CN102213594A - Method for fusing ocean current observation data of unmanned undersea vehicle (UUV) - Google Patents
Method for fusing ocean current observation data of unmanned undersea vehicle (UUV) Download PDFInfo
- Publication number
- CN102213594A CN102213594A CN 201110062965 CN201110062965A CN102213594A CN 102213594 A CN102213594 A CN 102213594A CN 201110062965 CN201110062965 CN 201110062965 CN 201110062965 A CN201110062965 A CN 201110062965A CN 102213594 A CN102213594 A CN 102213594A
- Authority
- CN
- China
- Prior art keywords
- ocean current
- uuv
- data
- carrier
- current profile
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 21
- 230000005888 antibody-dependent cellular phagocytosis Effects 0.000 claims abstract description 24
- 230000004927 fusion Effects 0.000 claims abstract description 18
- 238000005259 measurement Methods 0.000 claims abstract description 13
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims abstract description 9
- 238000004364 calculation method Methods 0.000 claims abstract description 7
- 238000001914 filtration Methods 0.000 claims abstract description 6
- 238000012937 correction Methods 0.000 claims abstract 3
- 238000005070 sampling Methods 0.000 claims description 7
- 238000007500 overflow downdraw method Methods 0.000 claims description 6
- 238000012545 processing Methods 0.000 claims description 6
- 238000000354 decomposition reaction Methods 0.000 claims description 5
- 230000000694 effects Effects 0.000 claims description 5
- 238000009499 grossing Methods 0.000 claims description 5
- 230000002159 abnormal effect Effects 0.000 claims description 3
- 239000011159 matrix material Substances 0.000 claims description 2
- 230000002123 temporal effect Effects 0.000 claims description 2
- 238000003491 array Methods 0.000 claims 1
- 230000001360 synchronised effect Effects 0.000 abstract description 6
- 238000010586 diagram Methods 0.000 description 6
- 241000251468 Actinopterygii Species 0.000 description 2
- 230000005540 biological transmission Effects 0.000 description 2
- 230000008030 elimination Effects 0.000 description 2
- 238000003379 elimination reaction Methods 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 238000009434 installation Methods 0.000 description 2
- 238000012805 post-processing Methods 0.000 description 2
- 230000001174 ascending effect Effects 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000002474 experimental method Methods 0.000 description 1
- 238000007499 fusion processing Methods 0.000 description 1
- 238000000691 measurement method Methods 0.000 description 1
- 238000012544 monitoring process Methods 0.000 description 1
- 238000011084 recovery Methods 0.000 description 1
Images
Landscapes
- Measurement Of Velocity Or Position Using Acoustic Or Ultrasonic Waves (AREA)
- Position Fixing By Use Of Radio Waves (AREA)
Abstract
本发明提供的是一种无人潜航器海流观测数据融合方法。1、海流剖面数据获取,UUV在水下一定深度航行时,获取UUV上部或下部一定水层厚度的海流剖面;2、海流剖面数据滤波,对获取的海流剖面原始数据进行滤波,消除野值,并对测量的随机误差进行平滑修正;3、海流剖面数据时间配准,将异步数据归算为相同时刻下的同步数据;4、UUV位置信息的推算,UUV从一个已知的坐标位置开始,根据UUV在该点的航向、航速和航行时间,推算下一时刻的坐标位置;5、海流剖面数据融合,将位置信息的推算所得的经纬度位置信息转换成ASCII码,插入到ADCP数据包的相应位置。本发明可以得到在大地坐标系下,准确、完整的海流剖面信息。
The invention provides a method for fusing ocean current observation data of an unmanned underwater vehicle. 1. Ocean current profile data acquisition, when the UUV is sailing at a certain depth underwater, the ocean current profile of a certain water layer thickness above or below the UUV is obtained; 2, Ocean current profile data filtering, the original data of the ocean current profile is filtered to eliminate outliers, And smooth corrections are made to the random errors of measurement; 3. Time registration of ocean current profile data, and asynchronous data are reduced to synchronous data at the same time; 4. Calculation of UUV position information, UUV starts from a known coordinate position, According to the heading, speed and sailing time of the UUV at this point, the coordinate position at the next moment is estimated; 5. The ocean current profile data fusion converts the longitude and latitude position information obtained from the position information calculation into ASCII codes, and inserts them into the corresponding ADCP data packet Location. The invention can obtain accurate and complete ocean current profile information under the geodetic coordinate system.
Description
技术领域technical field
本发明涉及的是一种水下无人航行器海洋观测数据处理方法。The invention relates to a method for processing marine observation data of an underwater unmanned vehicle.
