WO2019242336A1 - 一种水下滑翔器导航定位系统及上浮精度校正方法 - Google Patents

一种水下滑翔器导航定位系统及上浮精度校正方法 Download PDF

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WO2019242336A1
WO2019242336A1 PCT/CN2019/077887 CN2019077887W WO2019242336A1 WO 2019242336 A1 WO2019242336 A1 WO 2019242336A1 CN 2019077887 W CN2019077887 W CN 2019077887W WO 2019242336 A1 WO2019242336 A1 WO 2019242336A1
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glider
navigation
matrix
state
positioning
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PCT/CN2019/077887
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English (en)
French (fr)
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陈熙源
王俊玮
杨萍
方琳
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东南大学
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Priority to US17/040,543 priority Critical patent/US11927446B2/en
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
    • G01C21/10—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration
    • G01C21/12—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning
    • G01C21/16—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning by integrating acceleration or speed, i.e. inertial navigation
    • G01C21/165—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning by integrating acceleration or speed, i.e. inertial navigation combined with non-inertial navigation instruments
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B63—SHIPS OR OTHER WATERBORNE VESSELS; RELATED EQUIPMENT
    • B63B—SHIPS OR OTHER WATERBORNE VESSELS; EQUIPMENT FOR SHIPPING 
    • B63B79/00—Monitoring properties or operating parameters of vessels in operation
    • B63B79/10—Monitoring properties or operating parameters of vessels in operation using sensors, e.g. pressure sensors, strain gauges or accelerometers
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B63—SHIPS OR OTHER WATERBORNE VESSELS; RELATED EQUIPMENT
    • B63G—OFFENSIVE OR DEFENSIVE ARRANGEMENTS ON VESSELS; MINE-LAYING; MINE-SWEEPING; SUBMARINES; AIRCRAFT CARRIERS
    • B63G8/00—Underwater vessels, e.g. submarines; Equipment specially adapted therefor
    • B63G8/001—Underwater vessels adapted for special purposes, e.g. unmanned underwater vessels; Equipment specially adapted therefor, e.g. docking stations
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
    • G01C21/20—Instruments for performing navigational calculations
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
    • G01S19/38—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system
    • G01S19/39—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system the satellite radio beacon positioning system transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
    • G01S19/393—Trajectory determination or predictive tracking, e.g. Kalman filtering
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
    • G01S19/38—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system
    • G01S19/39—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system the satellite radio beacon positioning system transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
    • G01S19/42—Determining position
    • G01S19/48—Determining position by combining or switching between position solutions derived from the satellite radio beacon positioning system and position solutions derived from a further system
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
    • G01S19/38—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system
    • G01S19/39—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system the satellite radio beacon positioning system transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
    • G01S19/42—Determining position
    • G01S19/48—Determining position by combining or switching between position solutions derived from the satellite radio beacon positioning system and position solutions derived from a further system
    • G01S19/49—Determining position by combining or switching between position solutions derived from the satellite radio beacon positioning system and position solutions derived from a further system whereby the further system is an inertial position system, e.g. loosely-coupled
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B63—SHIPS OR OTHER WATERBORNE VESSELS; RELATED EQUIPMENT
    • B63B—SHIPS OR OTHER WATERBORNE VESSELS; EQUIPMENT FOR SHIPPING 
    • B63B2213/00—Navigational aids and use thereof, not otherwise provided for in this class
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B63—SHIPS OR OTHER WATERBORNE VESSELS; RELATED EQUIPMENT
    • B63G—OFFENSIVE OR DEFENSIVE ARRANGEMENTS ON VESSELS; MINE-LAYING; MINE-SWEEPING; SUBMARINES; AIRCRAFT CARRIERS
    • B63G8/00—Underwater vessels, e.g. submarines; Equipment specially adapted therefor
    • B63G8/001—Underwater vessels adapted for special purposes, e.g. unmanned underwater vessels; Equipment specially adapted therefor, e.g. docking stations
    • B63G2008/002—Underwater vessels adapted for special purposes, e.g. unmanned underwater vessels; Equipment specially adapted therefor, e.g. docking stations unmanned
    • B63G2008/004—Underwater vessels adapted for special purposes, e.g. unmanned underwater vessels; Equipment specially adapted therefor, e.g. docking stations unmanned autonomously operating

Definitions

  • the invention belongs to the field of navigation technology, and relates to a navigation positioning method of a hydro glider, in particular to a method for correcting the accuracy of a glider navigation system.
  • the method satisfies the calibration of a glider in extreme conditions.
  • the accuracy still meets certain requirements, improving the robustness and adaptability of the navigation system, and finally achieving accurate positioning of the glider.
  • Water gliders are an important tool for marine exploration and resource development.
  • the movement of the glider itself is not violent, so the low-cost, low-power consumption, long-hour MEMS-initial measurement unit (MEMS-initial measurement unit) is sufficient to meet the navigation and positioning requirements.
  • MEMS-initial measurement unit MEMS-initial measurement unit
  • the glider's long-term underwater work and interference conditions including ocean current surge, magnetic field interference, etc.
  • GPS global positioning system
  • data fusion algorithms to correct their positioning.
  • GPS global positioning system
  • it will be affected by factors such as bad weather, wave fluctuations, and ship occlusion, which will reduce the navigation accuracy of the multi-sensor data fusion algorithm, and make the positioning divergent or even invalid.
