CN110260885B - Satellite/inertia/vision combined navigation system integrity evaluation method - Google Patents
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Abstract
The invention discloses a satellite/inertia/vision combined navigation system integrity evaluation method, relates to integrity evaluation of a multi-sensor combined navigation system, and belongs to the technical field of calculation, reckoning and counting. The method comprises the steps of preprocessing measured values of each sensor at the current moment to obtain observation vectors of each sensor at the current moment, representing the observation vectors of each sensor at the current moment as a plurality of edges with common pose nodes, optimizing and constructing a residual weighted square sum function by using the relationship between the pose nodes and the edges, optimizing the pose nodes by adopting a firefly algorithm with strong robustness to solve the optimal solution of the residual weighted square sum function so as to realize global optimization, then calculating test statistics and comparing the test statistics with a detection threshold so as to judge whether a fault occurs or not, reducing the system computation, avoiding the time delay problem caused by the rapid increase of memory consumption along with the time lapse, and meeting the real-time detection requirement.
Description
Technical Field
The invention discloses a satellite/inertia/vision combined navigation system integrity evaluation method, relates to integrity evaluation of a multi-sensor combined navigation system, and belongs to the technical field of calculation, reckoning and counting.
Background
In recent years, new low-cost, small-sized and light-weight navigation sensors such as MEMS (Micro-Electro-Mechanical systems) sensors, MSIS (Micro Solid-state Inertial sensors), fiber optic gyroscopes and GNSS (global navigation Satellite System) receivers, as well as high-speed and large-capacity embedded microprocessors and distributed modular electronic devices, have rapidly developed, and have developed a wave of research on multi-Sensor navigation systems.
In order to improve the accuracy and reliability of the multi-sensor navigation system, experts at home and abroad propose various software and hardware redundancy technologies, integrity monitoring algorithms and multi-sensor information fusion algorithms. A great deal of research finds that the fault tolerance capability of the navigation system is mainly determined by a system hardware redundancy structure and an integrity monitoring algorithm. At present, the research methods for monitoring the integrity of navigation systems at home and abroad are mainly divided into a snapshot method and a continuous method.
The snapshot method utilizes measurement information of a single epoch to detect and isolate instantaneous faults, is usually used for faults with larger changes, typical methods include a least square residual method, a parity vector method and the like, and the RAIM algorithm about GPS which is widely used for research at home and abroad belongs to the snapshot method. The global least square method proposed by yankeen waves et al improves the reliability of the RAIM algorithm compared to the conventional least square residue method. Aiming at the problems of the least square method in the satellite fault detection and identification, Wangershi et al take diagonal elements of a covariance matrix in a measurement equation as weighting factors, and further obtain a satellite fault detection algorithm based on the weighted least square method. Songkai et al propose a comprehensive RAIM algorithm based on Kalman filtering and parity vector method, which improves the detection rate of small pseudo-range deviation and reduces the requirement for the number of visible satellites compared with the conventional RAIM algorithm. Welching et al studied the neural network-based odd-even vector compensation method to solve the problem of fault diagnosis of the skewed system. However, although the snapshot method can effectively detect step failures of the inertial sensor or the GPS signal, it is difficult to detect soft failures caused by factors such as drift of the inertial device.
For detection of the gradual error, a continuous method based on history accumulated information, such as an SPRT method or a kinetic-based model method, is generally used. Aiming at the characteristics of noise distribution observed by a GPS receiver and the problems of particle degradation and sampling exhaustion of basic particle filtering, Wangershi et al provides a global positioning system RAIM algorithm organically combining particle filtering and a likelihood ratio method based on resampling by a genetic algorithm.
Although many learners have made extensive research on multi-sensor navigation systems, most employ filtering algorithms for integrity monitoring. At present, many INS/GPS combined navigation systems adopt a centralized filtering structure and show a better effect, but with the increase of the number of sensors, the calculated amount is increased rapidly, so that time delay is brought, the real-time requirement of a user cannot be met, and the fault isolation is not facilitated. The distributed filter structure provides a flexible and variable scheme for the multi-sensor navigation system, but the problem of dynamic relation of each local state still needs to be solved.