背景技术Background technique
无人潜航器(Unmanned Underwater Vehicles,简称UUV)又称无人水下航行器或水下机器人,是一种以水面船舶为支援平台,可长时间在水下自主远程航行的无人智能小型装备平台。UUV携带多普勒海流剖面仪(Acoustic Doppler Current Profile,简称ADCP)海流测量法,属于水下自航式海洋观测技术,该海流观测方法可隐蔽观测、不受海面风浪影响、可实现全天候工作等特点,克服了传统坐底自容式海流监测方式的观测范围小,布放回收困难的不足,也克服了水面船舶携带ADCP观测海流时受海面环境影响严重、不能够对深海海流观测的缺陷,而UUV携带ADCP在水下测量海流时不能够接收到GPS信号,从而导致海流信息因缺失地理位置信息而变得不够准确和全面。而国内外文献中对于UUV搭载ADCP在水下航行时,测得的海流剖面数据未有对其进行数据融合的相关报道。Unmanned Underwater Vehicles (UUV for short), also known as unmanned underwater vehicles or underwater robots, is a kind of unmanned intelligent small equipment that uses surface ships as a support platform and can sail autonomously and long-distance underwater for a long time. platform. UUV carries the Doppler current profiler (Acoustic Doppler Current Profile, referred to as ADCP) current measurement method, which belongs to the underwater self-propelled ocean observation technology. It overcomes the shortcomings of the traditional bottom-seat self-contained current monitoring method, such as the small observation range and difficulty in deployment and recovery, and also overcomes the defects that surface ships are seriously affected by the sea environment when carrying ADCP to observe currents, and cannot observe deep-sea currents. However, when UUV carries ADCP to measure ocean currents underwater, it cannot receive GPS signals, resulting in inaccurate and comprehensive ocean current information due to lack of geographic location information. However, in domestic and foreign literature, there is no report on the data fusion of the measured ocean current profile data when the UUV is equipped with ADCP for underwater navigation.
发明内容Contents of the invention
本发明的目的在于提供一种可以得到在大地坐标系下,准确、完整的海流剖面信息的无人潜航器海流观测数据融合方法。The purpose of the present invention is to provide a method for fusion of unmanned submersible vehicle ocean current observation data that can obtain accurate and complete ocean current profile information under the geodetic coordinate system.
本发明的目的是这样实现的:The purpose of the present invention is achieved like this:
1.海流剖面数据获取1. Ocean current profile data acquisition
由外壳、推进器、DVL、ADCP、姿态传感器、罗经、GPS和控制计算机组成UUV,UUV在水下一定深度航行时,ADCP在控制计算机控制下,发射一定频率的声波,利用多普勒效应获取UUV上部或下部一定水层厚度的海流剖面;UUV is composed of shell, thruster, DVL, ADCP, attitude sensor, compass, GPS and control computer. When UUV sails at a certain depth underwater, ADCP emits sound waves of a certain frequency under the control of the control computer, and uses the Doppler effect to obtain The current profile of a certain water layer thickness above or below the UUV;
2.海流剖面数据滤波2. Ocean current profile data filtering
对获取的海流剖面原始数据进行滤波,消除野值,并对测量的随机误差进行平滑修正;Filter the obtained raw data of the ocean current profile, eliminate the outliers, and smooth out the random error of the measurement;
3.海流剖面数据时间配准3. Time registration of ocean current profile data
在对海流观测数据融合之前将经过滤波处理的异步数据归算为相同时刻下的同步数据;Before the fusion of ocean current observation data, the filtered asynchronous data is reduced to synchronous data at the same time;
4.UUV位置信息的推算4. Calculation of UUV position information
UUV从一个已知的坐标位置开始,根据UUV在该点的航向、航速和航行时间,推算下一时刻的坐标位置;UUV starts from a known coordinate position, and calculates the coordinate position at the next moment according to the course, speed and sailing time of the UUV at this point;
5.海流剖面数据融合5. Ocean current profile data fusion
将位置信息的推算所得的经纬度位置信息转换成ASCII码,插入到ADCP数据包的相应位置,更新包长与校验码之后完成位置信息与海流数据的融合。The longitude and latitude position information calculated by the position information is converted into ASCII code, inserted into the corresponding position of the ADCP data packet, and the fusion of position information and ocean current data is completed after updating the packet length and check code.
为了解决UUV携带ADCP在水下测量海流时不能够实时接收到GPS信号,从而导致海流信息因缺失地理位置信息而变得不够准确和全面的问题,本发明提出了一种UUV自身信息和海流数据融合的方法,以得到在大地坐标系下,准确、完整的海流剖面信息。In order to solve the problem that the UUV carrying ADCP cannot receive GPS signals in real time when measuring ocean currents underwater, resulting in inaccurate and comprehensive ocean current information due to lack of geographical location information, this invention proposes a UUV self-information and ocean current data Fusion method to obtain accurate and complete ocean current profile information in the geodetic coordinate system.
为获得持续、稳定的海流剖面数据,本发明设计了一个ADCP的平台,即UUV,UUV主要有外壳、推进器、DVL、ADCP、姿态传感器、罗经、GPS和控制计算机组成,UUV在水下一定深度航行时,ADCP(换能器头向上或向下安装)在控制计算机控制下,发射一定频率的声波,利用多普勒效应获取UUV上部或下部一定水层厚度的海流剖面。In order to obtain continuous and stable ocean current profile data, the present invention designs an ADCP platform, that is, UUV. UUV mainly consists of shell, propeller, DVL, ADCP, attitude sensor, compass, GPS and control computer. During deep navigation, the ADCP (transducer head installed upward or downward) emits sound waves of a certain frequency under the control of the control computer, and uses the Doppler effect to obtain the ocean current profile of a certain water layer thickness above or below the UUV.