  • the purpose of the present invention is to provide a navigation and positioning system for a glider and a correction method for the accuracy of the float
  • the multi-sensor data fusion algorithm is used to ensure the robustness and adaptability of the glider navigation and positioning to overcome the decrease in navigation and positioning accuracy caused by the traditional integrated navigation algorithm when the glider in the extreme harsh conditions is corrected.
  • a hydro glider navigation and positioning system includes a micro-electromechanical system inertial measurement unit (MEMS-IMU), a global satellite positioning system receiving module (GPS), a three-axis magnetometer, a Doppler velocimeter (DVL), and a combination Navigation hardware processing system.
  • MEMS-IMU integrates a three-axis accelerometer and a three-axis gyroscope, and the output three-axis acceleration and angular rate information obtains the attitude, speed and position navigation information of the hydro glider through an inertial navigation algorithm; the three-axis magnetometer It is used to correct the heading information of the glider.
  • the Doppler velocimeter is used to judge the movement state of the glider.
  • the GPS is used in the glider upward correction operation to form GPS / INS position and speed correction with the MEMS-IMU.
  • Loose integrated navigation system using integrated navigation filter algorithm to correct the speed and position error of the glider; the integrated navigation hardware processing system is used for the three-axis magnetometer, GPS, MEMS-IMU, and DVL signal reception processing and clock synchronization Work, and the algorithmic solution work of integrated navigation multi-sensor information.
  • the navigation and positioning system of the hydro glider has two operating states: an underwater working state and a floating correction state; if the navigation positioning error is too large, the glider stops working in real time and switches from the underwater working state to the floating correction State; when the speed / position error of the GPS / INS integrated navigation system is less than the set threshold, the glider switches from the floating correction state to the underwater working state and continues to work.
  • the signals from the MEMS-IMU and the triaxial magnetometer are used for underwater work, and the quaternion algorithm based on complementary filtering is used to complete the navigation and positioning of the glider.
  • the floating correction uses signals output by GPS and MEMS-IMU, and uses a loose integrated navigation system with GPS / INS position and speed correction to complete the navigation and positioning of the glider.
  • the integrated navigation filter algorithm uses a H ⁇ Kalman filtering algorithm, the state prediction covariance matrix of the algorithm H ⁇ filter gain matrix H ⁇ filtered state optimal covariance matrix
  • k and k-1 represent the current time and the previous time respectively, ⁇ k
  • k-1 is the state transition matrix, H k is the observation matrix, Q k-1 is the system noise covariance, and Sk is the multiple adaptive gradient.
  • a factorial matrix, L k is an estimate of a linear combination of system state quantities, I is a unit matrix, ⁇ is an adaptive threshold.
  • the multiple adaptive fading factor matrix S k diag (s 1, s 2, s 3 ..., s n) is calculated by the following formula:
  • ⁇ i is the ith observation element of the observation matrix H k and j ii (k) is the matrix I diagonal element, ⁇ i is the threshold of chi-square detection, ⁇ i (k) is the i-th observation element of the innovation matrix V k , and b ii (k) is the matrix The i-th diagonal element.
  • the adaptive threshold ⁇ ⁇ ⁇ ⁇ a ; wherein, Trace () represents the matrix trace operation, and ⁇ () represents the spectral radius of the matrix.
  • An ascending accuracy correction method for a navigation and positioning system of a glider includes the following steps:
  • the glider After the glider floats to the surface, it receives GPS latitude, longitude, altitude, and three-axis speed signals, three-axis acceleration of the IMU, and three-axis angular rate signals.
  • the H ⁇ Kalman filtering algorithm based on multiple adaptive fade factors is applied. Loose integrated navigation system for GPS / INS position and speed correction to complete data fusion;
  • step (2) the speed and position error of the integrated navigation system is gradually reduced to zero, and if the error is less than a set threshold, the ascent correction work is considered complete and the glider is switched to the underwater working state.
  • the glider navigation and positioning system of the present invention uses the low-cost inertial MEMS-IMU and triaxial magnetometer to complete the attitude, speed, and position calculation of the glider when the glider is under water
  • the high-precision GPS module and inertial MEMS-IMU components are used to perform multi-sensor data fusion to reduce the navigation and positioning error of the glider.
  • the conditions and requirements for switching between these two states of the glider can be set appropriately through software and hardware according to the specific work and task. Due to the accuracy correction of the glider, the influence of external uncertain interference on it is unknown, and the statistical characteristics of noise and the system model are time-varying.
  • the H ⁇ Kalman filter algorithm based on multiple adaptive fade factors used in the present invention is a multi-sensor data fusion algorithm that can adapt to time-varying conditions, and can ensure that the stability and accuracy of the floating correction meet the requirements.
  • the invention overcomes the problems that the accuracy of navigation positioning caused by the traditional integrated navigation algorithm decreases and diverges during the upward correction of the water glider under extreme conditions, so that the glider cannot continue to work normally. Finally, accurate positioning of the water glider is achieved.
  • FIG. 1 is a schematic diagram of a structure of a navigation and positioning system of a hydro glider and a state transition diagram of underwater work and correction of a float according to an embodiment of the present invention.
  • FIG. 2 is a block diagram of a loose integrated navigation system for GPS / INS position and speed correction according to an embodiment of the present invention.
