CN113253308B - Method and system for determining positioning performance of unmanned aerial vehicle satellite navigation terminal - Google Patents
Method and system for determining positioning performance of unmanned aerial vehicle satellite navigation terminal Download PDFInfo
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- CN113253308B CN113253308B CN202110514723.3A CN202110514723A CN113253308B CN 113253308 B CN113253308 B CN 113253308B CN 202110514723 A CN202110514723 A CN 202110514723A CN 113253308 B CN113253308 B CN 113253308B
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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
- G01S19/01—Satellite radio beacon positioning systems transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
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Abstract
The invention discloses a method and a system for determining positioning performance of an unmanned aerial vehicle satellite navigation terminal. The method comprises the following steps: acquiring characteristic parameters in the current state; the characteristic parameters comprise the frequency of the interference signal, the bandwidth of the interference signal and the initial carrier-to-noise ratio of each satellite in the satellite navigation terminal; the interference signal is a single-source electromagnetic interference signal or a multi-source electromagnetic interference signal; inputting the characteristic parameters into an electromagnetic sensitivity threshold prediction model to obtain tracking lock losing thresholds of each satellite in the current state; obtaining the electromagnetic interference allowance of each satellite based on the tracking lock losing threshold and the power of the interference signal; and determining the positioning performance of the satellite navigation terminal based on the electromagnetic interference allowance. The invention realizes the real-time determination of the positioning performance of the satellite navigation terminal of the unmanned aerial vehicle under the condition of considering electromagnetic interference.
Description
Technical Field
The invention relates to the field of positioning performance testing, in particular to a method and a system for determining positioning performance of an unmanned aerial vehicle satellite navigation terminal.
Background
At present, most of positioning performance determination methods related to satellite navigation terminals adopt a transverse contrast evaluation method based on certain fixed parameters, for example, a carrier-to-noise ratio of visible satellites, pseudo-range observation errors and geometric precision factors of all visible satellites are used as an evaluation index system, and a virtual positive and negative ideal evaluation method is provided based on an information entropy theory algorithm. And unmanned aerial vehicle can receive the electromagnetic interference of different grade type at the flight in-process, the electromagnetic interference of different grade type influences the positioning performance at navigation terminal differently, and along with unmanned aerial vehicle's flight, space electromagnetic interference constantly changes, also constantly changes to unmanned aerial vehicle satellite navigation terminal's influence, consequently, how to confirm unmanned aerial vehicle satellite navigation terminal positioning performance in real time under the circumstances of considering electromagnetic interference is the present problem that awaits a urgent solution.
Disclosure of Invention
Therefore, a method and a system for determining the positioning performance of the satellite navigation terminal of the unmanned aerial vehicle are needed to be provided, so that the positioning performance of the satellite navigation terminal of the unmanned aerial vehicle can be determined in real time under the condition of considering electromagnetic interference.
In order to achieve the purpose, the invention provides the following scheme:
a method for determining positioning performance of an unmanned aerial vehicle satellite navigation terminal comprises the following steps:
acquiring characteristic parameters in the current state; the characteristic parameters comprise the frequency of the interference signal, the bandwidth of the interference signal and the initial carrier-to-noise ratio of each satellite in the satellite navigation terminal; the interference signal is a single-source electromagnetic interference signal or a multi-source electromagnetic interference signal;
inputting the characteristic parameters into an electromagnetic sensitivity threshold prediction model to obtain tracking lock losing thresholds of the satellites in the current state;
obtaining an electromagnetic interference margin of each satellite based on the tracking loss-of-lock threshold and the power of the interference signal;
determining the positioning performance of the satellite navigation terminal based on the electromagnetic interference allowance; the positioning performance comprises a first-level performance and a second-level performance; the first-level performance is the performance when the satellite loss begins to occur in the satellite navigation terminal; the second-level performance is the performance when the total number of effective satellites in the satellite navigation terminal is less than the set minimum positioning satellite number.
Optionally, the method for determining the electromagnetic sensitivity threshold prediction model includes:
adopting a navigation terminal electromagnetic interference effect test, and constructing an electromagnetic sensitivity information base by changing the type of an interference signal applied by a test satellite and changing characteristic parameters under different interference types; the electromagnetic sensitivity information base comprises characteristic parameters applied to the test satellite under various interference types and corresponding tracking unlocking threshold values;
and obtaining the electromagnetic sensitivity threshold prediction model by adopting a machine learning method based on the electromagnetic sensitivity information base.
Optionally, the obtaining the electromagnetic sensitivity threshold prediction model by using a machine learning method based on the electromagnetic sensitivity information base specifically includes:
dividing the electromagnetic sensitivity information base into a training sample and a test sample;
respectively inputting the training samples into an XGboost model, a GPR model and an SVR model for training to obtain a trained XGboost model, a trained GPR model and a trained SVR model;
and verifying the trained XGboost model, the trained GPR model and the trained SVR model respectively by using the test samples, and determining the model with the minimum training error as the electromagnetic sensitivity threshold prediction model.
