CN113933876A - Multi-satellite communication time difference positioning data fusion processing method - Google Patents
Multi-satellite communication time difference positioning data fusion processing method Download PDFInfo
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- 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
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- G01S19/421—Determining position by combining or switching between position solutions or signals derived from different satellite radio beacon positioning systems; by combining or switching between position solutions or signals derived from different modes of operation in a single system
- G01S19/423—Determining position by combining or switching between position solutions or signals derived from different satellite radio beacon positioning systems; by combining or switching between position solutions or signals derived from different modes of operation in a single system by combining or switching between position solutions derived from different satellite radio beacon positioning systems
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Abstract
The method for fusing and processing the multi-satellite communication time difference positioning data has the advantages of high time difference positioning speed and high precision. The invention is realized by the following technical scheme: after the multi-satellite communication radiation source time difference positioning data is obtained, a mirror image point eliminating module is adopted to classify all positioning points according to target numbers, a mirror image point eliminating algorithm is used to eliminate mirror image points in positioning point classification results, and non-mirror image points are output to a positioning point aggregation module; the normal point aggregation module carries out point aggregation according to a point aggregation algorithm to generate an aggregation point, and the aggregation point is output to the outlier point removing module; the outlier rejection module rejects outliers according to an outlier rejection algorithm and outputs non-outliers to the aggregation point filtering module; and the aggregation point filtering module filters the aggregation point trace based on an aggregation point filtering algorithm, and performs fusion processing by using a covariance intersection algorithm and a k-nearest neighbor sliding window least square algorithm to generate a final fusion result.
Description
Technical Field
The invention belongs to a target tracking technology in the technical field of target trace fusion, mainly relates to a trace point data fusion method in a motor target real-time monitoring system, and particularly relates to a fusion method of multi-satellite communication time difference positioning data with large errors.
Background
With the development of multi-platform communication technology and the progress of time difference measurement technology, the time difference measurement technology has become a main positioning method in modern positioning. The satellite positioning technology includes, in terms of the number of satellites, double-satellite time difference and frequency difference combined positioning, three-satellite time difference independent positioning, multi-satellite time difference and frequency difference combined positioning and the like. The two-satellite joint positioning needs to measure TDO A and FDO A at the same time, and a joint equation set is established to obtain the position of a target. The three-star or multi-star positioning can measure only TDO A, and can measure TDO A and FDO A simultaneously, and different combination modes are selected according to the requirements of different algorithms. Each positioning algorithm has advantages and disadvantages, and the optimal positioning model and algorithm can be obtained only by fully comprehensive comparison. In satellite positioning, measurements on target signals are based, with the most basic and most common measurements including direction measurements on target signals, time differences required to reach different receivers, and differences in signal transmission and reception frequencies. Different measurement methods and principles are provided for different measurement contents, and targets of any method and principle are always unified, namely, the error is reduced to the maximum extent, and the measurement precision is improved, so that the purpose of improving the satellite positioning precision is achieved. There are many passive direction-finding systems in positioning, but the basic principle is that when a signal enters a receiver system, the amplitude, phase and arrival time of the signal are different due to different entrance angles, and the entrance angle of the signal can be obtained by measuring the difference of the amplitude, phase and arrival time. Thus, logically, multiple antennas are required for direction finding, and they receive signals that differ in at least one of amplitude, phase, or time. This difference will be used to calculate the direction of the signal. Therefore, the direction finding is not a phase-amplitude method, a phase-phase method, a time difference method and a mixed method thereof from the aspect of system. In the signal transmission process, since the signal sending time is unknown, it is impossible to measure the absolute time required for the signal to reach each receiver from the target, therefore, the time difference required for positioning calculation can be obtained only by comparing the time difference of the same signal reaching different receivers. The satellite positioning is performed through various errors, and some errors have correlation, some errors have no correlation, and all the errors have respective characteristics. In summary, these errors can be classified into two categories, one being errors associated with the satellite itself and the other being errors in the signal processing process. The satellite self-error is an error related to a satellite, and comprises a satellite