CN111817813A - Signal time delay-frequency shift parameter estimation and target signal recovery method based on signal sampling multivariate hypothesis test - Google Patents

Signal time delay-frequency shift parameter estimation and target signal recovery method based on signal sampling multivariate hypothesis test Download PDF

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CN111817813A
CN111817813A CN202010562321.6A CN202010562321A CN111817813A CN 111817813 A CN111817813 A CN 111817813A CN 202010562321 A CN202010562321 A CN 202010562321A CN 111817813 A CN111817813 A CN 111817813A
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方昌健
伍之昂
张璐
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NANJING AUDIT UNIVERSITY
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Abstract

The invention discloses a signal time delay-frequency shift parameter estimation method based on signal sampling multivariate hypothesis test, which comprises the steps of firstly, carrying out signal sampling on a link user of an unmanned aerial vehicle or a ground link user, and further constructing an energy parameter Y (n) ═ s (n) of a sampled signal s (n) at any moment2And calculating a decision threshold
Figure DDA0002544716300000011
λopt. Secondly, mixing Y (n) with
Figure DDA0002544716300000012
And λoptFor comparison, if
Figure DDA0002544716300000013
And Y (n)<λoptThen the sample time sequence number n is recorded and put into the set S. Thirdly, the time sequence numbers in the set S correspond to the links from the unmanned aerial vehicle/the ground linkThe signal of the user is sampled. Finally, by utilizing the synchronous relation of the receiving and transmitting ends of the unmanned aerial vehicle link and the ground communication link, the sampling areas from the unmanned aerial vehicle link and the ground link can be distinguished at the receiving end. From which time delay-frequency shift parameters of the air link and ground link signals can be estimated. And further filtering the interference and recovering the target signal by using the estimated time delay and frequency shift parameters.

Description

Signal time delay-frequency shift parameter estimation and target signal recovery method based on signal sampling multivariate hypothesis test
Technical Field
The method is suitable for the field of air-ground fusion communication in multipath propagation and node mobile environments, and interference avoidance between the air-ground links is further realized by estimating time delay-frequency shift parameters of the links of the unmanned aerial vehicle and the ground links by performing multivariate hypothesis test on the mixed sampling signals.
Background
The existing method for resisting the problems of multipath propagation and mobile Doppler frequency shift is mainly an orthogonal time-frequency air conditioning technology. The modulation technology utilizes diversity gain on a signal time-frequency domain, so that all modulation symbols experience the same time-invariant channel fading, and adverse effects of multipath time delay and Doppler frequency shift can be well resisted to reduce the signal transmission error rate. However, as a modulation technique, the existing orthogonal time-frequency-air conditioning method cannot solve the interference problem and the delay-frequency shift problem between heterogeneous systems. In the future 6G communication air-space-ground integrated background, system heterogeneity, node mobility and multipath propagation are widely faced problems in air-ground converged communication. Therefore, the problem that signal transmission under the scene of heterogeneous integration of unmanned aerial vehicle communication and ground communication is affected by time delay and frequency shift needs to be solved.
Disclosure of Invention
For a scene in which an unmanned aerial vehicle link and a ground communication link coexist, the existing technical scheme can only be applied to a communication scene of a single user link as a modulation technology, but is not applicable to a communication scene in which heterogeneous networks are fused. In order to overcome the defect that the traditional heterogeneous network interference avoiding method is affected by multipath time delay and Doppler frequency shift, the invention provides the following solution:
the technical scheme is as follows:
the invention firstly provides a signal time delay-frequency shift parameter estimation method based on signal sampling multivariate hypothesis test, which comprises the following steps:
s1, proposing a signal sampling hypothesis:
Figure BDA0002544716280000011
wherein, PuTransmit power for the drone link, DutIs the distance between the unmanned aerial vehicle link node and the ground link node, alphautFor corresponding path loss coefficients, ωutAndutthe frequency shift and the time delay of a line-of-sight path between an unmanned aerial vehicle link node and a ground link node are shown, wherein L is the number of paths and P is the number of pathsBSFor the base station transmitting power, hiIs the path loss per path, viAnd τiFrequency shift and time delay of each path; vectors a and b are data of a ground link and an unmanned aerial vehicle link, and w (t) is white Gaussian additive noise;
s2, constructing energy judgment statistic of sampling signal as Y (n) ═ y (n) & gtY2The four signal sampling hypotheses are examined to determine hypothesis H2And H3The lower sampling area is distinguished;
s2-1, mixing H4And H1Distinguish from assumptions;
S2-2、H2and H3Distinguishing according to the receiving and transmitting end synchronous information of the air link and the ground link;
s3, estimating a time delay-frequency shift parameter: h is to be3The sampling time sequence number m is stored in a set S, and a time delay-frequency shift parameter item is estimated:
Figure BDA0002544716280000021
in the formula, y (mT)s) For sampled versions of the aforesaid received signal y (t), i.e.
