CN112987061B - Fuzzy fusion positioning method based on GPS and laser radar - Google Patents
Fuzzy fusion positioning method based on GPS and laser radar Download PDFInfo
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Classifications
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- 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
- G01S19/45—Determining position by combining measurements of signals from the satellite radio beacon positioning system with a supplementary measurement
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- 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
- G01S17/00—Systems using the reflection or reradiation of electromagnetic waves other than radio waves, e.g. lidar systems
- G01S17/86—Combinations of lidar systems with systems other than lidar, radar or sonar, e.g. with direction finders
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- 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
- G01S17/00—Systems using the reflection or reradiation of electromagnetic waves other than radio waves, e.g. lidar systems
- G01S17/88—Lidar systems specially adapted for specific applications
-
- 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
- G01S19/45—Determining position by combining measurements of signals from the satellite radio beacon positioning system with a supplementary measurement
- G01S19/47—Determining position by combining measurements of signals from the satellite radio beacon positioning system with a supplementary measurement the supplementary measurement being an inertial measurement, e.g. tightly coupled inertial
Abstract
The invention relates to a fuzzy fusion positioning method based on GPS and laser radar. And when the GPS signal is bad or no signal is generated, the laser radar is adopted to perform feature matching and positioning through a priori map, the position of the unmanned ship is predicted according to the acceleration and the speed measured by the inertial sensing unit, the GPS data and the laser radar positioning data are respectively processed through an extended Kalman filtering method, and then the unmanned ship is finally positioned according to the difference value between the GPS data and the laser radar positioning data after the filtering processing and the corresponding measurement data before the filtering and the precision of the sensor by a fuzzy algorithm.
Description
Technical Field
The invention relates to the unmanned field, in particular to a GPS and laser radar based fuzzy fusion positioning method.
Background
With the continuous development of unmanned technology, unmanned application is becoming wider and wider, and unmanned ship application is gradually expanding from military field to civil field. Whether unmanned aerial vehicle, unmanned car, unmanned ship, to reach better result of use, accurate location is the biggest prerequisite. The traditional GPS positioning can basically meet civil demands in an open environment, but due to the complexity of the actual environment and the requirements of actual tasks, unmanned ships often inevitably need to pass through places where some GPS signals are not good or even not, such as bridge holes, tunnels and the like. At this time, relying on GPS alone causes a large positioning error. Therefore, it is necessary to use tools that can be positioned locally. The traditional GPS fusion INS performs positioning navigation, and can temporarily replace a GPS to perform positioning when a GPS signal is not good, but is not suitable for long-time independent positioning due to accumulated errors; at present, a laser radar is also used as a positioning method without a GPS, but partial data errors are larger due to the instability of the position of the laser radar on an unmanned ship, and most of the methods do not judge the position to process or simply filter the position, so that the positioning problem caused by the large errors is difficult to truly eliminate. In addition, the traditional GPS-free positioning navigation can select to directly discard all GPS data in a certain time period when the GPS signals are not good, and the GPS data is not fully utilized. The GPS and laser radar fusion positioning method provided by the patent utilizes all data to the greatest extent so that the positioning after fusion can reach higher precision. The existing positioning methods are therefore to be further perfected.
Disclosure of Invention
The invention aims to provide a positioning method based on fuzzy fusion of GPS and laser radar, which solves the problem of accurate positioning of unmanned ships when GPS signals are weakened or no GPS signals exist, and can still ensure accurate positioning of unmanned ships when large errors occur in GPS positioning or laser radar positioning.