The application aims to provide a method for combining a firefly algorithm and graph optimization, and global optimization of a multi-sensor navigation system is achieved through a small amount of calculation.
Disclosure of Invention
The invention aims to provide a satellite/inertia/vision combined navigation system integrity evaluation method aiming at the defects of the background technology, realizes the global optimization of a multi-sensor combined navigation system through less calculation amount, and solves the technical problem that the existing multi-sensor navigation system integrity evaluation method cannot meet the real-time requirement.
The invention adopts the following technical scheme for realizing the aim of the invention:
a satellite/inertia/visual integrated navigation system integrity evaluation method includes preprocessing measured values of each sensor at the current moment to obtain observation vectors of each sensor at the current moment, representing the observation vectors of each sensor at the current moment as a plurality of edges with common pose nodes, optimizing and constructing a residual weighted square sum function by using a graph according to relations between the pose nodes and the edges, optimizing the pose nodes by adopting a firefly algorithm to solve an optimal solution of the residual weighted square sum function, standardizing the optimal solution of the residual weighted square sum function to obtain a test threshold statistic, setting the test threshold statistic according to a given false alarm rate, and judging a fault when the test statistic exceeds the test threshold.
Furthermore, in the integrity evaluation method of the satellite/inertia/vision combined navigation system, the observation vector of each sensor at the current moment comprises position information and speed information of each sensor in a northeast coordinate system.
Further, in the integrity evaluation method of the satellite/inertia/vision combined navigation system, a residual weighted square sum function is constructed through the position of the pose nodes and the influence of the position of the pose nodes on the observation information represented by the edges between the pose nodes.
Further, in the integrity evaluation method for the satellite/inertia/vision combined navigation system, the process of solving the optimal solution of the residual weighted sum of squares function by optimizing the pose nodes by using the firefly algorithm is as follows: mapping a group of pose nodes obtained in the graph optimization process to the firefly position, determining the maximum fluorescence brightness of the firefly population by taking the minimum value of the residual weighted square sum function as a target, determining the movement direction of the firefly according to the maximum fluorescence brightness of the firefly population, randomly moving the firefly at the optimal position by combining the maximum attraction of the firefly at the optimal position to update the space position of the firefly, recalculating the maximum fluorescence brightness of the firefly population, and then searching for the next time until the requirement of the search precision is met or the maximum search frequency is reached.
Further, in a method for evaluating integrity of a satellite/inertial/visual integrated navigation system, the test statistic obtained by normalizing the optimal solution of the residual weighted sum of squares function is as follows:
according to a given false alarm rate PFASetting a detection threshold T6The expression of (a) is:
wherein r iskFor test statistic at time k, rkCompliance X degree of freedom of 62Distribution, F (X) is the optimal solution of the residual weighted sum of squares function,
is the variance of the received signal and the received signal,
is a probability distribution density function.
Still further, in a method for evaluating the integrity of a satellite/inertial/visual integrated navigation system, a weighted sum of squares function of residual errors is
zijAs pose node xiAnd pose node xjThe observation information, e (x), represented by the edges betweeni,xj,zij) Is represented by xiAnd xjThe relation between and observation zijVector error matrix with multiple fits, fijIs represented by xiAnd xjThe relation between and observation zijWith multiple coincident vector error functions, ΩijIs e (x)i,xj,zij) Weight of (1), wijIs fijX ═ X0,x1,x2,x3]，M＝{0,1,2,3}。
By adopting the technical scheme, the invention has the following beneficial effects: the invention combines a firefly algorithm and graph optimization, and provides a method for evaluating the integrity of a multi-sensor combined navigation system.
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FIG. 1 is a flow chart illustrating the integrity of the integrated navigation system of the present application.
Fig. 2 is a schematic diagram of the graph optimization.
Detailed Description
The technical scheme of the invention is explained in detail in the following with reference to the attached drawings.