UUV携带ADCP测量海流的过程中,由于仪器的自噪声、海况因素、声纳安装偏差和参数设置的不合理,以及姿态、罗经等传感器的误差,特别是当水层中有运动的鱼或海洋生物时,使得海流观测数据中含有异常数据(野值)和随机误差。本发明在对海流观测数据进行后处理时,对这些原始数据进行滤波,消除野值,并对测量的随机误差进行平滑修正,提高海流数据的精度。In the process of UUV carrying ADCP to measure ocean currents, due to the self-noise of the instrument, sea state factors, sonar installation deviation and unreasonable parameter settings, as well as errors in attitude, compass and other sensors, especially when there are moving fish or ocean in the water layer When biological, the ocean current observation data contains abnormal data (outlier values) and random errors. When the present invention performs post-processing on ocean current observation data, these original data are filtered, outliers are eliminated, random errors in measurement are smoothly corrected, and the accuracy of ocean current data is improved.
由于DVL、姿态传感器、罗经等传感器在采样起始时间、采样频率、传输延迟上很难完全同步,测得的海流数据、UUV的速度和航向数据在融合前往往是异步的。本发明在海流观测数据融合之前先将这些异步数据归算为相同时刻下的同步数据,这一处理过程就称为海流剖面信息时间配准。Since the DVL, attitude sensor, compass and other sensors are difficult to be completely synchronized in terms of sampling start time, sampling frequency, and transmission delay, the measured ocean current data, UUV speed and heading data are often asynchronous before fusion. In the present invention, these asynchronous data are reduced to synchronous data at the same time before the fusion of ocean current observation data, and this processing process is called ocean current profile information time registration.
UUV在水下航行时,GPS无法接收到实时的位置信息,为了获得UUV经纬度信息,需要对UUV的位置进行推算估计,这里采用舰位推算的方法:UUV从一个已知的坐标位置开始,根据UUV在该点的航向、航速和航行时间,推算下一时刻的坐标位置。When UUV is sailing underwater, GPS cannot receive real-time position information. In order to obtain UUV latitude and longitude information, it is necessary to calculate and estimate the position of UUV. Here, the method of ship position calculation is adopted: UUV starts from a known coordinate position, according to The course, speed and sailing time of the UUV at this point are used to calculate the coordinate position at the next moment.
UUV水下航行获得的海流剖面数据,不包含相应的经纬度位置信息,为了获得准确、全面的海流剖面数据,本发明将UUV经纬度位置信息与海流剖面信息进行数据融合:将舰位推算所得的经纬度位置信息转换成ASCII码,插入到ADCP数据包的相应位置,更新包长与校验码之后便完成了位置信息与海流数据的融合。The ocean current profile data obtained by UUV underwater navigation does not contain the corresponding longitude and latitude position information. In order to obtain accurate and comprehensive ocean current profile data, the present invention fuses the UUV latitude and longitude position information with the ocean current profile information: calculate the longitude and latitude of the ship position The position information is converted into ASCII code, inserted into the corresponding position of the ADCP data packet, and the fusion of position information and ocean current data is completed after updating the packet length and check code.
本发明相对现有技术具有如下的优点及效果:The present invention has following advantage and effect relative to prior art:
本发明能够实现UUV搭载的ADCP获得的海流剖面与UUV位置信息进行数据融合,从而得到全面、稳定、可靠的海流信息。The present invention can realize the data fusion of the ocean current profile obtained by the UUV-mounted ADCP and the UUV position information, thereby obtaining comprehensive, stable and reliable ocean current information.
附图说明Description of drawings
图1是本发明的海流观测数融合过程示意图;Fig. 1 is the schematic diagram of the ocean current observation number fusion process of the present invention;
图2是本发明的UUV组成框图;Fig. 2 is a UUV composition block diagram of the present invention;
图3是本发明的UUV位置推算示意图;Fig. 3 is a schematic diagram of UUV position estimation of the present invention;
图4是本发明的海流信息时间配准方案示意图;Fig. 4 is a schematic diagram of the time registration scheme of ocean current information of the present invention;
图5是本发明的融合后海流剖面信息WinRiver处理结果。Fig. 5 is the result of WinRiver processing of the fused ocean current profile information of the present invention.
具体实施方式Detailed ways
下面结合附图对本发明作进一步的描述。The present invention will be further described below in conjunction with the accompanying drawings.
附图2是本发明的UUV系统组成示意图,1是推进器,2是DVL,3是ADCP,4是姿态传感器,5是罗经,6是控制计算机,7是GPS,8是UUV外壳。Accompanying drawing 2 is a schematic diagram of the composition of the UUV system of the present invention, 1 is the thruster, 2 is the DVL, 3 is the ADCP, 4 is the attitude sensor, 5 is the compass, 6 is the control computer, 7 is the GPS, and 8 is the UUV shell.
图3是本发明中UUV位置推算原理示意图,其中初始经纬度(J0,W0)是指进行位置推算时初始时刻记录下的GPS经纬度,(J,W)为UUV位置推算时任意时刻UUV所在位置。Fig. 3 is a schematic diagram of the principle of UUV position estimation in the present invention, wherein the initial latitude and longitude (J 0 , W 0 ) refers to the GPS latitude and longitude recorded at the initial moment when the position is estimated, and (J, W) is where the UUV is at any time when the UUV position is estimated. Location.