Optionally, the obtaining the electromagnetic interference margin of each satellite based on the tracking out-of-lock threshold and the power of the interference signal specifically includes:
when the interference signal is a single-source electromagnetic interference signal, the calculation formula of the electromagnetic interference margin is
Δi＝Pi-Pj；
Wherein, DeltaiIs the electromagnetic interference margin, P, of the ith satelliteiIs the tracking out-of-lock threshold, P, for the ith satellitejIs the power of the single source electromagnetic interference signal.
Optionally, the obtaining the electromagnetic interference margin of each satellite based on the tracking out-of-lock threshold and the power of the interference signal specifically includes:
when the interference signal is a dual-source electromagnetic interference signal, the calculation formula of the electromagnetic interference margin is
Δi＝1-Si，
Wherein, DeltaiIs the electromagnetic interference margin of the ith satellite, SiIs the suppression factor, P, of the ith satellitei1Is the tracking out-of-lock threshold value, P, of the ith satellite under the action of the 1 st interference signali2Is the tracking out-of-lock threshold value, P, of the ith satellite under the action of the 2 nd interference signalj1Is the power of the 1 st interfering signal, Pj2Is the power of the 2 nd interfering signal.
Optionally, the determining the positioning performance of the satellite navigation terminal based on the electromagnetic interference margin specifically includes:
sequencing the electromagnetic interference margins of all satellites from large to small to obtain an interference margin sequence;
judging whether the Mth electromagnetic interference allowance in the interference allowance sequence is zero or not to obtain a first judgment result; wherein M is the total number of effective satellites in the satellite navigation terminal;
if the first judgment result is yes, determining that the satellite loss starts to occur in the satellite navigation terminal, and the satellite navigation terminal is in the first-level performance;
judging whether the Nth electromagnetic interference allowance in the interference allowance sequence is zero or not to obtain a second judgment result; wherein N is the set lowest positioning satellite number;
if the second judgment result is yes, determining that the total number of the effective satellites in the satellite navigation terminal is less than the set minimum positioning satellite number, and the satellite navigation terminal is in the second-level performance.
Optionally, the single-source electromagnetic interference signal is a continuous wave interference signal, a narrowband interference signal, or a broadband interference signal; the multi-source electromagnetic interference signal includes at least two of a continuous wave interference signal, a narrowband interference signal, and a wideband interference signal.
An unmanned aerial vehicle satellite navigation terminal positioning performance determination system, comprising:
the data acquisition module is used for acquiring characteristic parameters in the current state; the characteristic parameters comprise the frequency of the interference signal, the bandwidth of the interference signal and the initial carrier-to-noise ratio of each satellite in the satellite navigation terminal; the interference signal is a single-source electromagnetic interference signal or a multi-source electromagnetic interference signal;
the threshold value determining module is used for inputting the characteristic parameters into an electromagnetic sensitivity threshold value prediction model to obtain tracking lock losing threshold values of the satellites in the current state;
an electromagnetic interference margin determining module, configured to obtain an electromagnetic interference margin of each satellite based on the tracking out-of-lock threshold and the power of the interference signal;
a performance determination module, configured to determine positioning performance of the satellite navigation terminal based on the electromagnetic interference margin; the positioning performance comprises a first-level performance and a second-level performance; the first-level performance is the performance when the satellite loss begins to occur in the satellite navigation terminal; the second-level performance is the performance when the total number of effective satellites in the satellite navigation terminal is less than the set minimum positioning satellite number.
Optionally, the system for determining the positioning performance of the unmanned aerial vehicle satellite navigation terminal further includes: a prediction model determination module for determining the electromagnetic sensitivity threshold prediction model; the prediction model determining module specifically includes:
the information base construction unit is used for constructing an electromagnetic sensitivity information base by changing the type of an interference signal applied by a test satellite and changing characteristic parameters under different interference types by adopting a navigation terminal electromagnetic interference effect test; the electromagnetic sensitivity information base comprises characteristic parameters applied to the test satellite under various interference types and corresponding tracking unlocking threshold values;
and the model construction unit is used for obtaining the electromagnetic sensitivity threshold prediction model by adopting a machine learning method based on the electromagnetic sensitivity information base.
Optionally, the model building unit specifically includes:
the sample dividing subunit is used for dividing the electromagnetic sensitivity information base into a training sample and a test sample;
the training subunit is used for inputting the training samples into the XGboost model, the GPR model and the SVR model respectively for training to obtain a trained XGboost model, a trained GPR model and a trained SVR model;
and the verification subunit is used for respectively verifying the trained XGboost model, the trained GPR model and the trained SVR model by adopting the test samples, and determining the model with the minimum training error as the electromagnetic sensitivity threshold prediction model.
