技术领域technical field
本发明公开了一种卫星/惯性/视觉组合导航系统完好性评估方法，涉及多传感器组合导航系统的完好性评估，属于计算、推算、计数的技术领域。The invention discloses a method for evaluating the integrity of a satellite/inertial/vision integrated navigation system, relates to the integrity evaluation of a multi-sensor integrated navigation system, and belongs to the technical field of calculation, calculation and counting.
背景技术Background technique
近年来，MEMS(Micro-Electro-Mechanical System，微机电系统)传感器、MSIS(Micro Solid-state Inertial Sensor，微小固态惯性传感器)、光纤陀螺和GNSS(GlobalNavigation Satellite System，全球导航卫星系统)接收机等新型低成本、小型、轻质量的导航传感器以及高速大容量的嵌入式微处理器和分布式模块化电子设备发展迅速，兴起了多传感器导航系统的研究浪潮。In recent years, MEMS (Micro-Electro-Mechanical System, Micro-Electro-Mechanical System) sensors, MSIS (Micro Solid-state Inertial Sensor, tiny solid-state inertial sensor), fiber optic gyroscope and GNSS (Global Navigation Satellite System, Global Navigation Satellite System) receivers, etc. New low-cost, small, light-weight navigation sensors, high-speed and large-capacity embedded microprocessors and distributed modular electronic devices have developed rapidly, and a wave of research on multi-sensor navigation systems has arisen.
为了提高多传感器导航系统的准确性和可靠性，国内外专家提出了各种软硬件冗余技术、完好性监测算法以及多传感器信息融合算法。大量研究发现，导航系统的故障容错能力主要由系统硬件冗余结构和完好性监测算法决定。目前，国内外对导航系统的完好性监测的研究方法主要分为快照法和连续法。In order to improve the accuracy and reliability of the multi-sensor navigation system, experts at home and abroad have proposed various software and hardware redundancy technologies, integrity monitoring algorithms, and multi-sensor information fusion algorithms. A large number of studies have found that the fault tolerance of navigation systems is mainly determined by the system hardware redundancy structure and integrity monitoring algorithms. At present, the research methods on the integrity monitoring of navigation systems at home and abroad are mainly divided into snapshot method and continuous method.
快照法利用单历元的测量信息来检测和隔离瞬时故障，通常用于变化较大的故障，典型的方法有最小二乘残差法、奇偶矢量法等，国内外广泛用于研究的关于GPS的RAIM算法属于快照法。杨永波等人提出全局最小二乘法，相较于惯用的最小二乘残差法，提高了RAIM算法的可靠性。针对最小二乘法在卫星故障检测和识别中存在的问题，王尔申等人将测量方程中协方差矩阵的对角线元素作为加权因子，进而得到基于加权最小二乘法的卫星故障检测算法。宋凯等人提出了基于Kalman滤波和奇偶矢量法的综合RAIM算法，与传统RAIM算法相比，提高了对微小伪距偏差的检测率,同时减少了对可见卫星数的要求。魏春玲等研究了基于神经网络的奇偶向量补偿法以解决斜置系统的故障诊断问题。然而，虽然快照法可以有效检测惯性传感器或GPS信号的阶跃故障，但很难检测惯性器件漂移等因素引起的软故障。The snapshot method uses the measurement information of a single epoch to detect and isolate instantaneous faults, and is usually used for faults with large changes. Typical methods include the least squares residual method, parity vector method, etc. The RAIM algorithm belongs to the snapshot method. Yang Yongbo and others proposed the global least squares method, which improves the reliability of the RAIM algorithm compared with the conventional least squares residual method. Aiming at the problems of the least squares method in satellite fault detection and identification, Wang Ershen et al. used the diagonal elements of the covariance matrix in the measurement equation as weighting factors, and then obtained a satellite fault detection algorithm based on the weighted least squares method. Song Kai and others proposed a comprehensive RAIM algorithm based on Kalman filtering and parity vector method. Compared with the traditional RAIM algorithm, it improves the detection rate of small pseudorange deviations and reduces the requirement for the number of visible satellites. Wei Chunling and others studied the parity vector compensation method based on neural network to solve the problem of fault diagnosis of tilted system. However, although the snapshot method can effectively detect step faults of inertial sensors or GPS signals, it is difficult to detect soft faults caused by factors such as inertial device drift.