ephemeris error, a satellite clock error and the like. The satellite is influenced by various perturbation forces in actual operation. Therefore, there is necessarily an error between the position given by the satellite and the true position, which is referred to as ephemeris error, also called orbit error. The initial data error is determined by the quality of the satellite orbit determination system, such as the number and spatial distribution of orbit determination stations, the number and precision of observed values, an orbit model used in orbit calculation, the perfection degree of orbit determination software and the like. The satellite positioning uses the satellite position as a known reference value to determine the position of a point to be determined, so the ephemeris error of the satellite seriously affects the positioning accuracy and is an important error source in the satellite positioning process. Ephemeris error is a systematic error that cannot be eliminated by repeating the observation many times. At present, ephemeris error of a satellite is estimated and processed more difficultly, and the main reason is that the satellite is influenced by a plurality of perturbation forces in operation, and the acting forces are difficult to be measured fully and reliably through a ground monitoring station and the acting rule of the acting forces is difficult to master. In addition to the satellite ephemeris error and satellite clock error mentioned above, there is an error related to the earth, i.e. an error of the target geodetic height, also called earth form error. The earth's surface morphology necessarily introduces errors, which are systematic errors. Errors occur in the signal transmission process, mainly including ionosphere delay errors, troposphere delay errors and multipath effect errors in signal transmission. When the signal arrives at the receiver, the receiver may also introduce certain errors, including clock errors and position errors of the receiver. In actual signal measurement, the receiver antenna receives signals directly coming from the direction of a satellite and also receives signals reflected by other objects such as tall buildings, aerospace vehicles and the like, so that the received signals are mixed signals after interference of direct waves and reflected waves. Since the direct wave and each reflected wave have different paths, the signal is delayed, and a measurement error, called a multipath error, occurs. Many times, multipath errors are a major source of error. The multipath error can be regarded as a periodic error, and the error of satellite positioning includes a phase center position deviation of an antenna, a calculation error, an influence of earth rotation, tide correction, spoofing, and the like, in addition to the above-described main error.
Positioning is an important passive positioning method. The position of the target is acquired without emitting electromagnetic waves that illuminate the target, and we call such positioning a passive positioning. Passive location by no means requires a power source or power source, but merely means that the location station does not transmit electromagnetic signals to the object being located. Correspondingly, a positioning system in which a positioning station transmits a signal may be referred to as active positioning. By location, it is generally meant determining the position of an object on the earth's surface in some reference frame. Since the paths of the target source to different receivers are different, that is, there are distance differences, and in terms of time, there is a time difference when the same radiation source reaches different receivers, and it contains the spatial position information of the target, the target can be located by measuring the time difference of arrival. Since passive reconnaissance cannot directly know the transmission time of the radar pulse, it cannot be located by distance, but only by time difference of arrival. The accuracy of the time difference measurement positioning is largely determined by the measurement accuracy of the time of arrival and the positioning accuracy of the receiver platform itself. Because the measurement accuracy of the time difference is a key factor influencing the positioning accuracy, the time difference of the radiation source reaching the two receivers determines a hyperboloid (line), and the target position can be obtained by intersecting a plurality of hyperboloids. The time difference measurement positioning methods are more than ever, and include maximum likelihood estimation, least square estimation, minimum weighted mean square estimation and some direct calculation methods. A plurality of positioning systems adopting a TDOA time difference principle are typically provided with a "tower mala" improved "Weira (VERA)" system and a three-coordinate passive positioning system; a mobile passive positioning system; "happy A" air condition monitoring system, etc. Such systems are most suitable for air condition monitoring and as backup devices for Air Traffic Control (ATC) systems. The change rate of the target signal reaching the two receiver signals also contains the position and motion information of the target, and the state parameters of the target can be determined by extracting the change rate. Many positioning systems adopt a time difference/differential Doppler composite positioning system. In fact, the target is positioned by capturing and analyzing a target signal to obtain information such as the direction, time, amplitude, frequency, phase and the like of the signal, and the target is effectively positioned by utilizing the information and the relation between the target position and the motion state. The basic problem with the multi-station time difference measurement positioning technique is to give a set of platforms and their time difference measurements. The positioning accuracy is a function of relative geometric relation between the target and the receiver and measurement errors, so that the positioning accuracy is another optimal