1.海流剖面数据获取1. Ocean current profile data acquisition
通过设计一个UUV(如图1所示)来获取观测海域的海流剖面信息,该UUV主要有外壳、推进器、DVL、ADCP、姿态传感器、罗经、GPS和控制计算机组成,UUV在水下一定深度航行时,ADCP(换能器头向上或向下安装)在控制计算机控制下,发射一定频率的声波,利用多普勒效应可获取UUV上部或下部一定水层厚度的海流剖面数据,海流剖面数据通过串口(或网口)传输到控制计算机的存储介质进行存储,这样通过UUV就可以获取到海流剖面数据。By designing a UUV (as shown in Figure 1) to obtain the current profile information of the observed sea area, the UUV mainly consists of a shell, propeller, DVL, ADCP, attitude sensor, compass, GPS and control computer. The UUV is underwater at a certain depth. When sailing, ADCP (transducer head installed upwards or downwards) under the control of the control computer emits sound waves of a certain frequency, and the Doppler effect can be used to obtain the ocean current profile data of a certain water layer thickness above or below the UUV, and the ocean current profile data It is transmitted to the storage medium of the control computer through the serial port (or network port) for storage, so that the ocean current profile data can be obtained through the UUV.
2.海流观测数据滤波2. Ocean current observation data filtering
UUV携带ADCP测量海流的过程中,由于仪器的自噪声、海况因素、声纳安装偏差和参数设置的不合理,以及姿态、罗经等传感器的误差,特别是当水层中有运动的鱼或海洋生物时,使得海流观测数据中含有异常数据(野值)和随机误差。因此,在对海流观测数据进行后处理时,必须对这些原始数据进行滤波,消除野值,并对测量的随机误差进行平滑修正,提高海流数据的精度。In the process of UUV carrying ADCP to measure ocean currents, due to the self-noise of the instrument, sea state factors, sonar installation deviation and unreasonable parameter settings, as well as errors in attitude, compass and other sensors, especially when there are moving fish or ocean in the water layer When biological, the ocean current observation data contains abnormal data (outlier values) and random errors. Therefore, when post-processing the ocean current observation data, it is necessary to filter these raw data, eliminate outliers, and smooth the random error of measurement to improve the accuracy of the ocean current data.
(1)基于多项式拟合的海流数据野值剔除(1) Outlier elimination of ocean current data based on polynomial fitting
基于多项式拟合的野值剔除方法需要对已知的海流观测数据拟合一个多项式,再利用三倍拟合残差序列的均方误差准则进行判别剔除,较其它方法易于操作。该方法对海流观测数据野值剔除步骤如下:The outlier elimination method based on polynomial fitting needs to fit a polynomial to the known ocean current observation data, and then use the mean square error criterion of the triple fitting residual sequence to discriminate and eliminate, which is easier to operate than other methods. The steps of this method to eliminate the outlier value of ocean current observation data are as follows:
①对已知的海流观测数据(ti,yi),i=0,1,...,m,合适的次数n进行最小二乘多项式拟合,其中ti为海流测量时间,yi为海流速度大小;① Carry out least squares polynomial fitting on the known ocean current observation data (t i , y i ), i=0, 1, ..., m, appropriate number n, where t i is the ocean current measurement time, y i is the ocean current velocity;
②求出系数矩阵α0,α1,·,αn,得到拟合多项式 ② Calculate the coefficient matrix α 0 , α 1 , , α n to get the fitting polynomial
③计算出相应的拟合值序列pi:i=1,2,·,m;③Calculate the corresponding fitting value sequence p i : i=1, 2, ·, m;
④生成拟合残差序列Δyi=pi-yi,i=1,2,·,m;④ Generate a fitting residual sequence Δy i =p i -y i , i=1, 2, , m;
⑤计算拟合残差序列的均方误差σ:⑤ Calculate the mean square error σ of the fitted residual sequence:
⑥利用3σ准则判断并剔除野值,剔除野值后的数据为y′i:i=1,2,·,m:⑥ Use the 3σ criterion to judge and eliminate outliers, and the data after eliminating outliers is y′ i : i=1, 2, , m:
当|Δyi|<3σ时:When |Δy i |<3σ:
y′i=yi (2)y′ i =y i (2)
当|Δyi|>3σ时:When |Δy i |>3σ:
yi=(yi-3+yi-2+yi-1+yi+1+yi+2+yi+3)/6 (3)y i =(y i-3 +y i-2 +y i-1 +y i+1 +y i+2 +y i+3 )/6 (3)
根据式(2)和式(3)可知,若残差值小于门限值,为正常值,此值不变;若大于或等于门限值,则判断为野值,用与其相邻的六个数据的均值代替此野值。门限值3σ可根据实验具体情况做出适当的更改。According to formula (2) and formula (3), if the residual value is less than the threshold value, it is a normal value, and this value remains unchanged; if it is greater than or equal to the threshold value, it is judged as an outlier value, and the six adjacent The mean of the data is used to replace the wild value. The threshold value 3σ can be changed appropriately according to the specific situation of the experiment.