  • FIG. 3 is a flowchart of a H ⁇ Kalman filtering algorithm based on multiple adaptive fading factors based on the correction state of the hydro glider in the embodiment of the present invention.
  • a hydro glider navigation system disclosed in the present invention mainly includes a micro-electromechanical system inertial measurement unit (MEMS-IMU), a global satellite positioning system receiving module (GPS), a three-axis magnetometer, and more Pooler Velocimeter (DVL), Digital Signal Processing Module (DSP), Advanced Reduced Instruction Set Microprocessor (ARM).
  • MEMS-IMU micro-electromechanical system inertial measurement unit
  • GPS global satellite positioning system receiving module
  • DSP Digital Signal Processing Module
  • ARM Advanced Reduced Instruction Set Microprocessor
  • the MEMS-IMU integrates a three-axis accelerometer and a three-axis gyroscope.
  • the output of the three-axis acceleration and angular rate information obtains the attitude, speed, and position navigation information of the hydro glider through the inertial navigation algorithm.
  • data is fused with GPS signals to correct navigation and positioning errors.
  • the triaxial magnetometer is used in the complementary filtering algorithm to correct the heading information of the glider. At the same time, if the heading change is too large, it reflects that the magnetic field interference is strong, and the floating accuracy correction operation needs to be performed.
  • the Doppler velocimeter is used to judge the movement state of the hydro glider. If the speed changes too much, it reflects that the glider is greatly affected by the ocean current surge, and it is also necessary to perform a floating accuracy correction operation.
  • GPS is used in the glider upward correction operation. It uses the designed H ⁇ Kalman filter algorithm based on multiple adaptive fading factors to fuse with IMU data to correct the speed and position errors of the glider.
  • ARM and DSP constitute the integrated navigation hardware processing system of the water glider. Under water working condition, ARM is used for receiving and processing of magnetometer, IMU and DVL signals to work synchronously with the clock. DSP is used to solve the inertial navigation quaternion algorithm. In the floating correction state, the ARM is used for GPS and IMU signal receiving processing and clock synchronization work. DSP is used for H ⁇ Kalman filter algorithm based on multiple adaptive fade factors.
  • the invention discloses a method for correcting the floating accuracy of a navigation and positioning system for a glider, which includes the following steps:
  • the glider After the glider floats to the surface, it receives GPS latitude, longitude, altitude, and three-axis speed signals, three-axis acceleration of the IMU, and three-axis angular rate signals through the ARM + DSP integrated navigation hardware processing system.
  • the DSP performs data fusion on the received multi-sensor signals.
  • a H ⁇ Kalman filter algorithm based on multiple adaptive fade factors is designed. This algorithm is applied to the loose integrated navigation system of GPS / INS position and speed correction as shown in Figure 2 to complete the data fusion.
  • the IMU outputs tri-axial acceleration and angular rate information, and the position, velocity, and attitude angle of the glider are calculated by the inertial navigation system.
  • the GPS system directly outputs the position and speed of the glider.
  • the difference between the information (position, velocity) output by the GPS system and the INS system alone is used as an external observation input for the integrated navigation filter.
  • the optimal state quantities (position and velocity errors) of the output integrated navigation system are used to correct the navigation position and velocity containing various noise errors (the attitude angle cannot be corrected in the loose combination mode).
  • x k ⁇ k
  • x k is the system state amount at time k
  • k-1 is the state transition matrix
  • w k-1 is the system noise
  • z k is the system observation measurement at k time
  • H k is the observation matrix
  • v k-1 is Observation noise.
  • the noise expectation and covariance are:
  • V k z k -H k x k
  • k-1 is the state prediction covariance matrix
  • K k is the gain matrix, which is a gain coefficient obtained by integrating the uncertainty of the state space and the observation space. The gain coefficient determines the external observation measurement to the entire system Correction capability.
  • V k is the innovation matrix, which represents the difference between the external observation z k and the state quantity x k
  • Sk is only a Sk fading factor matrix. It is used to solve the problem that the mathematical model established by the research object cannot truly reflect the actual physical process and the lack of understanding of the statistical characteristics of the system noise, the model does not match the measured value obtained, which may easily cause the filter divergence.
  • the adaptive fading factor matrix Sk is obtained by the following algorithm:
  • ⁇ i is the ith observation element of the observation matrix H k and j ii (k) is the matrix I-th diagonal element
  • ⁇ i is the threshold for chi-square detection
  • ⁇ i (k) is the i-th observation element of the residual matrix V k
  • b ii (k) is the matrix The i-th diagonal element.
  • L k is an estimate of a linear combination of system state quantities, which is often taken as I, and y k represents a direct estimation of a state quantity x k .
  • N is the overall filtering time limit
  • x 0 is the initial state of the system, and the rest have been introduced.
  • the central idea of H ⁇ filtering is to ensure that the H ⁇ norm of the cost function J is minimized, and that the maximum energy gain from the interference signal to the estimation error is minimized. Therefore, the influence of external disturbances and model uncertainties on the system output is minimized.
  • the H ⁇ norm of the constructed algebraic function J may be less than ⁇ .
  • P k is the optimal covariance estimate of the system state quantity x k .
  • the robust performance of the filter is related to the selected threshold ⁇ .
  • ⁇ cannot be less than ⁇ 0 , otherwise there is no solution to the H ⁇ suboptimal problem, which will cause the filter to diverge.