Compared with the prior art, the invention has the beneficial effects that:
the invention provides a method and a system for determining positioning performance of an unmanned aerial vehicle satellite navigation terminal, which are used for acquiring characteristic parameters in a current state in real time; the characteristic parameters comprise the frequency of the interference signal, the bandwidth of the interference signal and the initial carrier-to-noise ratio of each satellite in the satellite navigation terminal; the interference signal is a single-source electromagnetic interference signal or a multi-source electromagnetic interference signal; inputting the characteristic parameters into an electromagnetic sensitivity threshold prediction model to obtain tracking lock losing thresholds of each satellite in the current state; obtaining the electromagnetic interference allowance of each satellite based on the tracking lock losing threshold and the power of the interference signal; the positioning performance of the satellite navigation terminal is determined based on the electromagnetic interference allowance, so that the positioning performance of the unmanned aerial vehicle satellite navigation terminal is determined in real time under the condition of considering the electromagnetic interference.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without inventive exercise.
Fig. 1 is a flowchart of a method for determining positioning performance of an unmanned aerial vehicle satellite navigation terminal according to an embodiment of the present invention;
FIG. 2 is a diagram of the XGboost model training results, the GPR model training results, and the SVR model training results; wherein, fig. 2(a) is a schematic diagram of the XGBoost model training result, fig. 2(b) is a schematic diagram of the GPR model training result, and fig. 2(c) is a schematic diagram of the SVR model training result;
FIG. 3 is a schematic diagram of the prediction performance of GPR and XGboost models; wherein, fig. 3(a) is a schematic diagram of XGBoost model prediction performance, and fig. 3(b) is a schematic diagram of GPR model prediction performance;
FIG. 4 is a schematic diagram of an electromagnetic interference level curve of a satellite navigation terminal and a navigation terminal positioning performance level under different types of single-source electromagnetic interference signals; fig. 4(a) is an electromagnetic interference level curve of a satellite navigation terminal and a navigation terminal positioning performance level schematic diagram under a single-frequency interference signal, fig. 4(b) is an electromagnetic interference level curve of a satellite navigation terminal and a navigation terminal positioning performance level schematic diagram under a narrow-band interference signal of 0.4MHz, and fig. 4(c) is an electromagnetic interference level curve of a satellite navigation terminal and a navigation terminal positioning performance level schematic diagram under a wide-band interference signal of 2 MHz;
fig. 5 is a structural diagram of a positioning performance determining system for an unmanned aerial vehicle satellite navigation terminal according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in further detail below.
In the flight process of the unmanned aerial vehicle, the satellite signal quality received by the navigation receiver of the unmanned aerial vehicle is subjected to the ground satellite under the condition of no external obvious electromagnetic interferenceThe influence of factors such as signal power, multipath interference and the like is reflected to a visual result, namely, the satellite signal carrier-to-noise ratio, so that under the condition of no external electromagnetic interference, the signal carrier-to-noise ratio represents the capability of a receiver to actually track a navigation signal under the combined action of all environmental factors, and particularly when an unmanned aerial vehicle flies in the air, the satellite signal carrier-to-noise ratio received by the receiver is stable due to the weakened effect of the multipath interference, so that the power of the navigation signal can be represented by the satellite signal carrier-to-noise ratio (namely, the initial carrier-to-noise ratio) without the electromagnetic interference. Such a carrier-to-noise ratio C/N exists in the tracking loop inside the receiver0Threshold value: where the signal is less than C/N0With this threshold, the loop will lose the ability to stably track the weak signal, so C/N can be used0The threshold value is used as the criterion of satellite tracking loss of the receiver, wherein C is carrier power, N0Is the noise power.
The following results are obtained through the previous electromagnetic interference effect experiments: the factors causing the tracking loop lock loss of the navigation receiver comprise the initial carrier-to-noise ratio of the navigation signal, the frequency of an interference signal and the bandwidth of the interference signal, and because the relationship between the three influencing factors and the loop tracking lock loss threshold is nonlinear, the nonlinear prediction modeling is difficult to be carried out by adopting the traditional deterministic analysis method, and the threshold of the loop tracking lock loss under all single-source electromagnetic interference is measured by tests, the workload is huge, the realization is difficult, so the development of the lock loss threshold prediction based on the machine learning method is a feasible method.
In the embodiment, through a navigation terminal electromagnetic interference test in advance, electromagnetic sensitivity threshold values of different satellites in the navigation terminal at different initial carrier-to-noise ratios are obtained, so that a training sample is constructed; through an intelligent learning training method, an electromagnetic sensitivity threshold prediction model of different satellites in the navigation terminal is constructed, then the sensitivity threshold of the satellites is predicted according to the frequency of interference signals, the bandwidth of the interference signals and the initial carrier-to-noise ratio of the satellite signals, and the real-time positioning performance of the navigation terminal is determined by judging the tracking states of the different satellites. The method for determining the positioning performance of the satellite navigation terminal of the unmanned aerial vehicle provided by the embodiment is explained in detail below.