In order to achieve the above purpose, the technical scheme of the invention is as follows: a fuzzy fusion positioning method based on GPS and laser radar comprises the following steps:
acquiring the acceleration of the unmanned ship in real time through the inertial sensing unit to calculate and acquire the current predicted position coordinate (namely acquiring the acceleration of the unmanned ship through the inertial sensing unit and combining the position estimation current predicted position coordinate of the unmanned ship at the previous moment);
acquiring the current global position coordinate of the unmanned ship through the GPS, and filtering GPS positioning data according to the sensor precision of the GPS by an extended Kalman filtering method to obtain the global positioning of the unmanned ship; if the GPS signal is good, the GPS positioning data is used as an unmanned ship positioning result; the extended Kalman filtering method takes the predicted position coordinates as state predicted values, takes displacement difference values generated in the period of a system obtaining primary positioning data as process noise, takes GPS data as measured values, takes GPS sensor measurement precision as measurement noise, and determines filtering weights between the state predicted values and the measured values through the magnitude relation between the process noise and the measurement noise;
if the GPS signal is not good, acquiring surrounding environment point cloud information and distance information through a laser radar, acquiring the current local position coordinate of the unmanned ship through matching with a built two-dimensional grid map of the surrounding environment, and filtering laser radar positioning data according to the sensor precision of the laser radar by an extended Kalman filtering method to acquire the local positioning of the unmanned ship; the extended Kalman filtering method is consistent with the method for GPS data filtering, and is different in that positioning data obtained by the laser radar are used as measured values;
obtaining local positioning data of a laser radar: firstly, according to point cloud information and distance information of a surrounding environment obtained by scanning the surrounding environment by a 360-degree scanning laser radar, a two-dimensional grid map is established by a cartographer positioning mapping method; according to the grid map, the laser radar obtains local accurate positioning of the laser radar in the grid map by scanning the point cloud distribution condition and the distance of the surrounding environment, matching the point cloud distribution condition and the distance with the grid map and combining laser odometer information; obtaining accurate positioning through coordinate conversion according to global coordinates corresponding to a given grid map starting point and the local positioning coordinates in the grid map;
determining GPS data for modeling by knowledge of the GPS positioning accuracyA fuzzy subset of the paste, a domain of the discourse, and a membership function. Because the data error is larger when the GPS signal is not good, the fuzzy subset used in the GPS data fuzzification is set { N, Z, P } with the membership number of 3, and the argument is valuedR is GPS sensor positioning accuracy. According to the real-time positioning requirement of the unmanned ship, adopting a simpler triangle membership function with smaller calculated quantity, and determining the specific expression of the triangle membership function according to the fuzzy subset and the domain as follows:
VG is the GPS data innovation value;
if the filtered GPS data innovation value is not inIf the GPS data is unavailable, otherwise, the new value corresponding to the filtered GPS data is brought into the membership function to obtain the membership of the new value of the GPS data in each fuzzy value, weighting and summing are carried out to obtain unreliable indexes after the GPS data is fuzzy, and the weighting values are respectively assigned to 0.25,0.5,0.25;
determining a fuzzy subset, a domain and a membership function of the laser radar positioning data for fuzzification through knowledge of the positioning accuracy of the laser radar. Using a set { NL, NS, Z, PS, PL } with fuzzy subset membership of 5 in fuzzifying the laser radar positioning data according to the laser radar sensor precision, wherein the argument value isRp is the lidar sensor accuracy. The triangle membership function with simpler calculation amount and smaller calculation amount is adopted, and the specific expression for determining the triangle membership function according to the fuzzy subset and the domain is as follows:
VP is the laser radar positioning coordinate innovation value, rp is the laser radar measurement precision;
if the filtered laser radar data innovation value is not inIf the laser radar positioning data are unavailable, otherwise, the filtered laser radar positioning data are brought into the membership function to obtain membership of the laser radar positioning data in each fuzzy value, weighted summation is carried out to obtain fuzzy laser radar unreliable indexes, and the weighted values are respectively 0.1,0.2,0.4,0.2,0.1;
if at least one of the two types of data is available, the available data is subjected to weight distribution according to the unreliable index, and meanwhile, the sensor data weight is distributed again according to the reliability of the data, namely the innovation value of the filtered data, so that final weight is obtained, and the sensor data available after filtering is subjected to weighted summation according to the final weight so as to obtain final positioning data; if both positioning data are not available, estimating the current position coordinate according to the previous position coordinate and the speed obtained by the inertial sensing unit.
Compared with the prior art, the invention has the following beneficial effects:
1. the method has a multi-level data processing process, and after the GPS data and the laser radar positioning data are respectively subjected to filtering processing, the GPS data and the laser radar positioning data are subjected to further fuzzy fusion processing;
2. the GPS data and the laser radar data are fully utilized for complementary fusion, so that the problems of positioning failure or large positioning error caused by individual sensor faults or accidental data large deviation are avoided, and the driving safety of the unmanned ship is improved;
3. the simple and efficient fusion strategy ensures that the requirement of system instantaneity is met while higher positioning accuracy is achieved.