  • ⁇ approaches infinity, the H infinite filter degenerates into a traditional kalman filter.
  • the conventional threshold ⁇ is generally determined based on actual glider working experience and cannot be changed, so that the filtering effect is more conservative. It is impossible to guarantee that the estimation error of the system is small and the system has high robustness in real time.
  • the adaptive algorithm for establishing the threshold ⁇ according to the Ricatti inequality is as follows:
  • ⁇ () represents the spectral radius of the matrix.
  • steps 1 and 2 are combined to form an integrated navigation algorithm (H ⁇ Kalman filter algorithm based on multiple adaptive fading factors) for the overall accuracy correction of the floating glider.
  • the algorithm flow chart is shown in Figure 3.
  • the integrated navigation filter is divided into three loops (filter loop, gain loop, and fade loop) to solve.
  • the specific process is as follows:
  • the speed and position error of the integrated navigation system is gradually reduced to zero by an H ⁇ Kalman filter algorithm based on multiple adaptive fade factors. If the error is less than a set threshold, it can be considered that the ascent correction work is completed and the glider can be switched to the underwater working state.

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Abstract

一种水下滑翔器导航定位系统及上浮精度校正方法，该水下滑翔器导航定位系统包括微机电系统惯性测量单元，全球卫星定位系统接收模块，三轴磁力计，多普勒测速仪以及组合导航硬件处理系统。针对上浮精度校正过程中，会受到外界恶劣天气、海浪波动、船舰遮挡等因素的影响，使得多传感器数据融合算法的导航精度降低，定位发散甚至失效的问题，建立了一种基于多重自适应渐消因子的H∞卡尔曼滤波算法来保证滑翔器导航定位的鲁棒性和自适应性，克服了极端恶劣条件下的水下滑翔器在上浮校正时，传统组合导航算法所导致的导航定位精度降低、发散以至于滑翔器无法继续正常工作的问题，最终实现水下滑翔器的准确定位。

Description

一种水下滑翔器导航定位系统及上浮精度校正方法 技术领域
本发明属于导航技术领域，涉及了水下滑翔器的导航定位方法，具体是一种水下滑翔器导航系统的上浮精度校正方法，该方法满足在极端恶劣情况下，水下滑翔器的上浮校准精度仍达到一定要求，提高导航系统的鲁棒性和自适应性，最终实现水下滑翔器的准确定位。
背景技术
水下滑翔器是一种用于海洋探索和资源开发的重要工具。水下滑翔器工作过程中本身运动并不剧烈，从而采用低成本、低功耗、长航时的微机电系统惯性测量单元(MEMS-initial measurement unit)足以符合导航定位要求。但滑翔器长时间的水下工作以及在干扰条件下(包括洋流涌动、磁场干扰等)，会产生较大的定位累积误差，因此滑翔器需要上浮至水面，利用高精度的全球定位系统(GPS)信号以及数据融合算法对其定位进行校正。但是上浮精度校正过程中，会受到外界恶劣天气、海浪波动、船舰遮挡等因素的影响，使得多传感器数据融合算法的导航精度降低，定位发散甚至失效。
因此，如何在极端恶劣条件下，保证水下滑翔器上浮精度校正工作的稳定性和精确性成为了水下滑翔器准确定位的重要问题。
发明内容
发明目的：针对滑翔器处于上浮校正状态时，可能受到外界恶劣天气、海浪波动、船舰遮挡等因素的影响的问题，本发明目的在于提供一种水下滑翔器导航定位系统及上浮精度校正方法，通过多传感器数据融合算法来保证滑翔器导航定位的鲁棒性和自适应性，以克服极端恶劣条件下的水下滑翔器在上浮校正时，传统组合导航算法所导致的导航定位精度降低、发散以至于滑翔器无法继续正常工作的问题，最终实现水下滑翔器的准确定位。