Fig. 1 is a flowchart of a method for determining positioning performance of an unmanned aerial vehicle satellite navigation terminal according to an embodiment of the present invention. Referring to fig. 1, the method for determining the positioning performance of the satellite navigation terminal of the unmanned aerial vehicle according to the embodiment includes:
step 101: acquiring characteristic parameters in the current state; the characteristic parameters comprise the frequency of the interference signal, the bandwidth of the interference signal and the initial carrier-to-noise ratio of each satellite in the satellite navigation terminal; the interference signal is a single-source electromagnetic interference signal or a multi-source electromagnetic interference signal.
The single-source electromagnetic interference signal is a continuous wave interference signal, a narrow-band interference signal or a broadband interference signal; the multi-source electromagnetic interference signal includes at least two of a continuous wave interference signal, a narrowband interference signal, and a wideband interference signal.
Step 102: and inputting the characteristic parameters into an electromagnetic sensitivity threshold prediction model to obtain a tracking lock-losing threshold of each satellite in the current state.
The method for determining the electromagnetic sensitivity threshold prediction model comprises the following steps:
1) adopting a navigation terminal electromagnetic interference effect test, and constructing an electromagnetic sensitivity information base by changing the type of an interference signal applied by a test satellite and changing characteristic parameters under different interference types; the electromagnetic sensitivity information base comprises characteristic parameters applied to the test satellite under various interference types and corresponding tracking unlocking threshold values.
2) And obtaining the electromagnetic sensitivity threshold prediction model by adopting a machine learning method based on the electromagnetic sensitivity information base. The method specifically comprises the following steps:
and dividing the electromagnetic sensitivity information base into a training sample and a test sample.
And respectively inputting the training samples into an XGboost model, a GPR model and an SVR model for training to obtain a trained XGboost model, a trained GPR model and a trained SVR model.
And verifying the trained XGboost model, the trained GPR model and the trained SVR model respectively by using the test samples, and determining the model with the minimum training error as the electromagnetic sensitivity threshold prediction model.
In practical application, the electromagnetic sensitivity threshold prediction model can be determined in the following specific manner:
firstly, a navigation terminal electromagnetic interference effect test needs to be carried out, a training sample is constructed, as the processing flows of all tracking loops in a receiver of the navigation terminal are basically similar, a No. 8 satellite is selected as a test object, namely, the No. 8 satellite is a test satellite, and the type of the interference signal (continuous wave interference signal, narrow-band interference signal and wide-band interference signal) is changed in the test process, and the values of three variables of the frequency of the interference signal, the bandwidth of the interference signal and the initial carrier-to-noise ratio of each satellite in the satellite navigation terminal under each interference type are changed, wherein the narrowband interference signal and the broadband interference signal are formed by noise modulation, the finally obtained test sample amount is 750 groups, 700 groups of test data are used as training samples for model training, and the other 50 groups of data are used as test samples for testing the prediction accuracy of the training model.
During the model training process, the XGBoost model training results are compared with the training results of the GPR model and the SVR model, as shown in fig. 2. As can be seen from fig. 2, for the training error of the tracking loop out-of-lock threshold, the training accuracy of the XGBoost model is higher than that of the GPR model and the SVR model, wherein although the SVR model can optimize the training result by adjusting the internal parameters, the training error under the optimal parameters is still large, exceeds the 3dB tolerance specified by the military standard, and does not meet the actual requirement, and the training errors of the GPR model and the XGBoost model are within the specified tolerance range.
In order to verify the effect of the training model in prediction, the test samples are used to compare the prediction performance of the GPR model and the XGBoost model, and the result is shown in fig. 3. As can be seen from FIG. 3, the prediction error of the GPR model is large, and the maximum position exceeds 15 dB; the prediction error of the XGboost model is smaller, the XGboost model is superior to the prediction result of the GPR model, and the 3dB tolerance specified by the national military standard is met. Therefore, the trained XGBoost model may be determined as an electromagnetic susceptibility threshold prediction model.
Step 103: and obtaining the electromagnetic interference margin of each satellite based on the tracking loss-of-lock threshold and the power of the interference signal.
When the interference signal is a single-source electromagnetic interference signal, evaluating the electromagnetic interference situation of the unmanned aerial vehicle satellite navigation terminal under the single-source electromagnetic interference signal, specifically:
(1) in the flight process of the unmanned aerial vehicle, the carrier-to-noise ratio of satellite signals in a navigation terminal (navigation receiver) changes little in a short time, and when the carrier-to-noise ratio of the satellite in the navigation terminal begins to generally decline and an interference signal monitored by an unmanned aerial vehicle environment sensing platform appears at the moment, the carrier-to-noise ratio of the satellite signals at the last moment is recorded as an initial carrier-to-noise ratio.