Drawings
FIG. 1 is a flow chart of a positioning method based on GPS and laser radar fuzzy fusion;
FIG. 2 is a graph of membership functions provided by the present invention for blurring the filtered GPS data;
FIG. 3 is a graph of membership functions for blurring the filtered lidar positioning data provided by the present invention.
Detailed Description
The technical scheme of the invention is specifically described below with reference to the accompanying drawings.
It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the present application. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments in accordance with the present application. As used herein, the singular is also intended to include the plural unless the context clearly indicates otherwise, and furthermore, it is to be understood that the terms "comprises" and/or "comprising" when used in this specification are taken to specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof.
The invention provides a fuzzy fusion positioning method based on GPS and laser radar, which is characterized by comprising the following steps:
acquiring the acceleration of the unmanned ship in real time through an inertial sensing unit so as to calculate and obtain the current predicted position coordinate;
acquiring the current global position coordinate of the unmanned ship through the GPS, and filtering GPS positioning data according to the sensor precision of the GPS by an extended Kalman filtering method to obtain the global positioning of the unmanned ship; if the GPS signal is good, the GPS positioning data is used as an unmanned ship positioning result; if the GPS signal is not good, acquiring surrounding environment point cloud information and distance information through a laser radar, acquiring the current local position coordinate of the unmanned ship through matching with a built two-dimensional grid map of the surrounding environment, and filtering laser radar positioning data according to the sensor precision of the laser radar by an extended Kalman filtering method to acquire the local positioning of the unmanned ship;
determining membership functions according to the sensor precision of the GPS and the laser radar and the innovation values of the global positioning data and the local positioning data after filtering;
judging the reliability of GPS global positioning data and laser radar local positioning data through a fuzzy algorithm according to the membership function, and judging whether the GPS global positioning data and the laser radar local positioning data are available or not according to the reliability; if at least one of the GPS global positioning data and the laser radar local positioning data is available, the available data are subjected to weight distribution according to the reliability and the respective sensor precision of the GPS and the laser radar, and the weight distribution and the corresponding positioning data are subjected to weighted summation to obtain a current positioning result; and if both data are not available, taking the predicted position coordinate as the current positioning coordinate.
The following is a specific implementation procedure of the present invention.
Fig. 1 is a flowchart of a fuzzy fusion positioning method based on GPS and laser radar provided in this example, as shown in fig. 1, the fuzzy fusion positioning method based on GPS and laser radar provided in this embodiment includes the following steps:
s1, obtaining the predictive positioning of the unmanned ship through the inertial sensing unit. And obtaining the advancing acceleration of the unmanned ship through the accelerometer, carrying out integral calculation and obtaining the predicted positioning of the current moment of the unmanned ship by the position of the previous moment. The inertial sensing unit is integrated in the interior of the flight control system, and the flight control system is arranged in the middle position of the interior of the unmanned ship.
S2, global positioning of the unmanned ship is obtained through a GPS sensor, and the global positioning after filtering is obtained through extended Kalman filtering. The GPS is arranged on the front position of the surface of the unmanned ship body.
When the extended Kalman filtering is carried out, the and predicted positioning is used as a state predicted value, the global positioning is used as a measured value, the position offset estimation in the running period of the positioning system is used as process noise, and the sensor precision of the GPS is used as measurement noise.
And judging whether the magnitude of the filtered global positioning data innovation value exceeds a given threshold value, and if so, starting the laser radar positioning module.
S3, a laser radar positioning module comprises a laser radar map building part, a laser radar positioning part, a coordinate conversion part and an extended Kalman filtering part.
The laser radar map is built mainly through a cartographer positioning map building method, and the boundaries of the built grid map are clear.
And the laser radar is used for obtaining local relative positioning of the unmanned ship in the grid map by scanning surrounding environment point cloud information and distance information and matching according to the grid map.
And carrying out coordinate conversion on the local relative positioning according to the local relative positioning and the mapping of the origin of the grid map in the global positioning to obtain positioning data based on the laser radar.