对于缓变误差的检测，通常使用SPRT法、基于动力学的模型法等基于历史累积信息的连续法。针对GPS接收机观测噪声分布的特点和基本粒子滤波存在的粒子退化与采样枯竭问题，王尔申等人给出了一种基于遗传算法进行再采样的粒子滤波与似然比方法有机结合的全球定位系统RAIM算法，该算法在非高斯测量噪声下成功地检测并隔离故障卫星,提高了故障检测的性能，但该算法比较耗时，还无法满足导航系统实时监测的要求。For the detection of slowly changing errors, continuous methods based on historical accumulated information, such as SPRT method and dynamics-based model method, are usually used. Aiming at the characteristics of GPS receiver observation noise distribution and the problem of particle degradation and sampling exhaustion in basic particle filter, Wang Ershen et al. proposed a global positioning system based on the organic combination of particle filter and likelihood ratio method for resampling based on genetic algorithm RAIM algorithm, which successfully detects and isolates faulty satellites under non-Gaussian measurement noise, improves the performance of fault detection, but this algorithm is time-consuming and cannot meet the requirements of real-time monitoring of navigation systems.
尽管不少学者对多传感器导航系统做了大量的研究，但是大都采用滤波算法来进行完好性监测。目前，许多INS/GPS组合导航系统采用集中式滤波结构，并表现出较好的效果，但随着传感器数量的增多，计算量会激增，从而带来时间延时，不能满足用户的实时性需求，不利于故障的隔离。分布式滤波结构为多传感器导航系统提供了灵活可变的方案，但仍需解决各局部状态的动力学关系这一问题。Although many scholars have done a lot of research on multi-sensor navigation systems, most of them use filtering algorithms for integrity monitoring. At present, many INS/GPS integrated navigation systems adopt a centralized filtering structure and show good results, but as the number of sensors increases, the amount of calculation will increase sharply, resulting in time delay, which cannot meet the real-time requirements of users , is not conducive to fault isolation. The distributed filtering structure provides a flexible and variable solution for the multi-sensor navigation system, but it still needs to solve the problem of the dynamic relationship of each local state.
本申请旨在提出一种萤火虫算法和图优化相结合的方法，通过较少的计算量实现多传感器导航系统的全局优化。This application aims to propose a method combining firefly algorithm and graph optimization to achieve global optimization of a multi-sensor navigation system with less calculation.
发明内容Contents of the invention
本发明的发明目的是针对上述背景技术的不足，提供了一种卫星/惯性/视觉组合导航系统完好性评估方法，通过较少的计算量实现了多传感器组合导航系统的全局优化，解决了现有多传感器导航系统完好性评估方法不能满足实时性需求的技术问题。The object of the invention of the present invention is to provide a satellite/inertial/visual integrated navigation system integrity evaluation method for the above-mentioned deficiencies in the background technology, which realizes the global optimization of the multi-sensor integrated navigation system through less calculation, and solves the problem of existing problems. The integrity assessment method of multi-sensor navigation system cannot meet the technical problem of real-time requirements.
本发明为实现上述发明目的采用如下技术方案：The present invention adopts following technical scheme for realizing above-mentioned purpose of the invention:
一种卫星/惯性/视觉组合导航系统完好性评估方法，对当前时刻各传感器的测量值进行预处理得到当前时刻各传感器的观测向量，将当前时刻各传感器的观测向量表示为具有公共位姿节点的多条边，利用位姿节点和边之间的关系，图优化构建残差带权平方和函数，采用萤火虫算法优化位姿节点来求解残差带权平方和函数的最优解，对残差带权平方和函数的最优解标准化得到检验统计量并根据给定的误警率设置检测门限，在检验统计量超过检测门限时判定故障。A satellite/inertial/visual integrated navigation system integrity assessment method, which preprocesses the measurement values of each sensor at the current moment to obtain the observation vector of each sensor at the current moment, and expresses the observation vector of each sensor at the current moment as a node with a common pose Using the relationship between the pose node and the edge, the graph optimizes to construct the residual weighted sum of squares function, and uses the firefly algorithm to optimize the pose node to solve the optimal solution of the residual weighted sum of squares function. The test statistic is obtained by standardizing the optimal solution of the sum of squares function with weighted difference, and the detection threshold is set according to the given false alarm rate, and the fault is judged when the test statistic exceeds the detection threshold.