技术方案：为实现上述发明目的，本发明采用如下技术方案：
一种水下滑翔器导航定位系统，包括：微机电系统惯性测量单元(MEMS-IMU)，全球卫星定位系统接收模块(GPS)，三轴磁力计，多普勒测速 仪(DVL)，以及组合导航硬件处理系统。所述MEMS-IMU集成了三轴加速度计和三轴陀螺仪，输出的三轴加速度和角速率信息通过惯性导航算法得到水下滑翔器的姿态、速度和位置导航信息；所述三轴磁力计用于校正滑翔器的航向信息，所述多普勒测速仪用于判断水下滑翔器的运动状态；所述GPS用于滑翔器上浮校正操作中，与MEMS-IMU组成GPS/INS位置速度校正的松组合导航系统，利用组合导航滤波器算法，校正滑翔器的速度、位置误差；所述组合导航硬件处理系统用于三轴磁力计、GPS、MEMS-IMU以及DVL信号的接收处理与时钟同步工作，以及组合导航多传感器信息的算法解算工作。
作为优选，所述水下滑翔器导航定位系统具有两种运行状态：水下工作状态和上浮校正状态；若导航定位误差过大，则滑翔器停止实时工作，由水下工作状态切换为上浮校正状态；当GPS/INS组合导航系统的速度、位置误差小于设定阈值时，则滑翔器由上浮校正状态切换为水下工作状态，继续工作。
作为优选，水下工作利用MEMS-IMU和三轴磁力计输出的信号，采用基于互补滤波的四元数算法完成滑翔器的导航定位。
作为优选，上浮校正利用GPS和MEMS-IMU输出的信号，采用GPS/INS位置速度校正的松组合导航系统，完成滑翔器的导航定位；其中组合导航滤波器算法采用基于多重自适应渐消因子的H∞卡尔曼滤波算法，该算法的状态预测协方差阵
Figure PCTCN2019077887-appb-000001
H∞滤波增益阵
Figure PCTCN2019077887-appb-000002
H∞滤波状态最优协方差阵
Figure PCTCN2019077887-appb-000003
式中k和k-1分别表示当前时刻和前一时刻，Φ k|k-1为状态转移矩阵，H k为观测矩阵，Q k-1为系统噪声协方差，S k为多重自适应渐消因子阵，L k为对系统状态量的线性组合的估计，I为单位阵，
Figure PCTCN2019077887-appb-000004
γ为自适应阈值。
作为优选，所述多重自适应渐消因子阵S k＝diag(s 1,s 2,s 3...,s n)由下式计算：
Figure PCTCN2019077887-appb-000005
式中，λ i为观测阵H k第i个观测元素，j ii(k)为矩阵
Figure PCTCN2019077887-appb-000006
的第i个对角线元素,ε i为卡方检测的阈值，υ i(k)为新息阵V k第i个观测元素，b ii(k)为矩阵
Figure PCTCN2019077887-appb-000007
的第i个对角线元素。
作为优选，所述自适应阈值γ＝η·γ a；其中，
Figure PCTCN2019077887-appb-000008
Trace()表示矩阵求迹运算，ρ()表示矩阵的谱半径。
一种用于水下滑翔器导航定位系统的上浮精度校正方法，包含如下步骤：
(1)判断水下滑翔器是否需要进行上浮精度校正操作，当航向变化过大、速度变化过大，或人为判定滑翔器导航定位误差过大时，滑翔器上浮将至水面，进行精度校正操作；。
(2)滑翔器上浮至水面后，接收GPS的经纬度、高度、三轴速度信号，IMU的三轴加速度，三轴角速率信号，将基于多重自适应渐消因子的H∞卡尔曼滤波算法应用于GPS/INS位置速度校正的松组合导航系统，完成数据融合；
(3)通过步骤(2)使得组合导航系统的速度、位置误差逐渐减小、趋于零，若误差小于一设定的阈值，则认为上浮校正工作完成，滑翔器切换至水下工作状态。
有益效果：本发明的水下滑翔器导航定位系统在滑翔器处于水下工作状态时，利用低成本的惯性MEMS-IMU以及三轴磁力计完成水下滑翔器的姿态、速度、位置解算，在滑翔器处于上浮校正状态时，利用高精度的GPS模块与惯性MEMS-IMU元件进行多传感器的数据融合，减小滑翔器的导航定位误差。水下滑翔器这两种状态的切换条件与要求可根据具体工作和任务通过软硬件进行合适的设定。由于，滑翔器上浮精度校正过程中，外界不确定性干扰对其的影响是未知的，同时噪声统计特征和系统模型是时变的。本发明采用的基于多重自适应渐 消因子的H∞卡尔曼滤波算法，是能自适应于时变条件下的多传感器数据融合算法，能保证上浮校正的稳定性和精确性满足要求。本发明克服了极端恶劣条件下的水下滑翔器在上浮校正时，传统组合导航算法所导致的导航定位精度降低、发散以至于滑翔器无法继续正常工作的问题。最终实现水下滑翔器的准确定位。
附图说明
图1是本发明实施例的水下滑翔器导航定位系统结构及水下工作与上浮校正状态转换示意图。
图2是本发明实施例中GPS/INS位置速度校正的松组合导航系统框图。
图3是本发明实施例中水下滑翔器上浮校正状态的基于多重自适应渐消因子的H∞卡尔曼滤波算法流程图。
具体实施方式
以下结合具体实施例和附图对本发明进行进一步说明：
如图1所示，本发明公开的一种水下滑翔器导航系统，主要包括：微机电系统惯性测量单元(MEMS-IMU)，全球卫星定位系统接收模块(GPS)，三轴磁力计，多普勒测速仪(DVL)，数字信号处理模块(DSP)，高级精简指令集微处理器(ARM)。
MEMS-IMU集成了三轴加速度计和三轴陀螺仪，输出的三轴加速度和角速率信息通过惯性导航算法得到水下滑翔器的姿态、速度、位置导航信息。同时在上浮精度校正时，与GPS信号进行数据融合，以校正导航定位误差。
