适于惯性制导炸弹的捷联惯导关键技术研究
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摘要
制导炸弹在现代战争中应用越来越广泛,而导航与制导系统是其核心部件。随着卫星导航系统的发展,卫星/惯性组合导航成为导航领域的主要发展方向。但卫星导航系统易受到干扰而变得不可使用,过分依赖卫星导航系统存在隐患,“用而不靠”是比较妥善的方式。捷联惯导系统可以不受外部干扰、完全自主的工作,所以具有一定精度的捷联惯导系统可为制导炸弹提供可靠的导航与制导能力,具有非常重要的军事应用价值。
本文的研究对象为适于惯性制导的中等精度捷联惯导系统,研究目的在于提高捷联惯导系统的精度,同时降低成本;研究的主要内容为捷联惯导系统的几项关键技术,包括数据采集技术、陀螺的标定与补偿技术、初始对准技术和磁航向系统的校正与补偿等。主要工作包括以下几个方面:
(1)研究了捷联惯导系统的构成原理,采用国产挠性陀螺仪和石英挠性加速度计设计了惯性测量单元。研究了目前导航计算机的研制方案,设计了以DSP和CPLD为核心的导航计算机。研究了数据采集技术和惯性仪表的数据采集电路,分别设计了高精度、低成本的流水线A/D和Σ-Δ型A/D多路并行采集、并行滤波数据采集电路。
(2)研究了陀螺刻度系数非线性问题,设计了分段非线性插值标定算法,提出了陀螺刻度系数神经网络标定方法和神经网络再训练方法。分段非线性插值标定算法在整个区间上细化陀螺的刻度系数,减小了非线性误差,与一次拟合法和分段法相比,非线性误差分别减小约6.2倍和2.4倍;实验结果表明神经网络与分段非线性插值算法的标定结果接近,说明神经网络标定方法是可行的。对神经网络模型进行再训练,非线性误差减小了约1.7倍,可提高神经网络对刻度系数非线性的逼近程度,进一步减小非线性误差。
(3)研究了捷联惯导系统的自动化标定技术。利用转台系统,不增加硬件设备,通过设计监控计算机与导航计算机之间的通信协议,建立串口通信机制,实现了捷联惯导系统标定过程的流程控制、数据采集、数据记录、标定计算和标定参数装订的自动化,标定的中间过程不需要人员参与。
(4)捷联惯导系统的测量信号中包含大量随机噪声,降低了初始对准的精度。提出将提升小波算法应用于惯性仪表测量信号去噪和初始对准中,实验结果表明该方法可以有效滤除信号中的随机噪声,能够提高初始对准的速度和精度;相比于小波算法,提升小波算法在测量信号滤波中能够缩短将近一半时间,更适于实时、在线应用。
(5)研究了适用于捷联惯导系统的传递对准技术,提出了神经网络传递对准方法。用子惯导的速度和姿态信息作为输入,以主、子惯导的速度和姿态误差作为参考信号,训练神经网络补偿子惯导的速度和姿态。仿真实验表明,神经网络传递对准方法对子惯导速度和姿态均有很好的补偿效果;并且研究了不同神经元对子惯导补偿效果的影响,确定了具有6个神经元的最佳网络结构。通过跑车实验,验证了神经网络方法对速度和姿态均有比较好的补偿效果,速度补偿误差均值小于0.15m/s,俯仰角和滚动角补偿误差均值小于0.03°,对航向角的补偿误差均值小于0.03°,补偿的速度和姿态误差能够稳定,不会发散。经神经网络补偿后60s的位置误差小于8m,100s的位置误差小于17m,表明神经网络补偿能够起到抑制位置误差发散的效果。
(6)研究了低成本磁航向系统的工作原理,设计了磁航向系统信号去噪和异常检测方法。针对其测量信号中包含大量噪声的问题,提出了应用提升小波去除磁航向系统测量噪声的方法;由于外部干扰磁场的存在,磁航向系统会出现测量异常情况,针对其测量异常,提出应用提升小波对异常进行检测的方法。实验结果表明,提升小波可有效地去除磁航向系统的测量噪声;并能有效的检测出磁航向系统的测量异常;校正后的数据可增加磁航向系统航向角的可用性。
(7)研究了磁航向系统误差补偿技术,结合捷联惯导系统的特点,提出以捷联惯导系统的航向信息为参考,训练神经网络补偿磁航向系统的误差。实质上,神经网络的补偿作用是实现磁航向系统跟踪捷联惯导系统的航向,实现两者之间的映射。实验结果表明,神经网络补偿方法将磁航向系统的航向角误差由±15?减小到约±1?,取得了明显的效果。
本文基于挠性陀螺集成的硬件系统,主要研究提高SINS精度方法,所研究的方法也适用于光纤陀螺等其他中等精度的SINS。
Guided munitions are increasing widely applied in high-tech modern wars. Navigation and guidance system is one of the keys of Guided munitions. With the development of satellite navigation system(SNS), SNS/SINS(strapdown inertial navigatoion system) integrated navigation has been the leading development direction. But SNS is likely to be unused because of outside disturbance. Toward SNS, the proper way is to use but not depend. SINS has undisturbed and autonomous navigation capacity. Therefore, SINS is very significant for military application with certain precision.