(2) According to the initial carrier-to-noise ratio of each satellite and the characteristics (the frequency of the interference signal and the bandwidth of the interference signal) of the interference signal monitored by the environment sensing platform, a tracking lock losing threshold value of each satellite signal in the current state can be obtained through an electromagnetic sensitivity threshold value prediction model and is recorded as Pi(where i is the serial number of the satellite signal).
(3) Interference signal power P measured by environment perception platformjWill interfere with the signal power PjOut-of-lock threshold P with each satelliteiMaking a difference to obtain the electromagnetic interference allowance delta of each satelliteiThe calculation formula of the electromagnetic interference margin is
Δi＝Pi-Pj；
Wherein, DeltaiIs the electromagnetic interference margin, P, of the ith satelliteiIs the tracking out-of-lock threshold, P, for the ith satellitejIs the power of the single source electromagnetic interference signal.
As the unmanned aerial vehicle inevitably encounters the condition of multi-source electromagnetic interference in the flying process, the situation evaluation method of the satellite navigation system under the multi-source electromagnetic interference is very significant. Taking in-band dual-source electromagnetic interference as an example, the situation evaluation method of the navigation terminal is as follows:
(1) in the flight process of the unmanned aerial vehicle, the carrier-to-noise ratio of the satellite signal in the navigation terminal changes little in a short time, when the carrier-to-noise ratio of the satellite in the navigation terminal begins to generally decline and the unmanned aerial vehicle environment perception platform monitors the occurrence of an interference signal, the carrier-to-noise ratio of the satellite signal at the last moment is recorded as an initial carrier-to-noise ratio.
(2) According to the initial carrier-to-noise ratio of each satellite and the characteristics (the frequency of an interference signal and the bandwidth of the interference signal) of the dual-source electromagnetic interference signal monitored by the environment sensing platform, tracking out-of-lock threshold values of each satellite signal under the action of two interference signals can be obtained through an electromagnetic sensitivity threshold value prediction model and are respectively recorded as Pi1And Pi2Where i is the serial number of the satellite signal and 1 and 2 are the labels of the dual source electromagnetic interference signal.
(3) The power of the double-source electromagnetic interference signal is measured by using an environment sensing platform and is respectively Pj1And Pj2First, the compression coefficient is calculated
And then calculating the electromagnetic interference allowance of each satellite based on the suppression coefficient of each satellite, wherein specifically, when the interference signal is a dual-source electromagnetic interference signal, the calculation formula of the electromagnetic interference allowance is
Δi＝1-Si，
Wherein, DeltaiIs the electromagnetic interference margin of the ith satellite, SiIs the suppression factor, P, of the ith satellitei1Is the tracking out-of-lock threshold value, P, of the ith satellite under the action of the 1 st interference signali2Is the tracking out-of-lock threshold value, P, of the ith satellite under the action of the 2 nd interference signalj1Is the power of the 1 st interfering signal, Pj2Is the power of the 2 nd interfering signal.
Step 104: and determining the positioning performance of the satellite navigation terminal based on the electromagnetic interference allowance.
The positioning performance comprises a first-level performance and a second-level performance; the first-level performance is the performance when the satellite loss begins to occur in the satellite navigation terminal, namely the satellite navigation terminal is in a performance degradation area; the second-level performance is the performance when the total number of effective satellites in the satellite navigation terminal is less than the set minimum number of positioning satellites, namely the satellite navigation terminal is in a positioning lock losing area.
The step 104 specifically includes:
and sequencing the electromagnetic interference margins of all the satellites from large to small to obtain an interference margin sequence.
Judging whether the Mth electromagnetic interference allowance in the interference allowance sequence is zero or not to obtain a first judgment result; and M is the total number of effective satellites in the satellite navigation terminal.
And if the first judgment result is yes, determining that the satellite loss starts to occur in the satellite navigation terminal, and the satellite navigation terminal is in the first-level performance.
Judging whether the Nth electromagnetic interference allowance in the interference allowance sequence is zero or not to obtain a second judgment result; wherein N is the set lowest positioning satellite number.
If the second judgment result is yes, determining that the total number of the effective satellites in the satellite navigation terminal is less than the set minimum positioning satellite number, and the satellite navigation terminal is in the second-level performance.