In order to meet the requirements of continuously improved positioning technology precision and reliability, the invention discloses a firefly algorithm and graph optimization combined method for evaluating the integrity of a satellite/inertia/visual odometer combined navigation system (the integrity comprises two meanings, namely, the integrity is in need of giving an alarm within a given alarm time for any fault exceeding an alarm threshold value, and the integrity risk probability is in need of being within a certain range, wherein the integrity risk probability refers to the probability of missed detection of an event that navigation position information exceeds the alarm threshold value).
Visual odometers mainly rely on visual sensors, such as monocular cameras, attached to moving objects to determine the motion of the robot by analyzing and processing sequences of related images, and to estimate the motion of the objects using similarities between adjacent images. The combination of the visual odometer and the traditional GPS/IMU navigation system becomes a development trend of the multi-sensor combined navigation system.
The integrity assessment method of the present invention is described below by taking a multi-sensor integrated navigation system including a GPS, an IMU and a visual odometer as an example, and the flow of the whole method is shown in fig. 1, which specifically includes the following 6 steps.
1) The GPS, the IMU and the visual odometer are preprocessed after the outputs of the GPS, the IMU and the visual odometer at the time k are collected, so that the position information of the GPS, the IMU and the visual odometer under an east-north coordinate system is obtained
And speed information
Constructing observations z for GPS, IMU and visual odometer10、 z20、z30：
Wherein the content of the first and second substances,
the position information of the GPS in the east direction, the north direction and the sky direction,
the speed information of the GPS in the east direction, the north direction and the sky direction,
the position information of the IMU in the east direction, the north direction and the sky direction,
the velocity information of the IMU in the east direction, the north direction and the sky direction,
for the position information of the visual odometer in the east direction, the north direction and the sky direction,
the speed information of the visual odometer in east, north and sky directions is shown.
2) Using graph-optimized unfolding analysis, starting point x0And vertex x1，x2，x3All are robot pose nodes, and for multiple sensors mounted on the same carrier, the starting point x0For a common pose node, three groups of observed quantity information respectively correspond to a starting point x in graph optimization0The three edges defined by the three vertices, as shown in FIG. 2, are ideally equivalent since they are information of the same state at the same time and observed by different sensors, vertex x1、x2、x3The observed quantity represented by the edges formed by the interconnection is 0,
then, we construct the following relationship:
zi0＝xi-x0，i＝1，2，3，
xi＝xji, j is 1, 2, 3 and i ≠ j,
in the equivalent way,
fi0＝xi-x0-zi0＝0，i＝1，2，3，
fij＝xi-xj0, i, j is 1, 2, 3 and i ≠ j,
constructing a residual weighted sum of squares function F (X) to calculate an optimal result:
wherein z isijAs pose node xiAnd pose node xjThe observation information, e (x), represented by the edges betweeni，xj，zij) Is represented by xiAnd xjThe relation between and observation ziiThere is a multi-matched vector error matrix, in this case the vector error function is denoted fijIn this formula, i and j can be 0, so fijComprises fi0Case (q) ofijIs e (x)i，xj，zij) X ═ X0，x1，x2，x3]，M＝{0，1，2，3}，wijIs an error fijThe corresponding weight.
3) Introduction of firefly algorithm solution graph optimization F (X)
a) Initializing basic parameters of an algorithm, randomly generating n fireflies, and setting the maximum attraction β0The light intensity absorption coefficient γ, the step factor α, the maximum iteration number MaxGeneration or the search precision e, and x (m) represents the pose node set represented by the mth firefly (m is 1, 2, 3.. multidot.n);
b) randomly initializing the position of the firefly, taking F (X) as an objective function, calculating the minimum value of the objective function and further determining the maximum fluorescence brightness I of the firefly population0：
Wherein q > 0 and is a constant;
c) calculating relative brightness I and attraction β (r) of firefly in the population, and determining the movement direction of firefly according to the relative brightness:
wherein, I0Expressing the luminance of the brightest firefly, i.e., autofluorescence luminance, in relation to the objective function value, the more excellent the objective function value, the higher the autofluorescence, β0The maximum attraction degree is shown, namely the attraction degree at the light source, gamma is shown as a light absorption coefficient, and the light intensity absorption coefficient is set to embody the characteristic and can be set to be constant because the fluorescence is gradually weakened along with the increase of the distance and the absorption of the transmission medium; r isabIs the distance between firefly a and firefly b, expressed as the euclidean distance;
d) updating the spatial position of the firefly, and randomly moving the firefly in the optimal position:
Xa(t+1)＝Xa(t)+β(Xb(t)-Xa(t))+α(rand-1/2)，
Xa(t)、Xa(t +1) is the spatial position of firefly a at time t and time t +1, Xb(t) spatial position of firefly b at time t, β (X)b(t)-Xa(t)) represents the attraction of firefly b to firefly a at time t, α being a step factor based on a random number (rand-1/2);
e) returning to b) recalculating the brightness of the firefly according to the updated position of the firefly;
f) when the search precision is met or the maximum search times are reached, the next step is carried out; otherwise, increasing the searching times by 1, turning to c), and performing the next searching;
g) and outputting a global extreme point and an optimal individual value, and obtaining a minimum value by F (X) when the optimal result in the graph optimization problem corresponds to the value of X.