三轴磁力计用于互补滤波算法中，校正滑翔器的航向信息。同时若航向变化过大，则反映磁场干扰较强，需要进行上浮精度校正操作。
多普勒测速仪用于判断水下滑翔器的运动状态，若速度变化过大，则反映滑翔器受洋流涌动影响较大，也需要进行上浮精度校正操作。
GPS用于滑翔器上浮校正操作中，利用设计的基于多重自适应渐消因子的H∞卡尔曼滤波算法，同IMU数据进行融合，校正滑翔器的速度、位置误差。
ARM和DSP构成了水下滑翔器的组合导航硬件处理系统。水下工作状态，ARM用于磁力计、IMU以及DVL信号的接收处理与时钟同步工作。DSP用于惯性导航四元数算法的解算工作。上浮校正状态，ARM用于GPS、IMU信号的接收处理与时钟同步工作。DSP用于基于多重自适应渐消因子的H∞卡尔曼滤波算法 解算工作。
本发明公开的一种用于水下滑翔器导航定位系统的上浮精度校正方法，包含以下步骤：
(1)当水下滑翔器导航系统处于水下工作状态时，若系统启用MEMS-IMU和磁力计模块，采用基于互补滤波的四元数算法计算滑翔器的姿态、速率和位置。四元数基本算法可以参见秦永元教授所编《惯性导航》一书，互补滤波理论可以参见陈熙源教授所写的发明专利《一种基于互补滤波的水下滑翔器节能算法》。
(2)判断水下滑翔器是否需要进行上浮精度校正操作。水下滑翔器工作过程中本身运动并不剧烈，若根据上述四元数算法计算得到的航向变化过大(磁场干扰较强)、速度变化过大(洋流涌动较大)，或人为判定滑翔器导航定位误差过大时，滑翔器上浮将至水面，进行精度校正操作。
(3)滑翔器上浮将至水面后，通过ARM+DSP组合导航硬件处理系统接收GPS的经纬度、高度、三轴速度信号，IMU的三轴加速度，三轴角速率信号。DSP对接收到的多传感器信号进行数据融合。在保证MEMS-IMU和GPS可靠性和精度的基础上，采用设计的一种基于多重自适应渐消因子的H∞卡尔曼滤波算法。将该算法应用于如图2所示的GPS/INS位置速度校正的松组合导航系统，以完成数据融合。在滑翔器上浮校正过程中，IMU输出三轴加速度和角速率信息，通过惯性导航系统解算得到滑翔器的位置、速率、姿态角。GPS系统直接输出滑翔器的位置、速率。将GPS系统与INS系统单独输出的信息(位置、速率)作差作为外部观测量输入组合导航滤波器。通过滤波器不断的迭代，输出组合导航系统的最优状态量(位置、速率误差)用于修正包含各种噪声误差的导航位置和速率(姿态角无法在松组合模式下，进行修正)。
上浮精度校正中，采用的一种基于多重自适应渐消因子的H∞卡尔曼滤波算法流程图如图3所示，具体步骤如下：
①自适应渐消因子建立
将组合导航系统考虑为一个线性动力学系统
x k＝Φ k|k-1x k-1+w k-1
z k＝H kx k+v k-1
其中，x k为k时刻系统状态量，Φ k|k-1为状态转移矩阵，w k-1为系统噪声； z k为k时刻系统观测量，H k为观测矩阵，v k-1为观测噪声。同时，噪声的期望和协方差为：
E(w k-1)＝0,var(w k-1)＝Q k-1
E(v k-1)＝0,var(v k-1)＝R k-1
自适应渐消因子卡尔曼滤波算法参见Yanrui Geng的《Adaptive estimation of multiple fading factors in Kalman filter for navigation applications》一作。写出具体的自适应渐消因子卡尔曼滤波方程：
x k＝Φ k|k-1x k-1
Figure PCTCN2019077887-appb-000009
Figure PCTCN2019077887-appb-000010
V k＝z k-H kx k|k-1
x k＝x k|k-1+K kV k
P k＝(I-K kH k)P k|k-1
式中，P k|k-1为状态预测协方差矩阵，K k为增益阵，为综合状态空间和观测空间的不确定度得到的一个增益系数，该增益系数决定了外部观测量对整个系统的校正能力。V k为新息矩阵，代表外部观测量z k与系统预测的状态量x k|k-1的差异大小。与标准卡尔曼滤波方程相比，仅多了S k渐消因子矩阵。用于解决对研究对象所建立的数学模型不能真实反映实际的物理过程以及缺乏对系统噪声统计特性的了解时，建模与获得的量测值存在不匹配情况，容易导致滤波发散的问题。自适应渐消因子阵S k由以下算法得到：
S k＝diag(s 1,s 2,s 3...,s n)
Figure PCTCN2019077887-appb-000011
式中，λ i为观测阵H k第i个观测元素，j ii(k)为矩阵
Figure PCTCN2019077887-appb-000012
的第i个对角线元素,ε i为卡方检测的阈值，υ i(k)为残差阵V k第i个观测元素，b ii(k)为矩阵
Figure PCTCN2019077887-appb-000013
的第i个对角线元素。值得注意的是，只有观测方程可观测的状态量才进行渐消因子计算，否则退化为标准卡尔曼滤波方程。
②自适应H∞卡尔曼滤波算法
考虑一个线性离散系统
Figure PCTCN2019077887-appb-000014
其中，L k为对系统状态量的线性组合的估计，常取为I，则y k表示对状态量x k的直接估计。
因此构造的造价函数J为：
Figure PCTCN2019077887-appb-000015
式中，N为整体滤波时间限定，x 0为系统初始状态量，其余已作介绍。H∞滤波中心思想是保证造价函数J的H∞范数最小，也就保证了从干扰信号到估计误差的最大能量增益最小。从而使得外界扰动和模型不确定对系统输出的影响最小。
min||J|| ∞＝γ 0