Figure BDA0002544716280000022
Wherein m and TsRespectively a sample number and a sample time interval.
Specifically, the S2-1 includes:
s2-1-1, setting a detection threshold with the minimum hypothesis detection error rate
Figure BDA0002544716280000023
Mixing Y (n) with
Figure BDA0002544716280000024
By comparison, H4Distinguish from four hypotheses;
s2-1-2, setting a detection threshold lambda with the minimum assumed detection error rateoptMixing Y (n) and λoptBy comparison, H1Distinguish from the remaining three hypotheses;
s2-1-3, obtaining H2And H3Sampling:
Figure BDA0002544716280000025
and Y (n)<λopt。
Preferably, the check threshold
Figure BDA0002544716280000026
Solving by:
Figure BDA0002544716280000027
in the formula, Pr { H1}＝(1-φu)φt+(1-φt)φuIs to assume H1And Pr { H }2}＝(1-φu)(1-φt) Is to assume H2Is the parameter phiuAnd phitRespectively representing the auxiliary time slot length proportion of the air link and the ground link; y (n) obeys a non-centric chi-square distribution with non-centric parameters:
Figure BDA0002544716280000028
in the formula (I), the compound is shown in the specification,
Figure BDA0002544716280000029
for the noise variance, the degree of freedom of the non-centric chi-square distribution is 1.
Preferably, the check threshold λoptSolving by:
Figure BDA0002544716280000031
wherein W (-) is a Lambert W function and the parameter di＝(1+κi)2/(1+2κi)，gi＝(1+2κi)/(1+κi) And κ isiIs assumed to be HiThe above non-central parameter of;
the non-central parameters are:
Figure BDA0002544716280000032
in the formula (I), the compound is shown in the specification,
Figure BDA0002544716280000033
the degree of freedom of non-central chi-square distribution is 1 for the noise variance;
the parameter C is:
Figure BDA0002544716280000034
in the formula, Pr { H1}＝(1-φu)φt+(1-φt)φuIs to assume H1And Pr { H }2}＝(1-φu)(1-φt) Is to assume H2Is the parameter phiuAnd phitIt represents the overhead timeslot length ratio of the air link and the terrestrial link, respectively.
The invention also discloses a target signal recovery method of the unmanned aerial vehicle link, which comprises the following steps:
s1, proposing a signal sampling hypothesis:
Figure BDA0002544716280000035
wherein, PuTransmit power for the drone link, DutBetween unmanned aerial vehicle link node and ground link nodeA distance ofutFor corresponding path loss coefficients, ωutAndutthe frequency shift and the time delay of a line-of-sight path between an unmanned aerial vehicle link node and a ground link node are shown, wherein L is the number of paths and P is the number of pathsBSFor the base station transmitting power, hiIs the path loss per path, viAnd τiFrequency shift and time delay of each path; vectors a and b are data of a ground link and an unmanned aerial vehicle link, and w (t) is white Gaussian additive noise;
s2, constructing energy judgment statistic of sampling signal as Y (n) ═ y (n) & gtY2The four signal sampling hypotheses are examined to determine hypothesis H2And H3The lower sampling area is distinguished;
s2-1, mixing H4And H1Distinguish from assumptions;
S2-2、H2and H3Distinguishing according to the receiving and transmitting end synchronous information of the air link and the ground link;
s3, estimating a time delay-frequency shift parameter: h is to be3The sampling time sequence number m is stored in a set S, and a time delay-frequency shift parameter item is estimated:
Figure BDA0002544716280000041
in the formula, y (mT)s) For sampled versions of the aforesaid received signal y (t), i.e.