(2)基于小波分解与重构的海流数据平滑(2) Ocean current data smoothing based on wavelet decomposition and reconstruction
剔除野值以后,虽然数据中的异常值没有了,但是由于UUV携带的ADCP在实际测量中受水中散射体和系统内部各种误差的影响,给测量数据带来一定的随机误差,尤其是海流数据这种非平稳信号,会出现信号跳变较大的情况。因此,对于多普勒测量数据的处理面临的主要问题是如何选择一种有效的数据平滑方法。小波分解和重构技术是一种适用范围很广的新的数据平滑处理方法,具有在消除高频噪声的同时保留信息的细节部分,因此本发明采用小波包分解和信号重构理论,对海流数据进行平滑。基于小波分解与重构的海流数据平滑就是借小波变换中的多分辨特点来对数据进行分解,通过由Mallat提出的多分辨信号算法来实现对数据的逐层分解,最后得到原信号的低频信息。After eliminating the outliers, although the outliers in the data are gone, the ADCP carried by the UUV is affected by various errors in the water scatterers and the system in actual measurement, which brings certain random errors to the measurement data, especially ocean currents. For non-stationary signals such as data, there will be large signal jumps. Therefore, the main problem facing the processing of Doppler measurement data is how to choose an effective data smoothing method. Wavelet decomposition and reconstruction technology is a new data smoothing method with a wide range of applications. It has the details of retaining information while eliminating high-frequency noise. The data is smoothed. The smoothing of ocean current data based on wavelet decomposition and reconstruction is to decompose the data by using the multi-resolution characteristics in the wavelet transform, and realize the layer-by-layer decomposition of the data through the multi-resolution signal algorithm proposed by Mallat, and finally obtain the low-frequency information of the original signal .
3.海流观测数据时间配准3. Time registration of ocean current observation data
由于DVL、姿态传感器、罗经等传感器在采样起始时间、采样频率、传输延迟上很难完全同步,测得的海流数据、UUV的速度和航向数据在融合前往往是异步的。因此,在海流观测数据融合之前需要先将这些异步数据归算为相同时刻下的同步数据,这一处理过程就称为海流剖面信息的时间配准。Since the DVL, attitude sensor, compass and other sensors are difficult to be completely synchronized in terms of sampling start time, sampling frequency, and transmission delay, the measured ocean current data, UUV speed and heading data are often asynchronous before fusion. Therefore, these asynchronous data need to be reduced to synchronous data at the same time before the fusion of ocean current observation data. This process is called time registration of ocean current profile information.
本发明采用基于线性插值的时间配准方案,线性插值的基本原理是在高精度的观测数据上估算出相对应的低精度时间点上的值,取同一处理周期的三组观测数据,利用线性插值法,建立时间配准方案。需要时间配准的三个数据源按测量精度由小到大的顺序是:UUV速度数据,UUV艏向数据,海流速度数据,具体过程见图4。The present invention adopts a time registration scheme based on linear interpolation. The basic principle of linear interpolation is to estimate the value at the corresponding low-precision time point on high-precision observation data, take three sets of observation data in the same processing cycle, and use linear Interpolation method to establish a temporal registration scheme. The three data sources that need time registration are in ascending order of measurement accuracy: UUV velocity data, UUV heading data, and sea current velocity data. The specific process is shown in Figure 4.
①定义数组并赋值,分别为t[],y[],其中t[]为海流测量时间,y[]为海流速度大小;① Define an array and assign values, respectively t[], y[], where t[] is the measurement time of the ocean current, and y[] is the velocity of the ocean current;
②输入待求函数值点T;②Input the value point T of the function to be found;
③在t数组内搜索T的位置,找到T邻近的插值节点ti、ti+1,其中ti≤T≤ti+1;③ Search the position of T in the t array, and find the interpolation nodes t i and t i+1 adjacent to T, where t i ≤ T ≤ t i+1 ;
④找到对应的y的插值节点yi和yi+1,利用每两个相邻插值基点作线性插值,即可得如下线性插值函数: ④Find the corresponding interpolation nodes y i and y i+1 of y, and use every two adjacent interpolation base points for linear interpolation to obtain the following linear interpolation function:
4.UUV位置信息的推算4. Calculation of UUV position information
UUV航行时,DVL测得的速度一般是船体坐标系下的速度,为了推算UUV航行时载体的经纬度,需要把船体坐标系下UUV的速度转化为北东坐标系下的速度。UUV载体航行速度在正东方向和正北方向的分量计算如下:When UUV is sailing, the speed measured by DVL is generally the speed in the hull coordinate system. In order to calculate the longitude and latitude of the carrier when UUV sails, it is necessary to convert the speed of UUV in the hull coordinate system to the speed in the northeast coordinate system. The components of the UUV carrier speed in the direction of due east and due north are calculated as follows:
式中vE、vN——分别为载体航行速度在正东方向和正北方向的分量;In the formula, v E and v N —— are the components of the carrier's navigation speed in the direction of due east and due north respectively;
vF、vL——分别为DVL测得的载体相对大地的前向速度和左向速度;v F , v L ——respectively, the forward velocity and leftward velocity of the carrier relative to the ground measured by DVL;
H——UUV载体的航向角,顺时针为正,逆时针为负,可由罗经测得。H——The heading angle of the UUV carrier, clockwise is positive and counterclockwise is negative, which can be measured by the compass.
结合图3,UUV载体航行中经纬度可由公式(5)进行计算:Combined with Figure 3, the longitude and latitude of UUV carrier navigation can be calculated by formula (5):
式中Δt——DVL采样周期;In the formula, Δt——DVL sampling period;
J、W——分别为n时刻UUV载体所在位置的经度和纬度;J, W——respectively the longitude and latitude of the UUV carrier's location at n time;
J0、W0——分别为初始时刻载体的经度和纬度,可由GPS接收机测得;J 0 , W 0 ——respectively, the longitude and latitude of the carrier at the initial moment, which can be measured by the GPS receiver;
vE,(i-1)、vN,(i-1)——分别为i-1时刻UUV航行速度在正东方向和正北方向的分量;v E, (i-1) , v N, (i-1) ——respectively the components of UUV navigation speed in the direction of due east and due north at time i-1;
RM,(i-1)、RN,(i-1)——分别为i-1时刻地球子午曲率半径和纬度圈曲率半径。R M, (i-1) , R N, (i-1) ——respectively, the radius of curvature of the earth's meridian and the radius of curvature of the latitude circle at time i-1.