进一步的，一种卫星/惯性/视觉组合导航系统完好性评估方法中，当前时刻各传感器的观测向量包含各传感器采集的东北天坐标系下的位置信息及速度信息。Further, in a satellite/inertial/visual integrated navigation system integrity assessment method, the observation vectors of each sensor at the current moment include position information and velocity information collected by each sensor in the northeast sky coordinate system.
进一步的，一种卫星/惯性/视觉组合导航系统完好性评估方法中，通过位姿节点和位姿节点的位置对与位姿节点之间的边所代表的观测信息的影响构建残差带权平方和函数。Further, in a satellite/inertial/visual integrated navigation system integrity assessment method, the influence of the pose node and the position of the pose node on the observation information represented by the edge between the pose node and the pose node is used to construct the residual weight Sum of squares function.
进一步的，一种卫星/惯性/视觉组合导航系统完好性评估方法中，采用萤火虫算法优化位姿节点来求解残差带权平方和函数的最优解的过程为：将图优化过程中的得到的一组位姿节点映射为萤火虫位置，以残差带权平方和函数取值最小为目标确定萤火虫群体的最大荧光亮度，根据萤火虫群体的最大荧光亮度决定萤火虫移动方向，再结合最佳位置萤火虫的最大吸引度随机移动最佳位置的萤火虫进而更新其空间位置，重新计算萤火虫群体的最大荧光亮度后进行下一次搜索直至满足搜索精度要求或达到最大搜索次数。Furthermore, in a satellite/inertial/visual integrated navigation system integrity assessment method, the process of using the firefly algorithm to optimize the pose node to solve the optimal solution of the residual weighted sum of squares function is as follows: A set of pose nodes in the map is the position of the firefly, and the maximum fluorescence brightness of the firefly group is determined with the minimum value of the residual weighted square sum function as the goal, and the moving direction of the firefly is determined according to the maximum fluorescence brightness of the firefly group, and combined with the optimal position of the firefly The maximum attractiveness of the fireflies randomly moves the fireflies in the best position to update their spatial positions, recalculate the maximum fluorescence brightness of the firefly population, and then perform the next search until the search accuracy requirements are met or the maximum number of searches is reached.
进一步的，一种卫星/惯性/视觉组合导航系统完好性评估方法中，对残差带权平方和函数的最优解标准化得到的检验统计量为：根据给定的误警率PFA设置检测门限T6的表达式为：其中，rk为k时刻的检验统计量，rk服从自由度为6的χ2分布，F(X)为残差带权平方和函数的最优解，为方差，为概率分布密度函数。Further, in a satellite/inertial/visual integrated navigation system integrity assessment method, the test statistic obtained by standardizing the optimal solution of the residual weighted sum of squares function is: The expression of setting the detection thresholdT6 according to the given false alarm ratePFA is: Among them, rk is the test statistic at time k, rk obeys the χ2 distribution with6 degrees of freedom, and F(X) is the optimal solution of residual weighted sum of squares function, is the variance, is a probability distribution density function.
再进一步的，一种卫星/惯性/视觉组合导航系统完好性评估方法中，残差带权平方和函数为zij为位姿节点xi和位姿节点xj之间的边所表示的观测信息，e(xi,xj,zij)为表示xi和xj之间的关系与观测zij有多吻合的向量误差矩阵，fij为表示xi和xj之间的关系与观测zij有多吻合的向量误差函数，Ωij为e(xi,xj,zij)的权重，wij为fij的权重，X＝[x0,x1,x2,x3]，M＝{0,1,2,3}。Furthermore, in a satellite/inertial/visual integrated navigation system integrity assessment method, the residual weighted sum of squares function is zij is the observation information represented by the edge between pose node xi and pose node xj , e(xi , xj , zij ) is the relationship between xi and xj and observation zij How consistent is the vector error matrix, fij is the vector error function indicating how consistent the relationship between xi and xj is with the observation zij , Ωij is the weight of e(xi , xj , zij ), wij is the weight of fij , X=[x0 ,x1 ,x2 ,x3 ], M={0,1,2,3}.