Figure BDA0002544716280000042
Wherein m and TsRespectively a sampling sequence number and a sampling time interval;
s4, restoring the target signal of the unmanned aerial vehicle link into:
Figure BDA0002544716280000043
wherein T isuIs the symbol period of the drone link, and
Figure BDA0002544716280000044
preferably, in S2-1, the method includes:
s2-1-1, setting a detection threshold with the minimum hypothesis detection error rate
Figure BDA0002544716280000045
Mixing Y (n) with
Figure BDA0002544716280000046
By comparison, H4Distinguish from four hypotheses;
s2-1-2, setting a detection threshold lambda with the minimum assumed detection error rateoptMixing Y (n) and λoptBy comparison, H1Distinguish from the remaining three hypotheses;
s2-1-3, obtaining H2And H3Sampling:
Figure BDA0002544716280000047
and Y (n)<λopt。
Preferably, the check threshold
Figure BDA0002544716280000048
Solving by:
Figure BDA0002544716280000049
in the formula, Pr { H1}＝(1-φu)φt+(1-φt)φuIs to assume H1And Pr { H }2}＝(1-φu)(1-φt) Is to assume H2Is the parameter phiuAnd phitRespectively representing the auxiliary time slot length proportion of the air link and the ground link; y (n) obeys a non-centric chi-square distribution with non-centric parameters:
Figure BDA0002544716280000051
in the formula (I), the compound is shown in the specification,
Figure BDA0002544716280000052
for the noise variance, the degree of freedom of the non-centric chi-square distribution is 1.
Preferably, the check threshold λoptSolving by:
Figure BDA0002544716280000053
wherein W (-) is a Lambert W function and the parameter di＝(1+κi)2/(1+2κi)，gi＝(1+2κi)/(1+κi) And κ isiIs assumed to be HiThe above non-central parameter of;
the non-central parameters are:
Figure BDA0002544716280000054
in the formula (I), the compound is shown in the specification,
Figure BDA0002544716280000055
the degree of freedom of non-central chi-square distribution is 1 for the noise variance;
the parameter C is:
Figure BDA0002544716280000056
in the formula, Pr { H1}＝(1-φu)φt+(1-φt)φuIs to assume H1And Pr { H }2}＝(1-φu)(1-φt) Is to assume H2Is the parameter phiuAnd phitIt represents the overhead timeslot length ratio of the air link and the terrestrial link, respectively.
The invention has the advantages of
The invention provides a signal delay-frequency shift parameter estimation and air-ground fusion communication interference avoidance method based on signal sampling multivariate hypothesis test, which can solve the problem that signal transmission of two heterogeneous networks of an air communication system and a ground system is adversely affected by multipath delay and mobile Doppler frequency shift, and eliminate mutual interference between the heterogeneous networks.
The invention realizes the filtering of the air-ground fusion communication link to the interference and the recovery of the target signal based on the time delay and the frequency shift parameter estimation of the signal, and avoids the adverse effect of the wireless multipath propagation and the mobile environment to the signal transmission. Since the adverse effects of the time delay and frequency shift have been taken into account when processing the signal at the receiving end, the recovery of the target signal is no longer affected by the time delay and frequency shift.
Drawings
FIG. 1 shows an application scenario of the present invention (a scenario in which an unmanned aerial vehicle link and a ground link coexist)
FIG. 2 is a comparison of interference performance between the proposed method and the conventional method
FIG. 3 is a comparison of the probability performance of communication interruption between the proposed method and the conventional method
Detailed Description
The invention is further illustrated by the following examples, without limiting the scope of the invention:
the technical scheme of the application is detailed in the summary of the invention, and the principle of the technical scheme is explained here:
in the co-existence scenario of the drone link and the ground communication link shown in fig. 1, the drone link includes a drone platform and a downlink user, and the ground link includes a base station and a mobile terminal. The unmanned aerial vehicle downlink user and the user terminal have mobility. Thus, both the signal transmission of the drone link and the ground link are affected by multipath propagation and doppler shift. For the receiving end of the unmanned aerial vehicle link or the ground link, the sampling signal is either the superposition of the target signal and the interference signal, or only a single-user signal or a pure noise signal, that is, the following possibilities exist:
Figure BDA0002544716280000061
wherein, PuTransmit power for the drone link, DutFor between unmanned aerial vehicle link node and ground link nodeDistance, αutFor corresponding path loss coefficients, ωutAndutthe frequency shift and the time delay of a line-of-sight path between an unmanned aerial vehicle link node and a ground link node are shown, wherein L is the number of paths and P is the number of pathsBSFor the base station transmitting power, hiIs the path loss per path, viAnd τiFrequency shift and time delay for each path. Vectors a and b are data for the ground link and drone link, and w (t) is white gaussian additive noise.