stationing form and the problem of how to improve the time measurement accuracy. After the time difference of the radiation source location (TDoA) data is obtained, the position of the radiation source can only be obtained by solving a set of non-linear location equations. The traditional method for solving the nonlinear equation system is based on iterative operation and linearization, so that the position of the radiation source needs to be initially estimated, and the accuracy of the solving method strongly depends on whether the initial position estimation is accurate or not. When the initial estimation is poor, a convergence solution cannot be obtained necessarily; meanwhile, the calculation amount of the estimation method is also large. In the positioning calculation process, because hyperbolic curve (plane) intersection sometimes generates positioning ambiguity, a method for removing the positioning ambiguity is also a problem to be solved in the time difference positioning processing. Meanwhile, when measurement noise is present, there will be a positioning error. The early passive positioning technology obtains the direction of a target radiation source by direction finding, and determines the position of a target by single-station accumulation or multi-station cooperation. The multi-station time difference location Technology (TDOA) is used for locating through target signal arrival time differences acquired by a plurality of observation stations, the time difference location has the greatest advantage of high location precision, but has the greatest weakness of time difference ambiguity, so that the TDOA has certain difficulty in dealing with high-repetition-frequency and high-maneuvering targets.
Aiming at an air maneuvering target, the multi-satellite communication time difference positioning data has the characteristics of large measurement error, difficulty in estimation of measurement noise and maneuvering noise, capability of only giving rough positioning error and the like. When traditional time difference positioning is carried out, positioning blurring often occurs, and positioning accuracy is affected. The traditional positioning data fusion method, such as the Karman filtering method, has poor filtering effect under the condition of uncertain measurement noise and maneuvering noise; the interactive multimode method can adapt to part of maneuvering noise, the filtering effect is improved compared with Kalman, but the filtering effect is still poor under the condition that the measurement noise and the maneuvering noise are not clear; the least squares do not utilize measurement noise and maneuver noise, so the filtering effect is worse than that of the Kalman and interactive multimode methods. Therefore, a fusion method of large-error positioning data only knowing the positioning error is urgently needed to be researched, and the covariance intersection algorithm and the k-nearest neighbor sliding window least square algorithm are utilized to perform fusion processing by means of the characteristic that the covariance intersection algorithm and the least square algorithm do not need covariance noise, so that the positioning accuracy is improved.
Disclosure of Invention
In order to solve the problem of accurate fusion of the large noise point traces, the invention provides the multi-satellite communication time difference positioning data fusion processing method which is high in time difference positioning speed and precision, has strong anti-interference fusion capability and robustness, can obviously improve the fusion precision and has large errors.
In order to achieve the above object, the present invention provides a method for fusion processing of multi-satellite communication time difference positioning data, which is characterized by comprising the following steps: after obtaining the multi-satellite communication radiation source time difference positioning data, adopting a mirror image point eliminating module to classify all positioning points according to target numbers, eliminating mirror image points in positioning point classification results by using a mirror image point eliminating algorithm, outputting non-mirror image points to a positioning point aggregation module, classifying all normal points according to the target numbers, performing normal point aggregation on normal point number classification results, performing point aggregation based on a point aggregation algorithm, fusing and estimating the positions of targets by using estimated positioning errors to generate an aggregation point, outputting the aggregation point to a outlier point eliminating module, performing outlier point elimination according to the outlier eliminating algorithm by using the outlier point eliminating module, outputting the non-outlier points to an aggregation point filtering module, filtering the aggregation point tracks by using the aggregation point filtering module based on the aggregation point filtering algorithm, and performing fusion processing by using a covariance intersection algorithm and a k nearest neighbor sliding window least square algorithm, and generating a final fusion result.
Compared with the prior art, the invention has the following beneficial effects:
according to the method, after the multi-satellite communication radiation source time difference positioning data is obtained, a mirror image point eliminating module is adopted for the aerial maneuvering target, all positioning points are classified according to target numbers, mirror image points in positioning point classification results are eliminated by using a mirror image point eliminating algorithm, mirror image points in the positioning points are eliminated, the mirror image point eliminating is accurate, real positioning points cannot be missed, at most, false positioning points are generated, and great influence is not generated on actual engineering application. Compared with a multi-platform positioning system, the number of platforms is reduced, and difficulty and cost of system implementation are reduced.
