradient components as network input, the influence on the probabilities of successful matchings is also discussed. Simulation results show that the presented gradient matching algorithm can effectively reduce the positioning errors of navigation system without the help of other INS information, and can be used in the event of large positioning errors.
From the viewpoint of gravimeter measure principle, the truth that large positioning error will lead to quite large observation error for matching navigation system during the normal gravity computation process is further analyzed, and a strategy of changing both the gravimeter measurement and gravity anomaly into gravity for matching process is carried out to eliminate such observation error. By taking advantage of short-term high precision characteristics of INS, a contour matching algorithm adaptive to large positioning error is proposed, which takes the trajectory most similar to INS indicated one as the matching trajectory. Simulations performed on synthetic gravity map and actual gravity map show that, the contour matching algorithm can achieve a pretty high precision even under operating sea condition and with a moderate-grade INS, thereby adapting to underwater correction of large INS positioning error.
Furthermore, to realize the correction of velocity and attitude errors for INS, a gravity/INS integrated navigation system is constructed on the basis of position reset information provided by gravity matching navigation system. The integrated system takes INS error model as the state equation and the positional difference between matching algorithm and INS as observation, and achieves a precise estimation of position, velocity and attitude errors of INS by the use of Kalman filter. The relationship between INS position, heading errors and gyro drift is analyzed, and the triple location correction method is applied to estimate the constant drifts of gyro. The feasibility and validity for estimating gyro drifts are proved via simulation experiments using the results of gravity aided navigation.
When underwater vehicles cruise in some area without any gravity data or with a featureless gravity field, gravity aided navigation system will not work. At this time, the simultaneous localization and mapping (SLAM) algorithm based on marine environmental features can substitute for gravity aided navigation system to reduce the increasing rate of navigation errors. The basic principle of SLAM is studied, and the systemic prediction, observation and state augmentation process are discussed according to the nonlinear model of underwater vehicles, and the algorithm is implemented with Extended Kalman Filter techniques. The simulated experiment results acquired in straight sailing pattern and local searching pattern prove that, EKF based SLAM algorithm can efficiently slow down the error increase and improve the positioning precision for submarines and other underwater vehicles. Since current data association methods have some deficiencies in computational complexity and association stability, a modified data association method is put forward. Simulation experiment results show that the presented method could achieve a satisfactory association result with a small computational complexity, thus suitable to on-line data association for underwater SLAM implementations.
鉴于我国现阶段尚没有实测重力梯度数据的客观情况,利用直角矩形棱柱法由海洋地形图构造了重力梯度图,并根据两片真实海域的地形图生成了相应的梯度图,作为重力梯度匹配算法研究和测试的基础。针对大部分现有重力匹配算法在惯导位置误差较大情况下不可用的缺点,在深入研究概率神经网络原理基础上,利用神经网络在分类问题上的优势,提出基于概率神经网络的重力梯度匹配算法,并讨论了不同梯度分量组合作为网络输入对算法匹配成功概率的影响,以解决惯导系统的大位置误差校正问题。仿真试验证明该梯度匹配算法无需惯导信息即可有效降低导航系统的位置误差,可用于潜器位置误差较大的情况下。
从重力仪测量原理入手,深入分析了在正常重力计算过程中较大的惯导位置误差会为匹配导航系统引入数值上无法忽略的观测误差的机理,提出将重力仪测量和重力异常数据库均转化为重力后再进行匹配的策略,从根本上消除了这种观测误差带来的不利影响。充分利用惯导系统短时高精度的特点,提出另一种在惯导位置误差较大情况下适用的基于价值函数最优化的等值线匹配算法,在先验重力图上通过搜索、跟踪和决策过程寻找与惯导航迹形状最相似的航迹作为匹配航迹。在模拟重力图和真实重力图上进行的仿真试验均表明,在正常海况和中等精度惯导情况下,等值线匹配算法即可获得较高的定位精度,适用于惯导大位置误差的水下校正。
为进一步实现对惯导系统的速度、姿态误差的校正,在重力匹配导航系统提供的位置校正信息基础上,以惯导系统误差模型为系统方程,重力匹配算法提供的位置与惯导输出位置之间的差值为观测量,构建了重力／惯性组合导航系统,通过卡尔曼滤波实现对惯导位置、速度和姿态误差的精确估计。分析惯导位置航向误差与陀螺漂移之间的关系,以重力匹配算法得到的匹配位置为位置重调值,应用三次定位校正法对陀螺常值漂移进行估计,并通过仿真试验证明了利用重力辅助导航结果对陀螺漂移估计的可行性和有效性。
在那些尚未测绘的或重力变化过于平缓的海区,重力辅助导航系统将无法工作,此时可采用基于海洋环境特征的同步构图定位算法作为重力辅助导航的替代方法,用于降低导航误差的增长速度。论文对同步构图定位算法的基本原理进行了深入研究,并针对水下载体的非线性系统模型,讨论了系统的预测、观测和状态扩充过程,利用扩展卡尔曼滤波技术对算法进行了实现。区域探索和航渡两组仿真试验的结果均证明,基于扩展卡尔曼滤波的同步构图定位算法可以有效减缓潜艇等水下航行器的误差增长速度,提高导航系统的定位精度。针对现有数据关联方法在计算复杂度和关联稳定性方面的不足,提出了一种改进数据关联方法,并利用仿真试验证明了该算法可在较小的计算复杂度下获得良好的关联效果,可作为同步构图定位算法的在线数据关联方法使用。