但由于H∞最优估计问题的封闭解仅在某些特定情况下可以得到，因此通常考虑的是设计H∞次优滤波。即给定一个足够接近γ 0的阈值γ，使构造的代数函数J的H∞范数小于γ即可。
如果Φ k|k-1满秩，则H∞次优问题存在解的充分必要条件可由黎卡提不等式(Riccati inequality)给出:
Figure PCTCN2019077887-appb-000016
式中，P k为系统状态量x k的最优协方差估计。
值得注意的是，滤波器的鲁棒性能与选择的阈值γ有关。阈值γ越小越接近γ 0，滤波器鲁棒性越强。但γ不能小于γ 0，否则H∞次优问题不存在解，将导致滤波器发散。同时，若γ趋近于无穷时，H无穷滤波器退化为传统kalman滤波器。常规阈值γ一般根据实际滑翔器工作经验确定且无法改变，从而使滤波的效果具有较大的保守性。不能实时保证系统的估计误差较小的同时系统又具有较高的鲁棒性。因此如果阈值γ的取值能够自适应于水下滑翔器不同的上浮校正环境，则能够在保证鲁棒性的基础上进一步提高上浮校正的导航定位精度。因此根据黎卡提不等式建立阈值γ的自适应算法如下：
Figure PCTCN2019077887-appb-000017
式中，ρ()表示矩阵的谱半径。令
Figure PCTCN2019077887-appb-000018
由于最优状态估计协方差P k不断在滤波迭代中更新，因此γ a是一个时变因子。
理想情况卡尔曼滤波的新息阵
Figure PCTCN2019077887-appb-000019
但系统不确定性影响会导致观测量的异常，从而引起滤波器的失常。这样的情况会引起新息序列平方和的
Figure PCTCN2019077887-appb-000020
的变化。
Figure PCTCN2019077887-appb-000021
较大时，反映不确定性干扰对系统造成影响较大，此时应增加系统鲁棒性(阈值γ应减小)，反之
Figure PCTCN2019077887-appb-000022
较小时，反映干扰较小，此时应更关注于系统导航定位精度(阈值γ应增大)。则自适应阈值γ为：
γ＝η·γ a其中，
Figure PCTCN2019077887-appb-000023
式中，
Figure PCTCN2019077887-appb-000024
为求迹运算，即求矩阵对角线元素之和。
与标准卡尔曼滤波器方程不同之处为：
Figure PCTCN2019077887-appb-000025
Figure PCTCN2019077887-appb-000026
式中，
Figure PCTCN2019077887-appb-000027
③基于多重自适应渐消因子的H∞卡尔曼滤波算法
将步骤①②的算法合并，形成整体的水下滑翔器上浮精度校正的组合导航算法(基于多重自适应渐消因子的H∞卡尔曼滤波算法)。算法流程图如图3所示，组合导航滤波器分为三个回路(滤波回路、增益回路、渐消回路)进行解算，具体流程如下：
i)首先进行时间更新。根据上一时刻滤波输出的最优状态量估计x k-1一步预测当前时刻的状态量x k|k-1，根据上一时刻滤波输出的最优协方差估计阵P k-1和渐消回路输出的渐消因子阵S k一同预测当前时刻的状态预测协方差P k|k-1。
ii)其次进行量测更新。根据当前时刻的状态预测协方差P k|k-1计算增益阵K k，在接收新的外部观测量Z k后利用增益阵K k和一步预测状态量x k|k-1更新系统最优状态量估计x k，再根据P k|k-1和时变因子γ a计算H∞阈值γ后，计算得到H∞滤波状态最优协方差P k。
iii)最后进行渐消因子更新。根据H∞滤波状态最优协方差P k更新渐消回路的过渡阵后，计算得到多重渐消因子阵S k。
(4)通过采用的一种基于多重自适应渐消因子的H∞卡尔曼滤波算法使得组合导航系统的速度、位置误差逐渐减小、趋于零。若误差小于一设定的阈值，则可认为上浮校正工作完成，滑翔器可切换至水下工作状态。

Claims (9)

  1. 一种水下滑翔器导航定位系统，其特征在于，包括微机电系统惯性测量单元MEMS-IMU，全球卫星定位系统接收模块GPS，三轴磁力计，多普勒测速仪DVL以及组合导航硬件处理系统；所述MEMS-IMU集成了三轴加速度计和三轴陀螺仪，输出的三轴加速度和角速率信息通过惯性导航算法得到水下滑翔器的姿态、速度和位置导航信息；所述三轴磁力计用于校正滑翔器的航向信息，所述多普勒测速仪用于判断水下滑翔器的运动状态；所述GPS用于滑翔器上浮校正操作中，与MEMS-IMU组成GPS/INS位置速度校正的松组合导航系统，利用组合导航滤波器算法，校正滑翔器的速度、位置误差；所述组合导航硬件处理系统用于三轴磁力计、GPS、MEMS-IMU以及DVL信号的接收处理与时钟同步工作，以及组合导航多传感器信息的算法解算工作。
  2. 根据权利要求1所述的水下滑翔器导航定位系统，其特征在于，所述水下滑翔器导航定位系统具有两种运行状态：水下工作状态和上浮校正状态；若导航定位误差过大，则滑翔器停止实时工作，由水下工作状态切换为上浮校正状态；当GPS/INS组合导航系统的速度、位置误差小于设定阈值时，则滑翔器由上浮校正状态切换为水下工作状态，继续工作。
  3. 根据权利要求1所述的水下滑翔器导航定位系统，其特征在于，水下工作利用MEMS-IMU和三轴磁力计输出的信号，采用基于互补滤波的四元数算法完成滑翔器的导航定位。
  4. 根据权利要求1所述的水下滑翔器导航定位系统，其特征在于，上浮校正利用GPS和MEMS-IMU输出的信号，采用GPS/INS位置速度校正的松组合导航系统，完成滑翔器的导航定位；其中组合导航滤波器算法采用基于多重自适应渐消因子的H∞卡尔曼滤波算法；