Taking medium-precision SINS suitable for inertial guidance as object, this dissertation intends to improve navigation precision and reduce the cost of SINS. Main contents are about the key technologies of SINS, including data-sampling, gyro’s calibration and compensation, SINS alignment, compensation of magnetic heading system(MHS). The details of main contents are the following aspects:
(1) Based on the introduced principles and developments of SINS, inertial instrument, navigation computer and data-sampling technology, we design a SINS including an inertial measurement unit(IMU) with domestic flexibility gyroscopes and quartz accelerometers, a navigation computer system with the core of digital signal processor(DSP) and complex programmable logical device(CPLD), and two high-precision and low-cost data-sampling systems with pipeline A/D chips and sigma-delta A/D chips respectively.
(2) Toward SINS, the calibration methods of piecewise nonlinear interpolating(PNIP) and multilayer feedforward neural network(MLFN) are put forward to compensate the nonlinear error of gyroscope’s scale factor. The PNIP can subdivide the scale factor and minish nonlinear error about 6.2 times and 2.4 times compared with least square fitting and piecewise compensation method respectively. Experiment results show that the calibration result of scale factor using MLFN is close to that of PNIP, and also indicate that MLFN is feasible. A retraining method is proposed to improve the performance of the trained MLFN. Results show that the retrained MLFN can much more approach the real scale factor and minish the nonlinear error about 1.7 times compared with the unrestrained MLFN.
(3) In order to meet the requirement of SINS’batch production, an automatic calibration and test method is designed. Considering SINS’characters, we design the communication protocol and realize serial communication mechanism between monitor computer and SINS. Based on a turntable system and utilizing the serial communication mechanism, the calibration process of SINS is automatically realized without adding any hardware equipment. The automatic process includes calibration flow control, data record, calibration result computation and parameters storage.
(4) Considering noisy data of inertial instruments which could depress SINS alignment precision, lifting wavelet is applied into the signal de-noising of inertial instruments and SINS coarse alignment. Results show that lifting wavelet algorithm is effective to the signal de-noising of inertial instruments. Also it can improve the alignment precision of SINS. Compared with classic wavelet, lifting wavelet can save almost half computation time and is more suitable to real-time and online application.
(5) Based on the introduction of transfer alignment methods, a neural network(NN) transfer alignment method is put forward to compensate the errors of salve inertial navigation system(INS). In the NN model, NN inputs are the velocity and attitude of salve INS, and NN output references are the errors of velocity and attitude between master INS and slave INS. Simulation results show that the NN model can make satisfactory compensation to the velocity and attitude of slave INS. Also NN models with deferent neurons could have deferent compensation effect, and we ascertain the best NN architecture with 6 neurons by simulation experiments. Vehicle test results show that NN transfer alignment method has effective compensation to velocity and attitude of slave INS with average of velocity error below 0.15m/s, averages of pitch error and roll error below 0.03°, and average of yaw error below 0.03°. And errors of compensated velocity and attitude are stable and convergent. Unaided navigation position error compensated by the NN is below 8m in 60s and below 17m in 100s, which shows that the NN model can effectively restain the divergence of position error.
(6) The principle of MHS is studied. A de-noising method and an anomaly detector are presented respectively to de-noise and detect the signal of a low-cost MHS. Considering the problem of MHS measurements including a lot of noises, lifting Wavelet is used in de-noising MHS measurements. MHS measurements could be anomaly due to magnetic disturbance from external interferences. Toward the anomaly measurements of MHS, lifting Wavelet is applied into detecting the anomaly signal. Experiment results show that lifting Wavelet could effectively de-noise the MHS measurements and detect the anomaly signal. After eliminating the anomaly measurements, the revised measurements could improve the usability of MHS heading.
(7) According to the characters of integrated navigation, a neural network is bringed forward to compensate the error of a low-cost MHS. The error sources of MHS are studied and the compensation methods are analyzed. When the Global Positioning System(GPS) is available, a multilayer feedforward neural network is trained to compensate MHS by the learning method of kalman filter and with the reference of SINS/GPS integrated navigation result. Experiment results show that the neural network can make a significant effect and reduce the heading error of MHS from±15? to±1?.