To test the four signal sampling hypotheses described above, hypothesis H is assumed2And H3The following sampling regions are distinguished, and the energy judgment statistic of the constructed sampling signal is as follows:
Y(n)＝|y(n)|2(2)
since noise w (n) is a gaussian random variable, y (n) follows a non-centric chi-squared distribution, and the non-centric parameters are:
Figure BDA0002544716280000071
parameter(s)
Figure BDA0002544716280000072
Is the noise variance, and the degree of freedom of the above-mentioned non-central chi-square distribution is 1. Accordingly, the probability density function for y (n) is:
Figure BDA0002544716280000073
wherein In(x) Modified Bessel functions of the first type.
Firstly, H is put in4The detection threshold that distinguishes from the four hypotheses such that the hypothesis detection error rate is the smallest is:
Figure BDA0002544716280000074
wherein Pr { H1}＝(1-φu)φt+(1-φt)φuIs to assume H1And Pr { H }2}＝(1-φu)(1-φt) Is to assume H2Is the parameter phiuAnd phitIt represents the overhead timeslot length ratio of the air link and the terrestrial link, respectively.
Second, hypothesis H1 is tested from the remaining three hypotheses H1-H3 such that the test threshold for the hypothesis test error rate is the minimum:
Figure BDA0002544716280000075
wherein
Figure BDA0002544716280000076
And W (-) is a Lambert W function,
parameter di＝(1+κi)2/(1+2κi)，gi＝(1+2κi)/(1+κi) And κ isiIs assumed to be HiThe following non-central parameters.
According to Y (n) and
Figure BDA0002544716280000077
and λoptCan assume H4And H1The lower sample is examined. Then, H remains2And H3The down-sampling can be directly distinguished according to the synchronization information of the transmitting and receiving ends of the air link and the ground link. For example, a sample of an unmanned link node during its link data transmission idle period is a signal sample from a ground link; the ground link node samples the signal from the drone link at a time when its link data transmission is idle for a short time. Thereby further converting H2And H3The samples under the two assumptions are distinguished. H is to be3The lower sampling time sequence number m is stored in the set S. Thus, the delay-frequency shift parameter term can be estimated as:
Figure BDA0002544716280000081
and then the target signal of the unmanned aerial vehicle link is recovered as:
Figure BDA0002544716280000082
wherein T isuIs the symbol period of the drone link, and
Figure BDA0002544716280000083
the simulation results shown in fig. 2 and fig. 3 show that, compared with the conventional method, the air-ground link interference avoidance method based on signal delay-frequency shift delay parameter estimation provided by the invention can better suppress the interference between unmanned aerial vehicle communication and ground communication in the signal delay and frequency shift domain, and can improve the robustness of signal reception to the delay and frequency shift problem to reduce the communication interruption probability.
The specific embodiments described herein are merely illustrative of the spirit of the invention. Various modifications or additions may be made to the described embodiments or alternatives may be employed by those skilled in the art without departing from the spirit or ambit of the invention as defined in the appended claims.

Claims (8)

1. A signal time delay-frequency shift parameter estimation method based on signal sampling multivariate hypothesis test is applied to a coexisting scene of an unmanned aerial vehicle link and a ground communication link, and is characterized by comprising the following steps of:
s1, proposing a signal sampling hypothesis:
Figure FDA0002544716270000011
wherein, PuTransmit power for the drone link, DutIs the distance between the unmanned aerial vehicle link node and the ground link node, alphautFor corresponding path loss coefficients, ωutAndutthe frequency shift and the time delay of a line-of-sight path between an unmanned aerial vehicle link node and a ground link node are shown, wherein L is the number of paths and P is the number of pathsBSFor the base station transmitting power, hiIs the path loss per path, viAnd τiFrequency shift and time delay of each path; vectors a and b are data of a ground link and an unmanned aerial vehicle link, and w (t) is white Gaussian additive noise;
s2, constructing energy judgment statistic of sampling signal as Y (n) ═ y (n) & gtY2The four signal sampling hypotheses are examined to determine hypothesis H2And H3The lower sampling area is distinguished;
s2-1, mixing H4And H1Distinguish from assumptions;
S2-2、H2and H3Distinguishing according to the receiving and transmitting end synchronous information of the air link and the ground link;
s3, estimating a time delay-frequency shift parameter: h is to be3The sampling time sequence number m is stored in a set S, and a time delay-frequency shift parameter item is estimated:
Figure FDA0002544716270000012
in the formula, y (mT)s) For sampled versions of the aforesaid received signal y (t), i.e.