5.海流剖面信息数据融合5. Ocean current profile information data fusion
数据融合主要是将UUV自身信息(姿态,航向、导航速度、位置)与海流剖面信息进行数据级别的融合;将UUV的姿态,航向、导航速度、位置等信息转换成ASCII码,经过前四个步骤处理后,融合到海流剖面信息数据包中的相应位置,更新包长与校验码之后便完成了位置信息与海流数据的融合。用海流后处理软件WinRiver软件打开融合后的海流剖面数据包,就可以直观地得到海流剖面在任一位置海流剖面的大小和方向。Data fusion is mainly to fuse the UUV's own information (attitude, heading, navigation speed, position) and ocean current profile information at the data level; convert the UUV's attitude, heading, navigation speed, position and other information into ASCII codes, after the first four After the steps are processed, it is fused to the corresponding position in the ocean current profile information data packet, and after updating the packet length and check code, the fusion of position information and ocean current data is completed. Open the fused ocean current profile data package with the ocean current post-processing software WinRiver software, and you can intuitively get the size and direction of the ocean current profile at any position.
图5是利用本发明的UUV搭载的ADCP传感器,在水下7m航行时测得了观测某区域的海流剖面信息,利用WinRiver软件处理海流剖面数据融合后的结果,从图5中可以直观地看出海流的大小和方向。Fig. 5 is the use of the ADCP sensor carried by the UUV of the present invention to measure and observe the ocean current profile information in a certain area when sailing underwater at 7m, and use the WinRiver software to process the results of the fusion of the ocean current profile data. It can be seen intuitively from Fig. 5 The magnitude and direction of ocean currents.
Claims (5)
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN 201110062965 CN102213594A (en) | 2011-03-16 | 2011-03-16 | Method for fusing ocean current observation data of unmanned undersea vehicle (UUV) |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN 201110062965 CN102213594A (en) | 2011-03-16 | 2011-03-16 | Method for fusing ocean current observation data of unmanned undersea vehicle (UUV) |
Publications (1)
Publication Number | Publication Date |
---|---|
CN102213594A true CN102213594A (en) | 2011-10-12 |
Family
ID=44744996
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
CN 201110062965 Pending CN102213594A (en) | 2011-03-16 | 2011-03-16 | Method for fusing ocean current observation data of unmanned undersea vehicle (UUV) |
Country Status (1)
Country | Link |
---|---|
CN (1) | CN102213594A (en) |
Cited By (24)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN102914296A (en) * | 2012-11-07 | 2013-02-06 | 国家海洋技术中心 | Observing method of small-sized underwater autonomous navigation observing platform |
CN103838146A (en) * | 2014-03-18 | 2014-06-04 | 大连海事大学 | Ocean current estimation system and estimation method |
US8881665B2 (en) | 2011-09-30 | 2014-11-11 | Cggveritas Services Sa | Deployment and recovery vessel for autonomous underwater vehicle for seismic survey |
CN104502633A (en) * | 2014-12-29 | 2015-04-08 | 南京世海声学科技有限公司 | Flow field data correction method for acoustic Doppler flow velocity profiler |
CN104613979A (en) * | 2013-11-05 | 2015-05-13 | 中国科学院沈阳自动化研究所 | Apparatus and method used for calibrating parameter of AUV autonomous navigation sensor |
US9090319B2 (en) | 2011-09-30 | 2015-07-28 | Seabed Geosolutions As | Autonomous underwater vehicle for marine seismic surveys |
CN104870940A (en) * | 2012-10-29 | 2015-08-26 | 德立文亚迪仪器公司 | System and method for water column aided navigation |
US9381986B2 (en) | 2012-11-21 | 2016-07-05 | Seabed Geosolutions B.V. | Jet-pump-based autonomous underwater vehicle and method for coupling to ocean bottom during marine seismic survey |
CN105891541A (en) * | 2016-06-13 | 2016-08-24 | 南京世海声学科技有限公司 | ADCP surface layer blind area flow velocity estimation method based on interlayer correlation |
US9457879B2 (en) | 2012-12-17 | 2016-10-04 | Seabed Geosolutions B.V. | Self-burying autonomous underwater vehicle and method for marine seismic surveys |
CN106352858A (en) * | 2016-11-21 | 2017-01-25 | 中国科学院大气物理研究所 | Atmospheric sea observation platform, system and method |
CN106802147A (en) * | 2017-01-24 | 2017-06-06 | 青岛海洋地质研究所 | Preventing seabed base in-situ observation platform auxiliary feeler lever departs from and safety guard |
US9969470B2 (en) | 2011-09-30 | 2018-05-15 | Cgg Services Sas | Deployment and recovery of autonomous underwater vehicles for seismic survey |
CN109297530A (en) * | 2018-08-06 | 2019-02-01 | 广州海洋地质调查局 | A deep-sea full-water column multi-environmental element information fusion method and processing terminal |
CN110243291A (en) * | 2019-01-10 | 2019-09-17 | 武汉滨湖电子有限责任公司 | A kind of laser pavement construction depth detection system anti-interference method |
CN106643723B (en) * | 2016-11-07 | 2019-11-26 | 哈尔滨工程大学 | A kind of unmanned boat safe navigation dead reckoning method |