Baesd on the integration hardware system of flexible gyroscopes, this dissertation majors in researching the way to improve SINS’precision and to reduce system cost. And also the methods of this dissertation are suitable for other medium-precision SINS, such as the SINS based on fiber optical gyros.
本文的研究对象为适于惯性制导的中等精度捷联惯导系统,研究目的在于提高捷联惯导系统的精度,同时降低成本;研究的主要内容为捷联惯导系统的几项关键技术,包括数据采集技术、陀螺的标定与补偿技术、初始对准技术和磁航向系统的校正与补偿等。主要工作包括以下几个方面:
(1)研究了捷联惯导系统的构成原理,采用国产挠性陀螺仪和石英挠性加速度计设计了惯性测量单元。研究了目前导航计算机的研制方案,设计了以DSP和CPLD为核心的导航计算机。研究了数据采集技术和惯性仪表的数据采集电路,分别设计了高精度、低成本的流水线A/D和Σ-Δ型A/D多路并行采集、并行滤波数据采集电路。
(2)研究了陀螺刻度系数非线性问题,设计了分段非线性插值标定算法,提出了陀螺刻度系数神经网络标定方法和神经网络再训练方法。分段非线性插值标定算法在整个区间上细化陀螺的刻度系数,减小了非线性误差,与一次拟合法和分段法相比,非线性误差分别减小约6.2倍和2.4倍;实验结果表明神经网络与分段非线性插值算法的标定结果接近,说明神经网络标定方法是可行的。对神经网络模型进行再训练,非线性误差减小了约1.7倍,可提高神经网络对刻度系数非线性的逼近程度,进一步减小非线性误差。
(3)研究了捷联惯导系统的自动化标定技术。利用转台系统,不增加硬件设备,通过设计监控计算机与导航计算机之间的通信协议,建立串口通信机制,实现了捷联惯导系统标定过程的流程控制、数据采集、数据记录、标定计算和标定参数装订的自动化,标定的中间过程不需要人员参与。
(4)捷联惯导系统的测量信号中包含大量随机噪声,降低了初始对准的精度。提出将提升小波算法应用于惯性仪表测量信号去噪和初始对准中,实验结果表明该方法可以有效滤除信号中的随机噪声,能够提高初始对准的速度和精度;相比于小波算法,提升小波算法在测量信号滤波中能够缩短将近一半时间,更适于实时、在线应用。
(5)研究了适用于捷联惯导系统的传递对准技术,提出了神经网络传递对准方法。用子惯导的速度和姿态信息作为输入,以主、子惯导的速度和姿态误差作为参考信号,训练神经网络补偿子惯导的速度和姿态。仿真实验表明,神经网络传递对准方法对子惯导速度和姿态均有很好的补偿效果;并且研究了不同神经元对子惯导补偿效果的影响,确定了具有6个神经元的最佳网络结构。通过跑车实验,验证了神经网络方法对速度和姿态均有比较好的补偿效果,速度补偿误差均值小于0.15m/s,俯仰角和滚动角补偿误差均值小于0.03°,对航向角的补偿误差均值小于0.03°,补偿的速度和姿态误差能够稳定,不会发散。经神经网络补偿后60s的位置误差小于8m,100s的位置误差小于17m,表明神经网络补偿能够起到抑制位置误差发散的效果。
(6)研究了低成本磁航向系统的工作原理,设计了磁航向系统信号去噪和异常检测方法。针对其测量信号中包含大量噪声的问题,提出了应用提升小波去除磁航向系统测量噪声的方法;由于外部干扰磁场的存在,磁航向系统会出现测量异常情况,针对其测量异常,提出应用提升小波对异常进行检测的方法。实验结果表明,提升小波可有效地去除磁航向系统的测量噪声;并能有效的检测出磁航向系统的测量异常;校正后的数据可增加磁航向系统航向角的可用性。
(7)研究了磁航向系统误差补偿技术,结合捷联惯导系统的特点,提出以捷联惯导系统的航向信息为参考,训练神经网络补偿磁航向系统的误差。实质上,神经网络的补偿作用是实现磁航向系统跟踪捷联惯导系统的航向,实现两者之间的映射。实验结果表明,神经网络补偿方法将磁航向系统的航向角误差由±15?减小到约±1?,取得了明显的效果。
本文基于挠性陀螺集成的硬件系统,主要研究提高SINS精度方法,所研究的方法也适用于光纤陀螺等其他中等精度的SINS。
Guided munitions are increasing widely applied in high-tech modern wars. Navigation and guidance system is one of the keys of Guided munitions. With the development of satellite navigation system(SNS), SNS/SINS(strapdown inertial navigatoion system) integrated navigation has been the leading development direction. But SNS is likely to be unused because of outside disturbance. Toward SNS, the proper way is to use but not depend. SINS has undisturbed and autonomous navigation capacity. Therefore, SINS is very significant for military application with certain precision.