    所述滤波算法的状态预测协方差阵
    Figure PCTCN2019077887-appb-100001
    H∞滤波增益阵
    Figure PCTCN2019077887-appb-100002
    H∞滤波状态最优协方差阵
    Figure PCTCN2019077887-appb-100003
    式中k和k-1分别表示当前时刻和前一时刻，Φ k|k-1为状态转移矩阵，H k为观测矩阵，Q k-1为系统噪声协方差，S k为多重自适应渐消因子阵，L k为对系统状态量的线性组合的估计，I为 单位阵，
    Figure PCTCN2019077887-appb-100004
    γ为自适应阈值。
  5. 根据权利要求4所述的水下滑翔器导航定位系统，其特征在于，所述多重自适应渐消因子阵S k＝diag(s 1,s 2,s 3...,s n)由下式计算：
    Figure PCTCN2019077887-appb-100005
    式中，λ i为观测阵H k第i个观测元素，j ii(k)为矩阵
    Figure PCTCN2019077887-appb-100006
    的第i个对角线元素,ε i为卡方检测的阈值，υ i(k)为新息阵V k第i个观测元素，b ii(k)为矩阵
    Figure PCTCN2019077887-appb-100007
    的第i个对角线元素。
  6. 根据权利要求5所述的水下滑翔器导航定位系统，其特征在于，所述自适应阈值γ＝η·γ a；其中，
    Figure PCTCN2019077887-appb-100008
    Figure PCTCN2019077887-appb-100009
    Trace()表示矩阵求迹运算，ρ()表示矩阵的谱半径。
  7. 一种用于水下滑翔器导航定位系统的上浮精度校正方法，其特征在于，包括如下步骤：
    (1)判断水下滑翔器是否需要进行上浮精度校正操作，当航向变化过大、速度变化过大，或人为判定滑翔器导航定位误差过大时，滑翔器上浮将至水面，进行精度校正操作；
    (2)滑翔器上浮至水面后，接收GPS的经纬度、高度、三轴速度信号，IMU的三轴加速度，三轴角速率信号，将基于多重自适应渐消因子的H∞卡尔曼滤波算法应用于GPS/INS位置速度校正的松组合导航系统，完成数据融合；
    (3)通过步骤(2)使得组合导航系统的速度、位置误差逐渐减小、趋于零，若误差小于一设定的阈值，则认为上浮校正工作完成，滑翔器切换至水下工作状态。
  8. 根据权利要求7所述的用于水下滑翔器导航定位系统的上浮精度校正方法，其特征在于，所述基于多重自适应渐消因子的H∞卡尔曼滤波算法的状态预测协方差阵
    Figure PCTCN2019077887-appb-100010
    H∞滤波增益阵
    Figure PCTCN2019077887-appb-100011
    H∞滤波状态最优协方差阵
    Figure PCTCN2019077887-appb-100012
    式中k和k-1分别表示当前时刻和前一时刻，Φ k|k-1为状态转移矩阵，H k为观测矩阵，Q k-1为系统噪声协方差，S k为多重自适应渐消因子阵，L k为对系统状态量的线性组合的估计，I为单位阵，
    Figure PCTCN2019077887-appb-100013
    γ为自适应阈值。
  9. 根据权利要求8所述的用于水下滑翔器导航定位系统的上浮精度校正方法，其特征在于，所述基于多重自适应渐消因子的H∞卡尔曼滤波算法的解算流程包括：
    首先进行时间更新，具体包括：根据上一时刻滤波输出的最优状态量估计x k-1一步预测当前时刻的状态量x k|k-1＝Φ k|k-1x k-1，根据上一时刻滤波输出的最优协方差估计阵P k-1和渐消回路输出的渐消因子阵S k一同预测当前时刻的状态预测协方差
    Figure PCTCN2019077887-appb-100014
    其次进行量测更新，具体包括根据当前时刻的状态预测协方差P k|k-1计算增益阵
    Figure PCTCN2019077887-appb-100015
    在接收新的外部观测量Z k后利用增益阵K k和一步预测状态量x k|k-1更新系统最优状态量估计x k，再根据P k|k-1和时变因子γ a计算H∞阈值γ后，计算得到H∞滤波状态最优协方差
    Figure PCTCN2019077887-appb-100016
    最后进行渐消因子更新，具体包括：根据H∞滤波状态最优协方差P k更新渐消回路的过渡阵后，计算得到多重渐消因子阵S k＝diag(s 1,s 2,s 3...,s n)，其 中
    Figure PCTCN2019077887-appb-100017
    式中，λ i为观测阵H k第i个观测元素，j ii(k)为矩阵
    Figure PCTCN2019077887-appb-100018
    的第i个对角线元素,ε i为卡方检测的阈值，υ i(k)为新息阵V k第i个观测元素，b ii(k)为矩阵
    Figure PCTCN2019077887-appb-100019
    的第i个对角线元素。
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