CN110608726A (en) * | 2019-10-23 | 2019-12-24 | 中电科信息产业有限公司 | Storm surge tide level observation station |
US10543892B2 (en) | 2017-02-06 | 2020-01-28 | Seabed Geosolutions B.V. | Ocean bottom seismic autonomous underwater vehicle |
CN110954108A (en) * | 2019-12-04 | 2020-04-03 | 宁波羽声海洋科技有限公司 | Underwater matching navigation positioning method and device based on ocean current and electronic equipment |
CN111596093A (en) * | 2020-04-21 | 2020-08-28 | 天津大学 | Seawater flow velocity data processing method based on ADCP |
CN113501114A (en) * | 2020-11-23 | 2021-10-15 | 自然资源部第一海洋研究所 | Deep sea current calculation method based on unpowered submergence real-time information of deep submergence device |
US11255998B2 (en) | 2018-05-17 | 2022-02-22 | Seabed Geosolutions B.V. | Cathedral body structure for an ocean bottom seismic node |
CN116400101A (en) * | 2023-06-08 | 2023-07-07 | 自然资源部第一海洋研究所 | Near-surface ocean current data credibility judging method |
CN118503607A (en) * | 2024-07-10 | 2024-08-16 | 天津大学 | Ocean current information prediction method, device and electronic equipment |
-
2011
- 2011-03-16 CN CN 201110062965 patent/CN102213594A/en active Pending
Non-Patent Citations (1)
Title |
---|
《仪器仪表学报》 20110131 张勋 等 UUV水流观测数据融合与可视化技术研究 第57-62页 1-5 第32卷, 第1期 * |
Cited By (34)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US9090319B2 (en) | 2011-09-30 | 2015-07-28 | Seabed Geosolutions As | Autonomous underwater vehicle for marine seismic surveys |
US9487275B2 (en) | 2011-09-30 | 2016-11-08 | Cgg Services Sa | Deployment and recovery vessel for autonomous underwater vehicle for seismic survey |
US8881665B2 (en) | 2011-09-30 | 2014-11-11 | Cggveritas Services Sa | Deployment and recovery vessel for autonomous underwater vehicle for seismic survey |
US9969470B2 (en) | 2011-09-30 | 2018-05-15 | Cgg Services Sas | Deployment and recovery of autonomous underwater vehicles for seismic survey |
US9821894B2 (en) | 2011-09-30 | 2017-11-21 | Seabed Geosolutions As | Autonomous underwater vehicle for marine seismic surveys |
CN104870940A (en) * | 2012-10-29 | 2015-08-26 | 德立文亚迪仪器公司 | System and method for water column aided navigation |
CN102914296B (en) * | 2012-11-07 | 2015-09-16 | 国家海洋技术中心 | Small underwater independently navigates by water observation platform observation procedure |
CN102914296A (en) * | 2012-11-07 | 2013-02-06 | 国家海洋技术中心 | Observing method of small-sized underwater autonomous navigation observing platform |
US9381986B2 (en) | 2012-11-21 | 2016-07-05 | Seabed Geosolutions B.V. | Jet-pump-based autonomous underwater vehicle and method for coupling to ocean bottom during marine seismic survey |
US9457879B2 (en) | 2012-12-17 | 2016-10-04 | Seabed Geosolutions B.V. | Self-burying autonomous underwater vehicle and method for marine seismic surveys |
CN104613979A (en) * | 2013-11-05 | 2015-05-13 | 中国科学院沈阳自动化研究所 | Apparatus and method used for calibrating parameter of AUV autonomous navigation sensor |
CN103838146B (en) * | 2014-03-18 | 2016-08-24 | 大连海事大学 | A kind of current estimation system and method for estimation |
CN103838146A (en) * | 2014-03-18 | 2014-06-04 | 大连海事大学 | Ocean current estimation system and estimation method |
CN104502633B (en) * | 2014-12-29 | 2018-04-13 | 南京世海声学科技有限公司 | A kind of flow field data correcting method for acoustic Doppler fluid velocity profile instrument |
CN104502633A (en) * | 2014-12-29 | 2015-04-08 | 南京世海声学科技有限公司 | Flow field data correction method for acoustic Doppler flow velocity profiler |
CN105891541A (en) * | 2016-06-13 | 2016-08-24 | 南京世海声学科技有限公司 | ADCP surface layer blind area flow velocity estimation method based on interlayer correlation |
CN106643723B (en) * | 2016-11-07 | 2019-11-26 | 哈尔滨工程大学 | A kind of unmanned boat safe navigation dead reckoning method |
CN106352858A (en) * | 2016-11-21 | 2017-01-25 | 中国科学院大气物理研究所 | Atmospheric sea observation platform, system and method |
CN106802147A (en) * | 2017-01-24 | 2017-06-06 | 青岛海洋地质研究所 | Preventing seabed base in-situ observation platform auxiliary feeler lever departs from and safety guard |
US10543892B2 (en) | 2017-02-06 | 2020-01-28 | Seabed Geosolutions B.V. | Ocean bottom seismic autonomous underwater vehicle |
US11267546B2 (en) | 2017-02-06 | 2022-03-08 | Seabed Geosolutions B.V. | Ocean bottom seismic autonomous underwater vehicle |
US11255998B2 (en) | 2018-05-17 | 2022-02-22 | Seabed Geosolutions B.V. | Cathedral body structure for an ocean bottom seismic node |
CN109297530A (en) * | 2018-08-06 | 2019-02-01 | 广州海洋地质调查局 | A deep-sea full-water column multi-environmental element information fusion method and processing terminal |
CN109297530B (en) * | 2018-08-06 | 2019-08-23 | 广州海洋地质调查局 | A kind of multi-environment element information fusion method of the full water column in deep-sea and processing terminal |