The method comprises the steps of classifying all normal points according to target numbers by adopting a positioning point aggregation module, performing normal point aggregation on classification results of the normal point numbers, performing point aggregation based on a point aggregation algorithm, fusing the positions of the estimated targets by utilizing estimated positioning errors to generate aggregation points, outputting the aggregation points to a outlier point removing module, and removing the outliers by the outlier point removing module according to the outlier removing algorithm, wherein the algorithm has strong anti-interference fusion capability under the condition of outlier interference and has strong robustness.
The non-outlier points are output to an aggregation point filtering module, an aggregation point trace is filtered based on an aggregation point filtering algorithm, and a covariance intersection algorithm and a k-nearest neighbor sliding window least square algorithm are used for fusion processing to generate a final fusion result. The method utilizes outlier rejection, covariance intersection and least square algorithm to carry out comprehensive filtering, is easy to realize in engineering, does not need to know maneuvering noise and measurement noise of a target, only utilizes estimated positioning error to improve fusion precision, and can solve the problem of point trace fusion under the condition that the noise is not completely known. Simulation results show that the positioning accuracy and stability of the algorithm are superior to those of the traditional three-station time difference positioning method and the Kalman filtering method, the uncertainty of information can be reduced, and the positioning effect of the system can be improved.
Drawings
For a more clear understanding of the present invention, the invention will be described by way of specific embodiments, while referring to the accompanying drawings, in which:
FIG. 1 is a schematic view of a process flow of the multi-satellite communication time difference positioning data fusion process of the present invention;
FIG. 2 is a schematic view of a process for removing mirror image points of the mirror image point removing module of FIG. 1;
FIG. 3 is a schematic diagram of a normal point aggregation process flow of the anchor point aggregation module shown in FIG. 1;
FIG. 4 is a schematic diagram of an aggregation point filtering process flow of the aggregation point filtering module of FIG. 1;
FIG. 5 is a schematic flow chart of k-neighbor filtering computation of the aggregation point filtering module of FIG. 4;
Detailed Description
See fig. 1. According to the invention, after obtaining the multi-satellite communication radiation source time difference positioning data, a mirror image point eliminating module is adopted to classify all positioning points according to target numbers, a mirror image point eliminating algorithm is used to eliminate mirror image points in the positioning point classification results, mirror image points in the positioning points are eliminated, non-mirror image points are output to a positioning point aggregation module, all normal points are classified according to target numbers, normal point aggregation is carried out on the normal point number classification results, point trace aggregation is carried out based on the point trace aggregation algorithm, the positions of the estimation targets are fused by using estimated positioning errors to generate aggregation points, the aggregation points are output to a outlier point eliminating module, the outlier point eliminating module carries out outlier point elimination according to the outlier eliminating algorithm, the non-outlier points are output to an aggregation point filtering module, the aggregation point filtering module carries out filtering on the aggregation point traces based on the aggregation point filtering algorithm, and performing fusion processing by using a covariance intersection algorithm and a k-nearest neighbor sliding window least square algorithm to generate a final fusion result.
See fig. 2. The mirror image point eliminating module firstly classifies positioning points in eliminating mirror image points: classifying all positioning points according to target numbers, classifying the same target numbers into the same class, arranging the same target numbers from small to large according to position time, storing the same target numbers into a positioning point chain table, classifying different target numbers into different classes, and processing the same class in subsequent processing; then, carrying out positioning point association, judging whether the association is successful, if the received positioning point is the first point, distributing two new target numbers to the two coordinate position points, and respectively storing the two new target numbers into different positioning point linked lists according to the newly distributed target numbers; otherwise, respectively carrying out association judgment on the two coordinate position points by using an association judgment algorithm, if the association of the coordinate position points is successful, maintaining the historical target, and otherwise, starting the batch of the new target.