Taking medium-precision SINS suitable for inertial guidance as object, this dissertation intends to improve navigation precision and reduce the cost of SINS. Main contents are about the key technologies of SINS, including data-sampling, gyro’s calibration and compensation, SINS alignment, compensation of magnetic heading system(MHS). The details of main contents are the following aspects:
(1) Based on the introduced principles and developments of SINS, inertial instrument, navigation computer and data-sampling technology, we design a SINS including an inertial measurement unit(IMU) with domestic flexibility gyroscopes and quartz accelerometers, a navigation computer system with the core of digital signal processor(DSP) and complex programmable logical device(CPLD), and two high-precision and low-cost data-sampling systems with pipeline A/D chips and sigma-delta A/D chips respectively.
(2) Toward SINS, the calibration methods of piecewise nonlinear interpolating(PNIP) and multilayer feedforward neural network(MLFN) are put forward to compensate the nonlinear error of gyroscope’s scale factor. The PNIP can subdivide the scale factor and minish nonlinear error about 6.2 times and 2.4 times compared with least square fitting and piecewise compensation method respectively. Experiment results show that the calibration result of scale factor using MLFN is close to that of PNIP, and also indicate that MLFN is feasible. A retraining method is proposed to improve the performance of the trained MLFN. Results show that the retrained MLFN can much more approach the real scale factor and minish the nonlinear error about 1.7 times compared with the unrestrained MLFN.
(3) In order to meet the requirement of SINS’batch production, an automatic calibration and test method is designed. Considering SINS’characters, we design the communication protocol and realize serial communication mechanism between monitor computer and SINS. Based on a turntable system and utilizing the serial communication mechanism, the calibration process of SINS is automatically realized without adding any hardware equipment. The automatic process includes calibration flow control, data record, calibration result computation and parameters storage.
(4) Considering noisy data of inertial instruments which could depress SINS alignment precision, lifting wavelet is applied into the signal de-noising of inertial instruments and SINS coarse alignment. Results show that lifting wavelet algorithm is effective to the signal de-noising of inertial instruments. Also it can improve the alignment precision of SINS. Compared with classic wavelet, lifting wavelet can save almost half computation time and is more suitable to real-time and online application.
(5) Based on the introduction of transfer alignment methods, a neural network(NN) transfer alignment method is put forward to compensate the errors of salve inertial navigation system(INS). In the NN model, NN inputs are the velocity and attitude of salve INS, and NN output references are the errors of velocity and attitude between master INS and slave INS. Simulation results show that the NN model can make satisfactory compensation to the velocity and attitude of slave INS. Also NN models with deferent neurons could have deferent compensation effect, and we ascertain the best NN architecture with 6 neurons by simulation experiments. Vehicle test results show that NN transfer alignment method has effective compensation to velocity and attitude of slave INS with average of velocity error below 0.15m/s, averages of pitch error and roll error below 0.03°, and average of yaw error below 0.03°. And errors of compensated velocity and attitude are stable and convergent. Unaided navigation position error compensated by the NN is below 8m in 60s and below 17m in 100s, which shows that the NN model can effectively restain the divergence of position error.
(6) The principle of MHS is studied. A de-noising method and an anomaly detector are presented respectively to de-noise and detect the signal of a low-cost MHS. Considering the problem of MHS measurements including a lot of noises, lifting Wavelet is used in de-noising MHS measurements. MHS measurements could be anomaly due to magnetic disturbance from external interferences. Toward the anomaly measurements of MHS, lifting Wavelet is applied into detecting the anomaly signal. Experiment results show that lifting Wavelet could effectively de-noise the MHS measurements and detect the anomaly signal. After eliminating the anomaly measurements, the revised measurements could improve the usability of MHS heading.
(7) According to the characters of integrated navigation, a neural network is bringed forward to compensate the error of a low-cost MHS. The error sources of MHS are studied and the compensation methods are analyzed. When the Global Positioning System(GPS) is available, a multilayer feedforward neural network is trained to compensate MHS by the learning method of kalman filter and with the reference of SINS/GPS integrated navigation result. Experiment results show that the neural network can make a significant effect and reduce the heading error of MHS from±15? to±1?.
Baesd on the integration hardware system of flexible gyroscopes, this dissertation majors in researching the way to improve SINS’precision and to reduce system cost. And also the methods of this dissertation are suitable for other medium-precision SINS, such as the SINS based on fiber optical gyros.
引文
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