In practical application, when the interference signal is a single-source electromagnetic interference signal, the specific process of determining the positioning performance is as follows:
assuming that the total number of effective satellites observed by the satellite navigation terminal is 6 and the minimum number of positioning satellites is 4, the following sequence is obtained according to the value of the electromagnetic interference margin:
Δi 1≥Δi 2≥Δi 3≥Δi 4≥Δi 5≥Δi 6；
the upper marks 1, 2, 3, 4, 5 and 6 are sorted from large to small in the electromagnetic interference margin. When in use
At the moment, the power P of the single-source electromagnetic interference signalsmEqual to the satellite with the EMI margin ordered in bit 6Tracking an out-of-lock threshold, wherein the positioning state of the satellite navigation terminal enters a performance degradation area, and the phenomenon that a satellite is lost begins to occur inside the satellite navigation terminal; when in use
When the total number of effective satellites in the satellite navigation terminal does not meet the minimum positioning requirement, the positioning state of the satellite navigation terminal enters a positioning lock losing area, the receiver is positioned and lost, and the power of a single-source electromagnetic interference signal is P at the momentfmEqual to the tracking loss-of-lock threshold of the satellite with the electromagnetic interference margin ordering at the 4 th position. Fig. 4 is an electromagnetic interference level curve of a satellite navigation terminal under different types of single-source electromagnetic interference signals and a schematic view of a positioning performance level of the navigation terminal, wherein the number of satellites initially tracked by the satellite navigation terminal is 6, the minimum positioning star number is required to be 4, and a satellite tracking lock losing threshold is predicted by an electromagnetic sensitivity threshold prediction model.
In practical application, when the interference signal is a dual-source electromagnetic interference signal, the specific process of determining the positioning performance is as follows:
similarly, assuming that the total number of effective satellites observed by the satellite navigation terminal is 6 and the minimum number of positioning satellites is 4, the following sequence is obtained according to the value of the electromagnetic interference margin:
Δi 1≥Δi 2≥Δi 3≥Δi 4≥Δi 5≥Δi 6；
the upper marks 1, 2, 3, 4, 5 and 6 are sorted from large to small in the electromagnetic interference margin. When in use
At this time, the power of the dual-source electromagnetic interference signal is Psm1And Psm2，Psm1And Psm2Satisfy the requirement of
The tracking loss-of-lock threshold value of the satellite sequenced at the 6 th position of the electromagnetic interference margin under the action of one interference signal,
the tracking out-of-lock threshold value of the satellite with the 6 th position sorted for the electromagnetic interference margin under the action of another interference signal is obtained; the positioning state of the navigation terminal enters a performance degradation area, and the phenomenon of satellite loss begins to occur inside the navigation terminal; when in use
Then, the total number of effective satellites in the satellite navigation terminal does not meet the minimum positioning requirement, the positioning state of the satellite navigation terminal enters a positioning lock losing area, the positioning of a receiver is lost, and the power of double-source electromagnetic interference signals is Pfm1And Pfm2，Pfm1And Pfm2Satisfy the requirement of
The tracking loss-of-lock threshold value of the satellite sequenced at the 4 th position of the electromagnetic interference margin under the action of one interference signal,
the tracking out-of-lock threshold value of the satellite with the 4 th position sorted for the electromagnetic interference margin under the action of another interference signal is obtained; where two interfering signals have multiple power combinations may cause the receiver to enter different interference levels.
The unmanned aerial vehicle satellite navigation terminal positioning performance of this embodiment is confirmed, specifically has following advantage:
(1) the method and the device can realize real-time assessment of the electromagnetic interference situation of the navigation terminal in the flight process of the unmanned aerial vehicle and prediction of the electromagnetic interference situation on the flight track, and realize real-time determination of the positioning performance of the satellite navigation terminal of the unmanned aerial vehicle under the condition of considering the electromagnetic interference.
(3) The method adopts a machine learning method to predict satellite tracking lock loss in the navigation terminal, the prediction accuracy is better than 3dB, the workload of obtaining a satellite sensitivity threshold value through test measurement is reduced, and the method is simple and efficient to operate.
(3) The initial carrier-to-noise ratio of the satellite is used as the initial state of the navigation terminal, and the problem of influence of environmental influence factors on the positioning performance of the navigation terminal is simplified.
(4) The method is suitable for predicting and evaluating the electromagnetic interference situation of the navigation terminal under the electromagnetic interference of the narrow band and the wide band.
(5) The method is suitable for predicting and evaluating the electromagnetic interference situation of the navigation terminal under the double-source electromagnetic interference.
This embodiment also provides an unmanned aerial vehicle satellite navigation terminal location performance determination system, refer to fig. 5, the system includes:
a data obtaining module 201, configured to obtain a feature parameter in a current state; the characteristic parameters comprise the frequency of the interference signal, the bandwidth of the interference signal and the initial carrier-to-noise ratio of each satellite in the satellite navigation terminal; the interference signal is a single-source electromagnetic interference signal or a multi-source electromagnetic interference signal.
And a threshold determination module 202, configured to input the feature parameter into an electromagnetic sensitivity threshold prediction model to obtain a tracking out-of-lock threshold of each satellite in the current state.
An electromagnetic interference margin determining module 203, configured to obtain an electromagnetic interference margin of each satellite based on the tracking loss-of-lock threshold and the power of the interference signal.
A performance determining module 204, configured to determine positioning performance of the satellite navigation terminal based on the electromagnetic interference margin; the positioning performance comprises a first-level performance and a second-level performance; the first-level performance is the performance when the satellite loss begins to occur in the satellite navigation terminal; the second-level performance is the performance when the total number of effective satellites in the satellite navigation terminal is less than the set minimum positioning satellite number.