Inertial navigation system (INS) can meet the requirements of autonomous and covert navigation system for submarines and other underwater vehicles over long periods in blue water without exposure, however, the precision of INS is not satisfactory due to its well-known errors which increase with time. As a result, other aiding navigation systems are needed to correct such errors. Due to the characteristics of high precision and no need to surface, the marine geophysical navigation could make up for the aforementioned INS deficiencies in positioning precision, thus is the ideal underwater aiding navigation means for submarines and other underwater vehicles. Therefore, the dissertation will focus on the study of underwater geophysical navigation methods based on gravity and environmental features.
Since there are no measured gravity gradient data in our country nowadays, the right rectangular prism method is used to construct the gravity gradient map from marine terrain map, and two gradient maps are generated from actual marine terrain maps to be used as the study and test basis of gradient matching algorithm. Since most of current gravity matching algorithms failed in the event of large INS position error, based on the further study of the principle of probabilistic neural network (PNN), a PNN based gravity gradient matching algorithm is proposed to correct large INS position errors by taking advantages of fine classification ability of PNN. When taking different combinations of gradient components as network input, the influence on the probabilities of successful matchings is also discussed. Simulation results show that the presented gradient matching algorithm can effectively reduce the positioning errors of navigation system without the help of other INS information, and can be used in the event of large positioning errors.
From the viewpoint of gravimeter measure principle, the truth that large positioning error will lead to quite large observation error for matching navigation system during the normal gravity computation process is further analyzed, and a strategy of changing both the gravimeter measurement and gravity anomaly into gravity for matching process is carried out to eliminate such observation error. By taking advantage of short-term high precision characteristics of INS, a contour matching algorithm adaptive to large positioning error is proposed, which takes the trajectory most similar to INS indicated one as the matching trajectory. Simulations performed on synthetic gravity map and actual gravity map show that, the contour matching algorithm can achieve a pretty high precision even under operating sea condition and with a moderate-grade INS, thereby adapting to underwater correction of large INS positioning error.
Furthermore, to realize the correction of velocity and attitude errors for INS, a gravity/INS integrated navigation system is constructed on the basis of position reset information provided by gravity matching navigation system. The integrated system takes INS error model as the state equation and the positional difference between matching algorithm and INS as observation, and achieves a precise estimation of position, velocity and attitude errors of INS by the use of Kalman filter. The relationship between INS position, heading errors and gyro drift is analyzed, and the triple location correction method is applied to estimate the constant drifts of gyro. The feasibility and validity for estimating gyro drifts are proved via simulation experiments using the results of gravity aided navigation.
When underwater vehicles cruise in some area without any gravity data or with a featureless gravity field, gravity aided navigation system will not work. At this time, the simultaneous localization and mapping (SLAM) algorithm based on marine environmental features can substitute for gravity aided navigation system to reduce the increasing rate of navigation errors. The basic principle of SLAM is studied, and the systemic prediction, observation and state augmentation process are discussed according to the nonlinear model of underwater vehicles, and the algorithm is implemented with Extended Kalman Filter techniques. The simulated experiment results acquired in straight sailing pattern and local searching pattern prove that, EKF based SLAM algorithm can efficiently slow down the error increase and improve the positioning precision for submarines and other underwater vehicles. Since current data association methods have some deficiencies in computational complexity and association stability, a modified data association method is put forward. Simulation experiment results show that the presented method could achieve a satisfactory association result with a small computational complexity, thus suitable to on-line data association for underwater SLAM implementations.
引文
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