In the process of starting a new target, a mirror image point removing module allocates a new target number to a coordinate position point by taking a positioning point as space-time positioning information of the target, and stores the coordinate position point into a positioning point linked list appointed by the target number, wherein the space-time positioning information comprises two coordinate position points, one is a normal point, the other is a mirror image point, the specific contents of the positioning point mainly comprise a target number O, a positioning error e, a position time t and a WGS-84 coordinate position 1: longitude l1, latitude b1, altitude h1, WGS-84 coordinate position 2: longitude l2, latitude b2, and altitude h2, where e is in meters, t is in seconds, l1, b1, l2, b2 are in degrees, and h1 and h2 are in meters.
The mirror image point eliminating module adopts an association judgment algorithm of coordinate position points to traverse all the positioning point linked lists to sequentially carry out position time judgment and distance judgment; judging the position time, namely calculating the position time difference between the coordinate position point and the last point in the positioning point linked list, if the position time difference is greater than a specified association time threshold, failing to associate, and otherwise, judging the distance; and (4) distance judgment, namely calculating the distance between the coordinate position point and the last point in the positioning point linked list, wherein if the distance is greater than a specified association distance threshold, association fails, and if not, association succeeds.
And the mirror image point eliminating module acquires a locating point linked list successfully associated in the historical target maintenance, stores the coordinate position point 1 into the locating point linked list in the order from small to large according to the time, judges the mirror image point, traverses all the locating point linked lists, if the length of the linked list is less than the specified mirror image point threshold, all the locating points in the linked list are mirror image points, otherwise, the locating points are non-mirror image points, and outputs all the locating points in the linked list to the normal point aggregation module.
See fig. 3. In the normal point aggregation processing of the positioning point aggregation module, normal point numbers are classified firstly, all normal points are classified according to target numbers, the same target numbers are classified into the same class, are arranged from small to large according to position time and are stored in a normal point chain table, different target numbers are classified into different classes, and the subsequent processing is carried out aiming at the same class; then in normal point time classification: traversing all coordinate position points in the normal point linked list, if the point is the first point, saving the point as the first point of aggregation into the aggregation point linked list, otherwise, calculating the position time difference between the point and the first point in the aggregation point linked list, if the position time difference is less than a specified aggregation time threshold, adding the point into the aggregation point linked list, otherwise, carrying out position time aggregation, positioning error aggregation, longitude aggregation, latitude aggregation and altitude aggregation on the point in the aggregation point linked list, emptying the aggregation point linked list after aggregation is finished, and saving the point as the first point of aggregation into the aggregation point linked list.
The anchor point aggregation module in the location time aggregation: if only one point exists in the aggregation point linked list, the point position time is the aggregation point position time, and aggregation is completed; otherwise, performing time average calculation on all the position time t in the aggregation point linked list, and taking the calculated position time average as the aggregation point position time; the position-time average calculation formula is as follows:
wherein m is the number of aggregation points, tiIs the position time of the normal point participating in the aggregation, and t is the aggregation point position time.
The positioning point aggregation module is used for aggregating the positioning errors: if only one point exists in the aggregation point linked list, the point positioning error is an aggregation point positioning error, and aggregation is completed; otherwise, performing weighted fusion on all positioning errors in the aggregation point linked list, and taking the calculated weighted fusion value of the positioning errors as the aggregation point positioning errors; and calculating a positioning error weighted fusion value by adopting the following calculation formula:
wherein m is the number of aggregation points, eiThe positioning error of the normal point participating in the aggregation is shown as e, and the positioning error of the aggregation point is shown as e.
The anchor point aggregation module in longitude aggregation: if only one point exists in the aggregation point linked list, the point longitude is the aggregation point longitude, and aggregation is completed; if the aggregation point linked list has at least 5 points, removing the longitude maximum value and the longitude minimum value, and carrying out longitude weighted fusion on the longitudes of the other points; otherwise, performing weighted fusion on all longitudes in the aggregation point linked list; taking the calculated longitude weighted fusion value as an aggregation point longitude l of an aggregation point; the aggregation point longitude is calculated using the calculation formula shown below:
wherein n is the number of longitudes participating in weighted fusion, eiFor longitude positioning errors participating in weighted fusion, liIs the longitude involved in the weighted fusion.