Figure FDA0002544716270000015
Wherein m and TsRespectively a sample number and a sample time interval.
2. The method according to claim 1, wherein in the S2-1, comprising:
s2-1-1, setting a detection threshold with the minimum hypothesis detection error rate
Figure FDA0002544716270000013
Mixing Y (n) with
Figure FDA0002544716270000014
By comparison, H4Distinguish from four hypotheses;
s2-1-2, setting a detection threshold lambda with the minimum assumed detection error rateoptMixing Y (n) and λoptBy comparison, H1Distinguish from the remaining three hypotheses;
s2-1-3, obtaining H2And H3Sampling:
Figure FDA0002544716270000021
and Y (n)<λopt。
3. The method of claim 2, wherein the verification threshold is set
Figure FDA0002544716270000022
Solving by:
Figure FDA0002544716270000023
in the formula, Pr { H1}＝(1-φu)φt+(1-φt)φuIs to assume H1And Pr { H }2}＝(1-φu)(1-φt) Is to assume H2Is the parameter phiuAnd phitRespectively representing the auxiliary time slot length proportion of the air link and the ground link; y (n) obeys a non-centric chi-square distribution with non-centric parameters:
Figure FDA0002544716270000024
in the formula (I), the compound is shown in the specification,
Figure FDA0002544716270000025
for the noise variance, the degree of freedom of the non-centric chi-square distribution is 1.
4. Method according to claim 2, characterized in that the check threshold λ isoptSolving by:
Figure FDA0002544716270000026
wherein W (-) is a Lambert W function and the parameter di＝(1+κi)2/(1+2κi)，gi＝(1+2κi)/(1+κi) And κ isiIs assumed to be HiThe above non-central parameter of;
the non-central parameters are:
Figure FDA0002544716270000027
in the formula (I), the compound is shown in the specification,
Figure FDA0002544716270000028
the degree of freedom of non-central chi-square distribution is 1 for the noise variance;
the parameter C is:
Figure FDA0002544716270000031
in the formula, Pr { H1}＝(1-φu)φt+(1-φt)φuIs to assume H1And Pr { H }2}＝(1-φu)(1-φt) Is to assume H2Is the parameter phiuAnd phitIt represents the overhead timeslot length ratio of the air link and the terrestrial link, respectively.
5. A target signal recovery method of an unmanned aerial vehicle link is applied to a scene where the unmanned aerial vehicle link and a ground communication link coexist, and is characterized by comprising the following steps:
s1, proposing a signal sampling hypothesis:
Figure FDA0002544716270000032
wherein, PuTransmit power for the drone link, DutIs the distance between the unmanned aerial vehicle link node and the ground link node, alphautFor corresponding path loss coefficients, ωutAndutthe frequency shift and the time delay of a line-of-sight path between an unmanned aerial vehicle link node and a ground link node are shown, wherein L is the number of paths and P is the number of pathsBSFor the base station transmitting power, hiIs the path loss per path, viAnd τiFrequency shift and time delay of each path; vectors a and b are data of a ground link and an unmanned aerial vehicle link, and w (t) is white Gaussian additive noise;
s2, constructing energy judgment statistic of sampling signal as Y (n) ═ y (n) & gtY2The four signal sampling hypotheses are examined to determine hypothesis H2And H3The lower sampling area is distinguished;
s2-1, mixing H4And H1Distinguish from assumptions;
S2-2、H2and H3Distinguishing according to the receiving and transmitting end synchronous information of the air link and the ground link;
s3, estimating a time delay-frequency shift parameter: h is to be3The sampling time sequence number m is stored in a set S, and a time delay-frequency shift parameter item is estimated:
Figure FDA0002544716270000033
in the formula, y (mT)s) For sampled versions of the aforesaid received signal y (t), i.e.