And (3) performing extended Kalman filtering, processing through the extended Kalman filtering according to the positioning data based on the laser radar and the precision of the used laser radar sensor, wherein the positioning data based on the laser radar is used as a measured value, the precision of the laser radar sensor is used as measured noise, and the filtered laser radar positioning data is obtained through calculation.
S4, fusing two sets of positioning data, including the determination of fuzzy subsets, domains, membership functions and the determination of fusion rules.
According to the fusion condition that the error of the filtered global positioning data is large, determining a fuzzy subset used in the filtered global positioning data fuzzification as a set { N, Z, P } with the membership number of 3, and performing discourse domain valueR is GPS sensor positioning accuracy. According to the real-time positioning requirement of the unmanned ship, a simpler triangle membership function with smaller calculation amount is adopted, and the triangle membership function is determined according to the fuzzy subset and the domain, as shown in fig. 2, and the specific expression is as follows:
VG is the GPS data innovation value, when VG exceeds the given threshold rangeWhen the data is not fused.
Using a set { NL, NS, Z, PS, PL } with fuzzy subset membership of 5 in fuzzifying the laser radar positioning data according to the laser radar sensor precision, wherein the argument value isRp is the lidar sensor accuracy. Determining a triangle membership function according to the fuzzy subset and the domain, as shown in fig. 3, wherein the specific expression is as follows:
VP is the laser radar positioning coordinate innovation value, rp is the laser radar measurement precision, and when VP exceeds the given threshold rangeWhen the data is not fused.
When both sets of data meet a given threshold condition, substituting new information values corresponding to the two sets of filtered data into corresponding membership functions to calculate membership degrees corresponding to the members of each fuzzy subset, and carrying out weighted summation with corresponding weights to obtain the credibility of the set of filtered positioning data, wherein the credibility is gR and pR respectively;
normalizing the reliability values gR and pR corresponding to the two groups of data and reassigning the normalized reliability values according to the accuracy of the two sensors, namelyAnd obtaining the final fusion allocation weight.
And calculating a final fusion positioning result, namely x=x1+gR+x2 pR according to the fusion allocation weight.
If only one group of data meets the given threshold condition, the group of filtered positioning data is used as a current positioning final result; and if both sets of data do not meet the given threshold condition, taking the predicted positioning value as a final current positioning result.
The embodiment combines the GPS and the laser radar to position, so that the system positioning is not influenced by the strength of GPS signals and sudden faults of the sensor, and meanwhile, the fusion of the GPS data and the laser radar positioning data is realized by using a simple fuzzy decision method, so that the time cost caused by complex decision calculation is avoided, the requirement of the system instantaneity is met, and the better fusion effect is achieved.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The above description is only a preferred embodiment of the present invention, and is not intended to limit the invention in any way, and any person skilled in the art may make modifications or alterations to the disclosed technical content to the equivalent embodiments. However, any simple modification, equivalent variation and variation of the above embodiments according to the technical substance of the present invention still fall within the protection scope of the technical solution of the present invention.