The anchor point aggregation module in latitude aggregation: if only one point exists in the aggregation point linked list, the point latitude is the aggregation point latitude, and aggregation is completed; if the aggregated point linked list has at least 5 points, then the latitude maximum value and the latitude minimum value are removed, and the latitudes of the other points are subjected to latitude weighted fusion; otherwise, performing weighted fusion on all latitudes in the aggregation point linked list; taking the calculated latitude weighted fusion value as the latitude b of the aggregation point; calculating the latitude of the aggregation point by adopting the following calculation formula:
wherein n is the number of latitudes participating in weighted fusion, eiTo participate in weighted fusion of latitude positioning errors, biIs the latitude participating in the weighted fusion.
The anchor point aggregation module is used for in high aggregation: if only one point exists in the aggregation point linked list, the height of the point is the height of the aggregation point, and aggregation is completed; if the aggregation point linked list has at least 5 points, the height maximum value and the height minimum value are removed, height weighted fusion is carried out on the heights of the rest points, and otherwise, weighted fusion is carried out on all the heights in the aggregation point linked list; taking the calculated height weighted fusion value as the height h of the aggregation point; the height of the aggregation point is calculated using the calculation formula shown below:
wherein n is the number of heights participating in the weighted fusion, eiHeight positioning error for participation in weighted fusion, hiIs the height involved in the weighted fusion.
The outlier point removing module removes outliers according to an outlier removing algorithm, outputs non-outliers to the aggregation point filtering module, does not remove the first point by adopting the outlier point removing algorithm, and calculates the distance r between the (i-1) th point and the (i-2) th point in a traversing way from the ith point (i is more than or equal to 3)1Calculating the distance r between the ith point and the (i-1) th point2Distance r between the ith point and the (i-2) th point3If r is3<r1+r2And if the ith-1 point is the outlier, removing the ith-1 point, and traversing and calculating from the ith point again after removing.
See fig. 4. The aggregation point filtering module firstly classifies the aggregation point numbers: classifying all aggregation points according to target numbers, classifying the same target numbers into the same class, arranging the same target numbers from small to large according to position time, storing the same target numbers into an aggregation point chain table, classifying different target numbers into different classes, and processing the same class in subsequent processing; then classifying the aggregation point time; and finally, respectively filtering longitude and latitude.
And filtering the aggregation point trace according to an aggregation point filtering algorithm, wherein k-nearest neighbor sliding window filtering is mainly adopted, namely longitude and latitude are respectively filtered by using the nearest k points to generate a final fusion result, and k is generally 5.
The aggregation point filtering module is in aggregation point time classification: traversing all coordinate position points in the aggregation point linked list, calculating the position time difference between each point P2 in the filtering point linked list and the point P1, deleting the point P2 from the filtering point linked list if the position time difference is greater than a specified filtering time threshold, adding the point P1 into the filtering point linked list, carrying out longitude filtering and latitude filtering on the points in the filtering point linked list, and assigning the longitude and the latitude of the P1 as the longitude and the latitude after filtering.
The aggregation point filtering module, in longitude filtering: if the point number in the aggregation point linked list is less than 3, the longitude of the last point 1 is taken as the longitude of a filtering point, and filtering is finished; otherwise, k nearest neighbor filtering is carried out on all points in the filtering point linked list; taking the calculated longitude filter value as the longitude of the filter point; the calculation process of the longitude filter is shown in fig. 5.
See fig. 5. In the calculation process of longitude filtering, the aggregation point filtering module firstly performs space-time position normalization, translates position time to the position where 0 starts, translates longitude to the position where 0 starts, and translates by adopting the following formula:
wherein t isiAnd liThe position time t of the ith point for participating in the filtering calculationiAnd ith Point longitude li；
Then, the normalized position time and longitude are subjected to filter parameter calculation to calculate filter parameters (a)0，a1) The following formula is adopted to calculate the filtering parameters:
and is
Finally, the position time and the longitude are subjected to filtering estimation value calculation to obtain a longitude filtering estimation value
tkTo the last pointNormalized position time, tiAnd liThe normalized position time and longitude of the ith point are shown, and k is the point number participating in filtering calculation;
the aggregation point filtering module is used for latitude filtering: if the number of points in the aggregation point linked list is less than 3, taking the latitude of the last 1 point as the latitude of the filtering point, and finishing filtering; otherwise, k nearest neighbor filtering is carried out on all points in the filtering point linked list; taking the calculated latitude filtering value as the latitude of the filtering point; the calculation process of latitude filtering is the same as that of longitude filtering.