CN110243291A (en) * | 2019-01-10 | 2019-09-17 | 武汉滨湖电子有限责任公司 | A kind of laser pavement construction depth detection system anti-interference method |
CN110608726A (en) * | 2019-10-23 | 2019-12-24 | 中电科信息产业有限公司 | Storm surge tide level observation station |
CN110954108A (en) * | 2019-12-04 | 2020-04-03 | 宁波羽声海洋科技有限公司 | Underwater matching navigation positioning method and device based on ocean current and electronic equipment |
CN111596093A (en) * | 2020-04-21 | 2020-08-28 | 天津大学 | Seawater flow velocity data processing method based on ADCP |
CN113501114A (en) * | 2020-11-23 | 2021-10-15 | 自然资源部第一海洋研究所 | Deep sea current calculation method based on unpowered submergence real-time information of deep submergence device |
CN113501114B (en) * | 2020-11-23 | 2022-05-06 | 自然资源部第一海洋研究所 | Deep sea current calculation method based on unpowered submergence real-time information of deep submergence device |
CN116400101A (en) * | 2023-06-08 | 2023-07-07 | 自然资源部第一海洋研究所 | Near-surface ocean current data credibility judging method |
CN116400101B (en) * | 2023-06-08 | 2023-08-29 | 自然资源部第一海洋研究所 | Near-surface ocean current data credibility judging method |
CN118503607A (en) * | 2024-07-10 | 2024-08-16 | 天津大学 | Ocean current information prediction method, device and electronic equipment |
CN118503607B (en) * | 2024-07-10 | 2024-10-11 | 天津大学 | Ocean current information prediction method, device and electronic equipment |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
CN102213594A (en) | Method for fusing ocean current observation data of unmanned undersea vehicle (UUV) | |
CN105823480B (en) | Underwater moving target location algorithm based on single beacon | |
CN103697910B (en) | The correction method of autonomous underwater aircraft Doppler log installation error | |
CN102323586B (en) | A UUV-assisted navigation method based on ocean current profile | |
CN107990891B (en) | Combined Navigation Method of Underwater Robot Based on Long Baseline and Beacon Online Calibration | |
CN102980579B (en) | Autonomous underwater vehicle autonomous navigation locating method | |
US11460588B2 (en) | Method and system for real-time high-precision positioning in deep sea | |
CN106643723B (en) | A kind of unmanned boat safe navigation dead reckoning method | |
CN104280025B (en) | Deepwater robot ultra-short baseline Combinated navigation method based on uncented Kalman filter | |
CN103336267A (en) | Master-slave mode multi-UUV (Unmanned Underwater Vehicle) cooperative location method based on underwater acoustic communication delay | |
CN110274591B (en) | ADCP-assisted SINS navigation method for deep-dive manned submersibles | |
CN108614258A (en) | A kind of Underwater Navigation method based on single acoustic beacon distance measuring | |
CN111174774B (en) | A navigation information fusion method and system in a certain depth water level mode | |
CN111220146B (en) | An underwater terrain matching and localization method based on Gaussian process regression learning | |
CN109579850B (en) | Deep-water intelligent navigation method based on water speed-assisted inertial navigation | |
CN106054135A (en) | Passive underwater sound positioning method based on moving time window periodically | |
CN115390012B (en) | Multi-transponder coordinate measuring method, device and system for HOV (Hov) accurate positioning | |
CN108919324B (en) | Positioning method of underwater glider | |
Zhang et al. | Ocean current-aided localization and navigation for underwater gliders with information matching algorithm | |
CN110865333B (en) | Single beacon passive acoustic localization method for underwater glider under the influence of ocean current | |
CN105758405A (en) | UUV (unmanned underwater vehicle) tracking mother vessel path planning method applicable to terrible ocean environments | |
CN103292813A (en) | Information filtering method for improving formation and navigation accuracy of water surface boat | |
KR20170078835A (en) | Method of calculating the surface speed of at least one ship and method for deduction of each vector derived at any point of the trajectory of said ship | |
CN110764533A (en) | A collaborative target search method for multiple underwater robots | |
CN108089588A (en) | A kind of Observational depth segmented adaptive planing method of underwater robot |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
C06 | Publication | ||
PB01 | Publication | ||
C10 | Entry into substantive examination | ||
SE01 | Entry into force of request for substantive examination | ||
C02 | Deemed withdrawal of patent application after publication (patent law 2001) | ||
WD01 | Invention patent application deemed withdrawn after publication |
Application publication date: 20111012 |