Figure FDA0002544716270000034
Wherein m and TsRespectively a sampling sequence number and a sampling time interval;
s4, restoring the target signal of the unmanned aerial vehicle link into:
Figure FDA0002544716270000041
wherein T isuIs the symbol period of the drone link, and
Figure FDA0002544716270000042
6. the method according to claim 5, wherein in the S2-1, comprising:
s2-1-1, setting a detection threshold with the minimum hypothesis detection error rate
Figure FDA0002544716270000043
Mixing Y (n) with
Figure FDA0002544716270000044
By comparison, H4Distinguish from four hypotheses;
s2-1-2, setting a detection threshold lambda with the minimum assumed detection error rateoptMixing Y (n) and λoptBy comparison, H1Distinguish from the remaining three hypotheses;
s2-1-3, obtaining H2And H3Sampling:
Figure FDA0002544716270000045
and Y (n)<λopt。
7. The method of claim 6, wherein the verification threshold is set
Figure FDA0002544716270000046
Solving by:
Figure FDA0002544716270000047
in the formula, Pr { H1}＝(1-φu)φt+(1-φt)φuIs to assume H1And Pr { H }2}＝(1-φu)(1-φt) Is to assume H2Is the parameter phiuAnd phitRespectively representing the auxiliary time slot length proportion of the air link and the ground link; y (n) obeys a non-centric chi-square distribution with non-centric parameters:
Figure FDA0002544716270000048
in the formula (I), the compound is shown in the specification,
Figure FDA0002544716270000049
for the noise variance, the degree of freedom of the non-centric chi-square distribution is 1.
8. Method according to claim 6, characterized in that the check threshold λ isoptSolving by:
Figure RE-FDA0002673914140000051
wherein W (-) is a Lambert W function and the parameter di＝(1+κi)2/(1+2κi)，gi＝(1+2κi)/(1+κi) And κ isiIs assumed to be HiThe above non-central parameter of;
the non-central parameters are:
Figure RE-FDA0002673914140000052
in the formula (I), the compound is shown in the specification,
Figure RE-FDA0002673914140000053
the degree of freedom of non-central chi-square distribution is 1 for the noise variance;
the parameter C is:
Figure RE-FDA0002673914140000054
in the formula, Pr { H1}＝(1-φu)φt+(1-φt)φuIs to assume H1And Pr { H }2}＝(1-φu)(1-φt) Is to assume H2Is the parameter phiuAnd phitRespectively indicate airlink and groundThe slot length ratio is assisted by the amount of the face link.
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Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103117817A (en) * 2013-01-09 2013-05-22 北京邮电大学 Spectrum sensing method and device under time varying fading channel
CN103746954A (en) * 2014-01-23 2014-04-23 东南大学 Associated synchronization and frequency offset estimation method for OFDM (Orthogonal Frequency Division Multiplexing) system
WO2016025044A2 (en) * 2014-05-12 2016-02-18 Unmanned Innovation, Inc. Distributed unmanned aerial vehicle architecture
CN105636061A (en) * 2016-01-14 2016-06-01 南京邮电大学 Uniform and precise energy detection method on general fading channels
CN109495906A (en) * 2018-11-09 2019-03-19 重庆邮电大学 Based on unmanned plane-earth station's link prediction unmanned plane gateway selection algorithm

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103117817A (en) * 2013-01-09 2013-05-22 北京邮电大学 Spectrum sensing method and device under time varying fading channel
CN103746954A (en) * 2014-01-23 2014-04-23 东南大学 Associated synchronization and frequency offset estimation method for OFDM (Orthogonal Frequency Division Multiplexing) system
WO2016025044A2 (en) * 2014-05-12 2016-02-18 Unmanned Innovation, Inc. Distributed unmanned aerial vehicle architecture
CN105636061A (en) * 2016-01-14 2016-06-01 南京邮电大学 Uniform and precise energy detection method on general fading channels
CN109495906A (en) * 2018-11-09 2019-03-19 重庆邮电大学 Based on unmanned plane-earth station's link prediction unmanned plane gateway selection algorithm

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
YINGYING ZHANG等: "Research on Panorama Reconstruction Technique of UAV Aerial Image Based on Improved ORB Algorithm", 《IEEE》 *

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