Claims (10)
1. A multi-satellite communication time difference positioning data fusion processing method is characterized by comprising the following steps: after obtaining the multi-satellite communication radiation source time difference positioning data, adopting a mirror image point eliminating module to classify all positioning points according to target numbers, eliminating mirror image points in positioning point classification results by using a mirror image point eliminating algorithm, eliminating mirror image points in the positioning points, outputting non-mirror image points to a positioning point aggregation module, classifying all normal points according to the target numbers, performing normal point aggregation on the normal point number classification results, performing point trace aggregation based on a point trace aggregation algorithm, fusing the positions of estimated targets by using estimated positioning errors to generate a polymerization point, outputting the polymerization point to a outlier point eliminating module, performing outlier point elimination by using the outlier point eliminating module according to the outlier eliminating algorithm, outputting the non-outlier point to a polymerization point filtering module, filtering the polymerization point trace by using the polymerization point filtering algorithm, and performing fusion processing by using a covariance intersection algorithm and a k-nearest neighbor sliding window least square algorithm, generating the final blending result.
2. The method for fusion processing of multi-satellite communication time difference positioning data according to claim 1, characterized in that: the mirror image point eliminating module firstly classifies positioning points in eliminating mirror image points: classifying all positioning points according to target numbers, classifying the same target numbers into the same class, arranging the same target numbers from small to large according to position time, storing the same target numbers into a positioning point chain table, classifying different target numbers into different classes, and processing the same class in subsequent processing; then, carrying out positioning point association, judging whether the association is successful, if the received positioning point is the first point, distributing two new target numbers to the two coordinate position points, and respectively storing the two new target numbers into different positioning point linked lists according to the newly distributed target numbers; otherwise, respectively carrying out association judgment on the two coordinate position points by using an association judgment algorithm, if the association of the coordinate position points is successful, maintaining the historical target, and otherwise, starting the batch of the new target.
3. The method for fusion processing of multi-satellite communication time difference positioning data according to claim 1, characterized in that: in the process of starting a new target, a mirror image point removing module allocates a new target number to a coordinate position point by taking a positioning point as space-time positioning information of the target, and stores the coordinate position point into a positioning point linked list appointed by the target number, wherein the space-time positioning information comprises two coordinate position points, one is a normal point, the other is a mirror image point, the specific contents of the positioning point mainly comprise a target number O, a positioning error e, a position time t and a WGS-84 coordinate position 1: longitude l1, latitude b1, altitude h1, WGS-84 coordinate position 2: longitude l2, latitude b2, and altitude h2, where e is in meters, t is in seconds, l1, b1, l2, b2 are in degrees, and h1 and h2 are in meters.
4. The method for fusion processing of multi-satellite communication time difference positioning data according to claim 1, characterized in that: the mirror image point eliminating module adopts an association judgment algorithm of coordinate position points, traverses all the positioning point linked lists to sequentially carry out position time judgment and distance judgment, carries out position time judgment, calculates the position time difference between the coordinate position point and the last point in the positioning point linked lists, if the position time difference is greater than a specified association time threshold, association fails, otherwise carries out distance judgment; and (4) distance judgment, namely calculating the distance between the coordinate position point and the last point in the positioning point linked list, wherein if the distance is greater than a specified association distance threshold, association fails, and if not, association succeeds.
5. The method for fusion processing of multi-satellite communication time difference positioning data according to claim 1, characterized in that: and the mirror image point eliminating module acquires a locating point linked list successfully associated in the historical target maintenance, stores the coordinate position point 1 into the locating point linked list in the order from small to large according to the time, judges the mirror image point, traverses all the locating point linked lists, if the length of the linked list is less than the specified mirror image point threshold, all the locating points in the linked list are mirror image points, otherwise, the locating points are non-mirror image points, and outputs all the locating points in the linked list to the normal point aggregation module.