本发明采用上述技术方案，具有以下有益效果：本发明将萤火虫算法和图优化相结合，提出了一种多传感器组合导航系统的完好性评估方法，该方法首先将多传感器观测向量表示为具有公共位姿节点的多条边，利用位姿节点和边之间的关系，图优化构建残差带权平方和函数，再通过萤火虫算法优化位姿节点来进行全局优化，减少了系统计算量，避免内存消耗随时间推移急剧增加带来的时延问题，能够避免集中式结构组合导航系统不能满足实时性检测需求的缺陷，利用鲁棒性较强的萤火虫算法进行全局优化简单且易于实现，能够避免分布式滤波需要解决各局部状态动力学关系的问题。The present invention adopts the above-mentioned technical scheme, and has the following beneficial effects: the present invention combines the firefly algorithm with graph optimization, and proposes an integrity assessment method for a multi-sensor integrated navigation system. The method first expresses the multi-sensor observation vector as Multiple edges of the pose node, using the relationship between the pose node and the edge, graph optimization constructs the residual weighted square sum function, and then optimizes the pose node through the firefly algorithm to perform global optimization, which reduces the amount of system calculations and avoids The time delay problem caused by the sharp increase of memory consumption over time can avoid the defect that the centralized structure combined navigation system cannot meet the real-time detection requirements, and the global optimization using the robust firefly algorithm is simple and easy to implement, which can avoid Distributed filtering needs to solve the problem of the dynamic relationship of each local state.
附图说明Description of drawings
图1为本申请评估组合导航系统完好性的流程图。Figure 1 is a flow chart of the application for assessing the integrity of the integrated navigation system.
图2为图优化的示意图。Figure 2 is a schematic diagram of graph optimization.
具体实施方式Detailed ways
下面结合附图对发明的技术方案进行详细说明。The technical solution of the invention will be described in detail below in conjunction with the accompanying drawings.
为满足不断提高的定位技术精度和可靠性需求，本发明公开了一种萤火虫算法和图优化相结合方法，用于评估卫星/惯性/视觉里程计组合导航系统的完好性(完好性包括两方面的含义：一是对于超过告警阀值的任何故障都要在给定的告警时间内发出警告；二是完好性风险概率必须在一定范围内,完好性风险概率是指导航位置信息超过了告警阀值这个事件被漏检的概率)。In order to meet the continuously improving positioning technology accuracy and reliability requirements, the present invention discloses a combination method of firefly algorithm and graph optimization, which is used to evaluate the integrity of satellite/inertial/visual odometer integrated navigation system (integrity includes two aspects Meaning: First, for any fault exceeding the alarm threshold, a warning must be issued within a given alarm time; second, the integrity risk probability must be within a certain range, and the integrity risk probability means that the navigation position information exceeds the alarm threshold. is the probability that this event is missed).
视觉里程计主要依靠连接到移动物体上的视觉传感器，如单目相机，通过分析处理相关图像序列来确定机器人的运动，利用相邻图像间的相似性来估计物体的运动。将视觉里程计和传统的GPS/IMU导航系统相结合成为多传感器组合导航系统的一个发展趋势。Visual odometry mainly relies on visual sensors connected to moving objects, such as monocular cameras, to determine the motion of the robot by analyzing and processing related image sequences, and to estimate the motion of objects by using the similarity between adjacent images. The combination of visual odometry and traditional GPS/IMU navigation system has become a development trend of multi-sensor integrated navigation system.
下面以包含GPS、IMU和视觉里程计多传感器组合导航系统为例阐述本发明的完好性评估方法，整个方法的流程如图1所示，具体包括如下6个步骤。The integrity evaluation method of the present invention is described below by taking a multi-sensor integrated navigation system including GPS, IMU and visual odometer as an example. The flow of the entire method is shown in Figure 1, and specifically includes the following 6 steps.