As an optional implementation manner, the system for determining positioning performance of a drone satellite navigation terminal further includes: a prediction model determination module for determining the electromagnetic sensitivity threshold prediction model; the prediction model determining module specifically includes:
the information base construction unit is used for constructing an electromagnetic sensitivity information base by changing the type of an interference signal applied by a test satellite and changing characteristic parameters under different interference types by adopting a navigation terminal electromagnetic interference effect test; the electromagnetic sensitivity information base comprises characteristic parameters applied to the test satellite under various interference types and corresponding tracking unlocking threshold values.
And the model construction unit is used for obtaining the electromagnetic sensitivity threshold prediction model by adopting a machine learning method based on the electromagnetic sensitivity information base.
As an optional implementation manner, the model building unit specifically includes:
and the sample dividing subunit is used for dividing the electromagnetic sensitivity information base into a training sample and a test sample.
And the training subunit is used for inputting the training samples into the XGboost model, the GPR model and the SVR model respectively for training to obtain the trained XGboost model, the trained GPR model and the trained SVR model.
And the verification subunit is used for respectively verifying the trained XGboost model, the trained GPR model and the trained SVR model by adopting the test samples, and determining the model with the minimum training error as the electromagnetic sensitivity threshold prediction model.
The embodiments in the present description are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other. For the system disclosed by the embodiment, the description is relatively simple because the system corresponds to the method disclosed by the embodiment, and the relevant points can be referred to the method part for description.
The principles and embodiments of the present invention have been described herein using specific examples, which are provided only to help understand the method and the core concept of the present invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, the specific embodiments and the application range may be changed. In view of the above, the present disclosure should not be construed as limiting the invention.
Claims (4)
1. A method for determining positioning performance of an unmanned aerial vehicle satellite navigation terminal is characterized by comprising the following steps:
acquiring characteristic parameters in the current state; the characteristic parameters comprise the frequency of the interference signal, the bandwidth of the interference signal and the initial carrier-to-noise ratio of each satellite in the satellite navigation terminal; the interference signal is a single-source electromagnetic interference signal or a multi-source electromagnetic interference signal;
inputting the characteristic parameters into an electromagnetic sensitivity threshold prediction model to obtain tracking lock losing thresholds of the satellites in the current state;
obtaining an electromagnetic interference margin of each satellite based on the tracking loss-of-lock threshold and the power of the interference signal;
determining the positioning performance of the satellite navigation terminal based on the electromagnetic interference allowance; the positioning performance comprises a first-level performance and a second-level performance; the first-level performance is the performance when the satellite loss begins to occur in the satellite navigation terminal; the second-level performance is the performance when the total number of effective satellites in the satellite navigation terminal is less than the set minimum positioning satellite number;
the method for determining the electromagnetic sensitivity threshold prediction model comprises the following steps:
adopting a navigation terminal electromagnetic interference effect test, and constructing an electromagnetic sensitivity information base by changing the type of an interference signal applied by a test satellite and changing characteristic parameters under different interference types; the electromagnetic sensitivity information base comprises characteristic parameters applied to the test satellite under various interference types and corresponding tracking unlocking threshold values;
based on the electromagnetic sensitivity information base, obtaining the electromagnetic sensitivity threshold prediction model by adopting a machine learning method;
the method for obtaining the electromagnetic sensitivity threshold prediction model by adopting a machine learning method based on the electromagnetic sensitivity information base specifically comprises the following steps:
dividing the electromagnetic sensitivity information base into a training sample and a test sample;
respectively inputting the training samples into an XGboost model, a GPR model and an SVR model for training to obtain a trained XGboost model, a trained GPR model and a trained SVR model;
verifying the trained XGboost model, the trained GPR model and the trained SVR model respectively by using the test samples, and determining a model with the minimum training error as the electromagnetic sensitivity threshold prediction model;
the obtaining of the electromagnetic interference margin of each satellite based on the tracking out-of-lock threshold and the power of the interference signal specifically includes:
when the interference signal is a single-source electromagnetic interference signal, the calculation formula of the electromagnetic interference margin is
Δi＝Pi-Pj；
Wherein, DeltaiIs the electromagnetic interference margin, P, of the ith satelliteiIs the tracking out-of-lock threshold, P, for the ith satellitejIs the power of the single source electromagnetic interference signal;
the obtaining of the electromagnetic interference margin of each satellite based on the tracking out-of-lock threshold and the power of the interference signal specifically includes:
when the interference signal is a dual-source electromagnetic interference signal, the calculation formula of the electromagnetic interference margin is
Δi＝1-Si，
Wherein, DeltaiIs the electromagnetic interference margin of the ith satellite, SiIs the suppression factor, P, of the ith satellitei1Is the tracking out-of-lock threshold value, P, of the ith satellite under the action of the 1 st interference signali2Is the tracking out-of-lock threshold value, P, of the ith satellite under the action of the 2 nd interference signalj1As the 1 st interferencePower of signal, Pj2Is the power of the 2 nd interfering signal.