6. The method for fusion processing of multi-satellite communication time difference positioning data according to claim 1, characterized in that: in the normal point aggregation processing of the positioning point aggregation module, normal point numbers are classified firstly, all normal points are classified according to target numbers, the same target numbers are classified into the same class, are arranged from small to large according to position time and are stored in a normal point chain table, different target numbers are classified into different classes, and the subsequent processing is carried out aiming at the same class; then in normal point time classification: traversing all coordinate position points in the normal point linked list, if the point is the first point, saving the point as the first point of aggregation into the aggregation point linked list, otherwise, calculating the position time difference between the point and the first point in the aggregation point linked list, if the position time difference is less than a specified aggregation time threshold, adding the point into the aggregation point linked list, otherwise, carrying out position time aggregation, positioning error aggregation, longitude aggregation, latitude aggregation and altitude aggregation on the point in the aggregation point linked list, emptying the aggregation point linked list after the aggregation is finished, and saving the point as the first point of aggregation into the aggregation point linked list.
7. The method for fusion processing of multi-satellite communication time difference positioning data according to claim 1, characterized in that: the anchor point aggregation module in the location time aggregation: if only one point exists in the aggregation point linked list, the point position time is the aggregation point position time, and aggregation is completed; otherwise, performing time average calculation on all the position time t in the aggregation point linked list, and taking the calculated position time average as the aggregation point position time; the position-time average calculation formula is as follows:
wherein m is the number of aggregation points, tiIs the position time of the normal point participating in the aggregation, and t is the aggregation point position time.
8. The method for fusion processing of multi-satellite communication time difference positioning data according to claim 1, characterized in that: the anchor point aggregation module in longitude aggregation: if only one point exists in the aggregation point linked list, the point longitude is the aggregation point longitude, and aggregation is completed; if the aggregation point linked list has at least 5 points, removing the longitude maximum value and the longitude minimum value, and carrying out longitude weighted fusion on the longitudes of the other points; otherwise, performing weighted fusion on all longitudes in the aggregation point linked list; taking the calculated longitude weighted fusion value as an aggregation point longitude l of an aggregation point; the aggregation point longitude is calculated using the calculation formula shown below:
wherein n is the number of longitudes participating in weighted fusion, eiFor longitude positioning errors participating in weighted fusion, liIs the longitude involved in the weighted fusion.
9. The method for fusion processing of multi-satellite communication time difference positioning data according to claim 1, characterized in that: the anchor point aggregation module in longitude aggregation: if only one point exists in the aggregation point linked list, the point longitude is the aggregation point longitude, and aggregation is completed; if the aggregation point linked list has at least 5 points, removing the longitude maximum value and the longitude minimum value, and carrying out longitude weighted fusion on the longitudes of the other points; otherwise, performing weighted fusion on all longitudes in the aggregation point linked list; taking the calculated longitude weighted fusion value as an aggregation point longitude l of an aggregation point; the aggregation point longitude is calculated using the calculation formula shown below:
wherein n is the number of longitudes participating in weighted fusion, eiFor longitude positioning errors participating in weighted fusion, liIs the longitude involved in the weighted fusion.
10. The method for fusion processing of multi-satellite communication time difference positioning data according to claim 1, characterized in that: the anchor point aggregation module in latitude aggregation: if only one point exists in the aggregation point linked list, the point latitude is the aggregation point latitude, and aggregation is completed; if the aggregated point linked list has at least 5 points, then the latitude maximum value and the latitude minimum value are removed, and the latitudes of the other points are subjected to latitude weighted fusion; otherwise, performing weighted fusion on all latitudes in the aggregation point linked list; taking the calculated latitude weighted fusion value as the latitude b of the aggregation point; calculating the latitude of the aggregation point by adopting the following calculation formula:
wherein n is the number of latitudes participating in weighted fusion, eiTo participate in weighted fusion of latitude positioning errors, biIs the latitude participating in the weighted fusion.
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