Claims (6)
1. A fuzzy fusion positioning method based on GPS and laser radar is characterized by comprising the following steps:
acquiring the acceleration of the unmanned ship in real time through an inertial sensing unit so as to calculate and obtain the current predicted position coordinate;
acquiring the current global position coordinate of the unmanned ship through the GPS, and filtering GPS positioning data according to the sensor precision of the GPS by an extended Kalman filtering method to obtain the global positioning of the unmanned ship; if the GPS signal is good, the GPS positioning data is used as an unmanned ship positioning result; if the GPS signal is not good, acquiring surrounding environment point cloud information and distance information through a laser radar, acquiring the current local position coordinate of the unmanned ship through matching with a built two-dimensional grid map of the surrounding environment, and filtering laser radar positioning data according to the sensor precision of the laser radar by an extended Kalman filtering method to acquire the local positioning of the unmanned ship;
determining membership functions according to the sensor precision of the GPS and the laser radar and the innovation values of the global positioning data and the local positioning data after filtering; the specific determination mode of the membership function is as follows:
when the GPS signal is bad, the fuzzy subset used in the global positioning data fuzzification of the GPS is set { N, Z, P } with the membership number of 3, and the argument is valuedR is the sensor precision of the GPS;
according to the real-time positioning requirement of the unmanned ship, adopting a triangle membership function, and determining the specific expression of the triangle membership function according to the fuzzy subset and the domain as follows:
VG is the GPS data innovation value; if the GPS data innovation value is not in after filteringIf the GPS data is unavailable, otherwise, the innovation value corresponding to the filtered GPS data is brought into the membership function to obtain the membership of the GPS data innovation value in each fuzzy value, and weighted summation is carried out to obtain an unreliable index after the GPS data is fuzzy;
the fuzzy subset member number is set { NL, NS, Z, PS, PL }, and the argument value is equal to that of 5 in the fuzzy of the local positioning data of the laser radar according to the sensor precision of the laser radarRp is the sensor accuracy of the lidar itself; root of Chinese characterAccording to the real-time positioning requirement of the unmanned ship, adopting a triangle membership function, and determining the specific expression of the triangle membership function according to the fuzzy subset and the domain as follows:
VP is a laser radar positioning coordinate innovation value, rp is laser radar measurement precision; if the filtered laser radar data innovation value is not inIf the laser radar positioning data is not available, otherwise, the filtered laser radar positioning data is brought into a membership function to obtain membership of the laser radar positioning data in each fuzzy value, and weighted summation is carried out to obtain a fuzzy laser radar unreliable index;
judging the reliability of GPS global positioning data and laser radar local positioning data through a fuzzy algorithm according to the membership function, and judging whether the GPS global positioning data and the laser radar local positioning data are available or not according to the reliability; if at least one of the GPS global positioning data and the laser radar local positioning data is available, the available data are subjected to weight distribution according to the reliability and the respective sensor precision of the GPS and the laser radar, and the weight distribution and the corresponding positioning data are subjected to weighted summation to obtain a current positioning result; and if both data are not available, taking the predicted position coordinate as the current positioning coordinate.
2. The positioning method based on the fuzzy fusion of the GPS and the laser radar is characterized in that the laser radar is arranged at the middle position of the top of an unmanned ship, a two-dimensional grid map of an environment needing to be subjected to tasks is established by using a laser radar scanning method through a remotely controlled unmanned ship by adopting a cartograph positioning map-establishing method before the unmanned ship is started, and the laser radar obtains the current relative positioning of the unmanned ship on the two-dimensional grid map, namely a local position coordinate by scanning surrounding environment information and matching with the two-dimensional grid map during autonomous positioning navigation; and filtering the local positioning data of the laser radar through an extended Kalman filtering algorithm according to the difference value between the predicted position coordinates and the local position coordinates of the laser radar and the sensor accuracy of the laser radar.
3. The fuzzy fusion positioning method based on the GPS and the laser radar according to claim 1, wherein after the GPS global positioning data or the laser radar local positioning data are judged to be available, corresponding membership values of corresponding positioning data are substituted into the membership functions corresponding to the GPS global positioning data or the laser radar local positioning data to obtain corresponding membership degrees of fuzzy subset members, unreliable indexes of the GPS or the laser radar are respectively obtained through a weighted summation mode, normalization processing is carried out, meanwhile, the unreliable indexes after normalization processing are judged again according to the respective sensor precision of the GPS and the laser radar, and finally, fusion weights of the GPS and the laser radar are obtained, and weighted summation is carried out on the filtered positioning data corresponding to the fusion weights to obtain a final positioning result.
4. A positioning method based on fuzzy fusion of GPS and lidar according to claim 3, wherein the innovation value is the difference between the positioning data obtained by GPS or lidar and the positioning data obtained by filtering, that is, the difference between the positioning data measured by GPS or lidar and the positioning data obtained by filtering, and the larger the difference is, the larger the positioning error of GPS or lidar is, and the lower the reliability is.
5. The method of claim 4, wherein determining whether the data is available for fusion is based on whether the difference between the GPS or lidar filtered data and the predicted data exceeds a set threshold, the thresholds being set to respectivelyAnd->
6. The positioning method based on the fuzzy fusion of the GPS and the laser radar according to claim 1, wherein the GPS positioning data or the laser radar positioning data are processed and fused by using an extended Kalman filtering algorithm and a fuzzy algorithm successively.
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