1)收集GPS、IMU和视觉里程计三者在k时刻的输出后对它们进行预处理，从而得到GPS、IMU和视觉里程计采集的东北天坐标系下的位置信息以及速度信息构建GPS、IMU和视觉里程计的观测量z10、z20、z30：1) Collect the output of GPS, IMU and visual odometer at time k and preprocess them, so as to obtain the position information in the northeast sky coordinate system collected by GPS, IMU and visual odometer and speed information Construct the observations z10 , z20 , z30 of GPS, IMU and visual odometry:
其中，为GPS在东向、北向、天向的位置信息，为GPS在东向、北向、天向的速度信息，为IMU在东向、北向、天向的位置信息，为IMU在东向、北向、天向的速度信息，为视觉里程计在东向、北向、天向的位置信息，为视觉里程计在东向、北向、天向的速度信息。in, is the position information of GPS in the east, north and sky directions, is the speed information of GPS in the east direction, north direction and sky direction, is the location information of the IMU in the east, north, and sky directions, is the speed information of the IMU in the east direction, north direction and sky direction, is the position information of the visual odometry in the east, north, and sky directions, is the velocity information of the visual odometry in the east, north, and sky directions.
2)利用图优化展开分析，起点x0和顶点x1,x2,x3均为机器人位姿节点，对于安装在同一载体上的多传感器而言，起点x0为公共位姿节点，三组观测量信息分别对应于图优化中起点x0与三个顶点确定的三条边，如图2所示，由于它们是不同传感器观测的同一时刻同一状态的信息，理想情况下等同，顶点x1、x2、x3相互连接构成的边所代表的观测量为0，2) Using graph optimization to expand analysis, the starting point x0 and vertices x1 , x2 , x3 are robot pose nodes. For multiple sensors installed on the same carrier, the starting point x0 is a common pose node. The group observation information corresponds to the three edges determined by the starting point x0 and the three vertices in the graph optimization, as shown in Figure 2. Since they are the information of the same state at the same time observed by different sensors, they are ideally equivalent, and the vertex x1 , x2 , x3 are connected to each other, and the observation quantity represented by the side is 0,
于是，我们构建如下边的关系：Therefore, we construct the following relationship:
zi0＝xi-x0，i＝1,2,3，zi0 = xi −x0 , i = 1, 2, 3,
xi＝xj，i,j取1,2,3且i≠j，xi ＝xj , i, j take 1, 2, 3 and i≠j,
等价地，Equivalently,
fi0＝xi-x0-zi0＝0，i＝1,2,3，fi0 =xi -x0 -zi0 =0, i=1,2,3,
fij＝xi-xj＝0，i,j取1,2,3且i≠j，fij ＝xi -xj ＝0, i, j take 1, 2, 3 and i≠j,
构建残差带权平方和函数F(X)来计算最优结果：Construct the residual weighted sum of squares function F(X) to calculate the optimal result:
其中，zij为位姿节点xi和位姿节点xj之间的边所表示的观测信息，e(xi,xj,zij)为表示xi和xj之间的关系与观测zij有多吻合的向量误差矩阵，在本例中向量误差函数表示为fij，本式中，i、j可取值为0，故fij包括了fi0的情况，Ωij为e(xi,xj,zij)的权重，X＝[x0,x1,x2,x3]，M＝{0,1,2,3}，wij为误差fij对应的权重。Among them, zij is the observation information represented by the edge between pose node xi and pose node xj , e(xi , xj , zij ) is the relationship between xi and xj and the observation In this example, the vector error function is expressed as fij . In this formula,i and j can take the value of 0, so f ij includes the case of f i0, and Ω ijise( xi , xj , zij ), X=[x0 , x1 , x2 , x3 ], M={0,1,2,3}, wij is the weight corresponding to the error fij .