4) Calculating a test statistic rkAnd a detection threshold T6
F (x) at time k is normalized to obtain test statistics:
rkcompliance X degree of freedom of 62Distribution, given false alarm rate PFAAccording to a probability distribution density function
A detection threshold T may be determined6：
5) And (3) fault judgment: when r isk＞T6The time indicates that the fault is detected, the alarm is given, and the fault is required to be eliminated; when r isk＜T6A time indicates that no fault was detected.
6) And (5) updating the time, returning to the step 1 and continuing to execute the following steps.
Claims (5)
1. A satellite/inertia/visual integrated navigation system integrity evaluation method is characterized in that measured values of sensors at the current moment are preprocessed to obtain observation vectors of the sensors at the current moment, the observation vectors of the sensors at the current moment are represented as a plurality of edges with common pose nodes, a residual weighted square sum function is constructed through graph optimization by utilizing the relation between the pose nodes and the edges, the pose nodes are optimized by adopting a firefly algorithm to solve the optimal solution of the residual weighted square sum function, the optimal solution of the residual weighted square sum function is standardized to obtain test statistics, a detection threshold is set according to a given false alarm rate, and a fault is judged when the test statistics exceeds the detection threshold, wherein,
the process of solving the optimal solution of the residual weighted sum of squares function by adopting the firefly algorithm to optimize the pose nodes is as follows: mapping a group of pose nodes obtained in the graph optimization process to the firefly position, determining the maximum fluorescence brightness of the firefly population by taking the minimum value of the residual weighted square sum function as a target, determining the movement direction of the firefly according to the maximum fluorescence brightness of the firefly population, randomly moving the firefly at the optimal position by combining the maximum attraction of the firefly at the optimal position to update the space position of the firefly, recalculating the maximum fluorescence brightness of the firefly population, and then searching for the next time until the requirement of the search precision is met or the maximum search frequency is reached.
2. The method of claim 1, wherein the observation vector of each sensor at the current time comprises position information and velocity information of each sensor in a coordinate system of northeast of the earth.
3. The method of claim 1, wherein the residual weighted sum-of-squares function is constructed from the pose nodes and the effect of the positions of the pose nodes on the observation information represented by the edges between the pose nodes.
4. The method of claim 1, wherein the test statistic obtained by normalizing the optimal solution of the residual weighted sum of squares function is:
according to a given false alarm rate PFASetting a detection threshold T6The expression of (a) is:
wherein r iskFor test statistic at time k, rkCompliance X degree of freedom of 62Distribution, FOptimal solution(X) is the optimal solution of the residual weighted sum of squares function,
is the variance of the received signal and the received signal,
is a probability distribution density function.
5. The method of claim 3, wherein the residual weighted sum of squares function is
zijIs the ith position node xiAnd j position node xjThe observation information, e (x), represented by the edges betweeni,xj,zij) Is represented by xiAnd xjThe relation between and observation zijVector error matrix with multiple fits, fijIs represented by xiAnd xjThe relation between and observation zijWith multiple coincident vector error functions, ΩijIs e (x)i,xj,zij) Weight of (1), wijIs fijX ═ X0,x1,x2,x3]，M＝{0,1,2,3}。
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