2. The method according to claim 1, wherein the determining the positioning performance of the satellite navigation terminal based on the electromagnetic interference margin specifically includes:
sequencing the electromagnetic interference margins of all satellites from large to small to obtain an interference margin sequence;
judging whether the Mth electromagnetic interference allowance in the interference allowance sequence is zero or not to obtain a first judgment result; wherein M is the total number of effective satellites in the satellite navigation terminal;
if the first judgment result is yes, determining that the satellite loss starts to occur in the satellite navigation terminal, and the satellite navigation terminal is in the first-level performance;
judging whether the Nth electromagnetic interference allowance in the interference allowance sequence is zero or not to obtain a second judgment result; wherein N is the set lowest positioning satellite number;
if the second judgment result is yes, determining that the total number of the effective satellites in the satellite navigation terminal is less than the set minimum positioning satellite number, and the satellite navigation terminal is in the second-level performance.
3. The method for determining the positioning performance of the satellite navigation terminal of the unmanned aerial vehicle according to claim 1, wherein the single-source electromagnetic interference signal is a continuous wave interference signal, a narrowband interference signal or a broadband interference signal; the multi-source electromagnetic interference signal includes at least two of a continuous wave interference signal, a narrowband interference signal, and a wideband interference signal.
4. An unmanned aerial vehicle satellite navigation terminal location performance determination system, its characterized in that includes:
the data acquisition module is used for acquiring characteristic parameters in the current state; the characteristic parameters comprise the frequency of the interference signal, the bandwidth of the interference signal and the initial carrier-to-noise ratio of each satellite in the satellite navigation terminal; the interference signal is a single-source electromagnetic interference signal or a multi-source electromagnetic interference signal;
the threshold value determining module is used for inputting the characteristic parameters into an electromagnetic sensitivity threshold value prediction model to obtain tracking lock losing threshold values of the satellites in the current state;
an electromagnetic interference margin determining module, configured to obtain an electromagnetic interference margin of each satellite based on the tracking out-of-lock threshold and the power of the interference signal;
a performance determination module, configured to determine positioning performance of the satellite navigation terminal based on the electromagnetic interference margin; the positioning performance comprises a first-level performance and a second-level performance; the first-level performance is the performance when the satellite loss begins to occur in the satellite navigation terminal; the second-level performance is the performance when the total number of effective satellites in the satellite navigation terminal is less than the set minimum positioning satellite number;
unmanned aerial vehicle satellite navigation terminal location performance determination system still includes: a prediction model determination module for determining the electromagnetic sensitivity threshold prediction model; the prediction model determining module specifically includes:
the information base construction unit is used for constructing an electromagnetic sensitivity information base by changing the type of an interference signal applied by a test satellite and changing characteristic parameters under different interference types by adopting a navigation terminal electromagnetic interference effect test; the electromagnetic sensitivity information base comprises characteristic parameters applied to the test satellite under various interference types and corresponding tracking unlocking threshold values;
the model construction unit is used for obtaining the electromagnetic sensitivity threshold prediction model by adopting a machine learning method based on the electromagnetic sensitivity information base;
the model building unit specifically comprises:
the sample dividing subunit is used for dividing the electromagnetic sensitivity information base into a training sample and a test sample;
the training subunit is used for inputting the training samples into the XGboost model, the GPR model and the SVR model respectively for training to obtain a trained XGboost model, a trained GPR model and a trained SVR model;
the verification subunit is used for respectively verifying the trained XGboost model, the trained GPR model and the trained SVR model by adopting the test samples, and determining the model with the minimum training error as the electromagnetic sensitivity threshold prediction model;
the obtaining of the electromagnetic interference margin of each satellite based on the tracking out-of-lock threshold and the power of the interference signal specifically includes:
when the interference signal is a single-source electromagnetic interference signal, the calculation formula of the electromagnetic interference margin is
Δi＝Pi-Pj；
Wherein, DeltaiIs the electromagnetic interference margin, P, of the ith satelliteiIs the tracking out-of-lock threshold, P, for the ith satellitejIs the power of the single source electromagnetic interference signal;
the obtaining of the electromagnetic interference margin of each satellite based on the tracking out-of-lock threshold and the power of the interference signal specifically includes:
when the interference signal is a dual-source electromagnetic interference signal, the calculation formula of the electromagnetic interference margin is
Δi＝1-Si，
Wherein, DeltaiIs the electromagnetic interference margin of the ith satellite, SiIs the suppression factor, P, of the ith satellitei1Is the tracking out-of-lock threshold value, P, of the ith satellite under the action of the 1 st interference signali2Is the tracking out-of-lock threshold value, P, of the ith satellite under the action of the 2 nd interference signalj1Is the power of the 1 st interfering signal, Pj2Is the power of the 2 nd interfering signal.
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