3)引入萤火虫算法求解图优化中的F(X)3) Introduce the firefly algorithm to solve F(X) in graph optimization
a)初始化算法基本参数，随机生成n只萤火虫，设置最大吸引度β0，光强吸收系数γ，步长因子α，最大迭代次数MaxGeneration或搜索精度ε，以X(m)表示第m只萤火虫代表的位姿节点集合(m＝1,2,3,…,n)；a) Initialize the basic parameters of the algorithm, randomly generate n fireflies, set the maximum attraction β0 , the light intensity absorption coefficient γ, the step size factor α, the maximum number of iterations MaxGeneration or the search accuracy ε, and X(m) represents the mth firefly Representative pose node set (m=1,2,3,...,n);
b)随机初始化萤火虫的位置，以F(X)为目标函数，计算目标函数的最小值进而确定萤火虫群体的最大荧光亮度I0：b) Randomly initialize the position of fireflies, take F(X) as the objective function, calculate the minimum value of the objective function and then determine the maximum fluorescence brightness I0 of the firefly population:
其中，q>0且为常数；Among them, q>0 and is a constant;
c)计算群体中萤火虫的相对亮度I和吸引度β(r)，根据相对亮度决定萤火虫的移动方向：c) Calculate the relative brightness I and attractiveness β(r) of fireflies in the group, and determine the moving direction of fireflies according to the relative brightness:
其中，I0表示最亮萤火虫的亮度，即自身荧光亮度，与目标函数值相关，目标函数值越优，自身亮度越高；β0表示最大吸引度，即光源处的吸引度，γ表示光吸收系数，因为荧光会随着距离的增加和传播媒介的吸收逐渐减弱，所以设置光强吸收系数以体现此特性，可设置为常数；rab是萤火虫a与萤火虫b之间的距离,以欧几里得距离来表示；Among them, I0 represents the brightness of the brightest firefly, that is, the autofluorescence brightness, which is related to the objective function value. The better the objective function value, the higher the self-brightness; Absorption coefficient, because the fluorescence will gradually weaken with the increase of distance and the absorption of the propagation medium, so the light intensity absorption coefficient is set to reflect this characteristic, which can be set as a constant; rab is the distance between firefly a and firefly b, expressed in ohms expressed in miles;
d)更新萤火虫的空间位置，对处在最佳位置的萤火虫进行随机移动：d) Update the spatial position of the fireflies, and randomly move the fireflies in the best position:
Xa(t+1)＝Xa(t)+β(Xb(t)-Xa(t))+α(rand-1/2)，Xa (t+1)=Xa (t)+β(Xb (t)-Xa (t))+α(rand-1/2),
Xa(t)、Xa(t+1)为t时刻、t+1时刻萤火虫a的空间位置，Xb(t)为t时刻萤火虫b的空间位置，β(Xb(t)-Xa(t))表示t时刻萤火虫b对萤火虫a的吸引度，α为基于随机数(rand-1/2)的步长因子；Xa (t), Xa (t+1) are the spatial position of firefly a at time t and t+1, Xb (t) is the spatial position of firefly b at time t, β(Xb (t)-Xa (t)) represents the attraction of firefly b to firefly a at time t, and α is the step factor based on random number (rand-1/2);
e)根据更新后萤火虫的位置，返回b)重新计算萤火虫的亮度；e) According to the position of the firefly after the update, return to b) to recalculate the brightness of the firefly;
f)当满足搜索精度或达到最大搜索次数时转下一步；否则，搜索次数增加1，转c)，进行下一次搜索；f) Go to the next step when the search accuracy is satisfied or the maximum number of searches is reached; otherwise, the number of searches is increased by 1, and then go to c) for the next search;
g)输出全局极值点和最优个体值,对应上述图优化问题中的最优结果，即X如何取值时，F(X)取得最小值。g) Output the global extremum point and the optimal individual value, corresponding to the optimal result in the above graph optimization problem, that is, when X takes the value, F(X) obtains the minimum value.
4)计算检验统计量rk和检测门限T64) Calculate the test statistic rk and the detection threshold T6
将k时刻的F(X)标准化，可得检验统计量：Standardize F(X) at time k to obtain the test statistic:
rk服从自由度为6的χ2分布，给定误警率PFA，根据概率分布密度函数可确定检测门限T6:rk obeys the χ2 distribution with 6 degrees of freedom, given the false alarm rate PFA , according to the probability distribution density function The detection threshold T6 can be determined:
5)故障判定：当rk>T6时表示检测到故障，告警，需进行故障排除；当rk<T6时表示未检测到故障。5) Fault judgment: When rk >T6 , it means that a fault is detected, an alarm is issued, and troubleshooting is required; when rk <T6 , it means that no fault is detected.
6)时间更新，返回第1步继续执行后面的步骤。6) Time update, return to step 1 and continue to execute the following steps.
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