CN112616132A - Low-altitude air-ground unmanned aerial vehicle channel multipath tracking method based on geometric prior model - Google Patents

Low-altitude air-ground unmanned aerial vehicle channel multipath tracking method based on geometric prior model Download PDF

Info

Publication number
CN112616132A
CN112616132A CN202011488728.5A CN202011488728A CN112616132A CN 112616132 A CN112616132 A CN 112616132A CN 202011488728 A CN202011488728 A CN 202011488728A CN 112616132 A CN112616132 A CN 112616132A
Authority
CN
China
Prior art keywords
multipath
propagation
term
short
instantaneous
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Granted
Application number
CN202011488728.5A
Other languages
Chinese (zh)
Other versions
CN112616132B (en
Inventor
胡塞·罗德里格斯·皮内罗
黄泽宇
蔡雪松
托马斯·迪亚兹·博拉尼诺
尹学锋
王宇
朱虹
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Tongji University
Original Assignee
Tongji University
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Tongji University filed Critical Tongji University
Priority to CN202011488728.5A priority Critical patent/CN112616132B/en
Publication of CN112616132A publication Critical patent/CN112616132A/en
Application granted granted Critical
Publication of CN112616132B publication Critical patent/CN112616132B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/30—Services specially adapted for particular environments, situations or purposes
    • H04W4/40—Services specially adapted for particular environments, situations or purposes for vehicles, e.g. vehicle-to-pedestrians [V2P]
    • H04W4/44—Services specially adapted for particular environments, situations or purposes for vehicles, e.g. vehicle-to-pedestrians [V2P] for communication between vehicles and infrastructures, e.g. vehicle-to-cloud [V2C] or vehicle-to-home [V2H]
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B17/00—Monitoring; Testing
    • H04B17/30—Monitoring; Testing of propagation channels
    • H04B17/391—Modelling the propagation channel
    • H04B17/3912—Simulation models, e.g. distribution of spectral power density or received signal strength indicator [RSSI] for a given geographic region
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L45/00—Routing or path finding of packets in data switching networks
    • H04L45/24—Multipath

Landscapes

  • Engineering & Computer Science (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
  • Physics & Mathematics (AREA)
  • Spectroscopy & Molecular Physics (AREA)
  • Electromagnetism (AREA)
  • Position Fixing By Use Of Radio Waves (AREA)

Abstract

The invention relates to a geometric prior model-based low-altitude air-ground unmanned aerial vehicle channel multipath tracking method, which specifically comprises the following steps: s1, acquiring instantaneous propagation multipath component parameters of communication signals between the base station and the unmanned aerial vehicle; s2, according to the parameters of the instantaneous propagation multipath components, identifying the evolution of the short-term propagation multipath components through the short-term space consistency of the instantaneous propagation multipath components to obtain short-term propagation multipath; and S3, determining distance evaluation measurement according to the prior information of the short-term propagation multipath, judging the short-term propagation multipath belonging to the same blocked long-term propagation multipath according to the distance evaluation measurement, and combining the corresponding short-term propagation multipath to obtain the long-term propagation multipath. Compared with the prior art, the method has the advantages of effectively tracking the propagation multipath evolution, improving the accuracy of the propagation multipath modeling in the low-altitude unmanned air-ground channel propagation environment and the like.

Description

Low-altitude air-ground unmanned aerial vehicle channel multipath tracking method based on geometric prior model
Technical Field
The invention relates to the technical field of wireless communication, in particular to a low-altitude air-ground unmanned aerial vehicle channel multipath tracking method based on a geometric prior model.
Background
With the development of 5G communication, unmanned aerial vehicles have wider application scenes, such as aerial imaging, agricultural application, cargo transportation, search and rescue tasks, remote sensing technology and the like. Most of the applications mentioned above rely on a stable communication connection between the drone and the base station, which requires accurate channel modeling of the drone air-to-ground radio wave propagation channel. But due to the changing environment in a time-varying scenario, the number of propagation multipaths and the parameters related thereto may vary with the movement of the drone. When the channels are characterized, the channels at different times are regarded as independent channels, and some valuable information may be lost, thereby leading to potential erroneous conclusions, for example, the spatial consistency of the channels may be lost, so that it is very important to track the time evolution of the propagation multipath for channel modeling.
Different types of algorithms have been proposed in the prior art to track the time evolution of the propagation multipath. An extended kalman filter has been proposed to track the time delay, angle and signal amplitude of propagation multipath. Meanwhile, a kalman enhanced super-resolution tracking algorithm is proposed in research, which uses the output of the maximum likelihood estimator instead of the received signal as a measurement model and can also determine the order of the time-varying model (i.e., the number of propagation multi-paths). There have also been studies to propose a particle filter to overcome the problem of inaccurate linearization, but with the disadvantage of higher complexity. In addition, there are also studies to apply image processing techniques to directly track the evolution trajectory of propagation multipath using the time-varying delay power spectrum. However, the propagation environment of the low-altitude unmanned air-ground channel is generally scattered, propagation multipath is easily blocked, great difficulty is brought to modeling, and the problem is difficult to overcome by the method.
Disclosure of Invention
The invention aims to provide a low-altitude air-ground unmanned aerial vehicle channel multipath tracking method based on a geometric prior model in order to overcome the defect that the prior art cannot overcome the influence caused by the blockage of multipath propagation in the low-altitude unmanned aerial vehicle air-ground channel propagation environment.
The purpose of the invention can be realized by the following technical scheme:
a low-altitude air-ground unmanned aerial vehicle channel multipath tracking method based on a geometric prior model specifically comprises the following steps:
s1, acquiring instantaneous propagation multipath component parameters of communication signals between the base station and the unmanned aerial vehicle;
s2, according to the instantaneous propagation multipath component parameters, identifying the short-term propagation multipath component evolution through the short-term space consistency of the instantaneous propagation multipath components to obtain short-term propagation multipath;
s3, determining distance evaluation measurement according to the prior information of the short-term propagation multipath, judging the short-term propagation multipath belonging to the same blocked long-term propagation multipath according to the distance evaluation measurement, and combining the corresponding short-term propagation multipath to obtain the long-term propagation multipath.
The path types of the instantaneous propagation multipath include a direct path and a scattered path.
Further, the propagation time of the scattering path is specified as the sum of the square root of the hyperbola and a constant part.
The instantaneous propagation multipath component parameters include time delay, doppler frequency and power.
Further, the instantaneous propagation multipath component is specifically a time-varying channel impulse response, and a calculation formula is as follows:
Figure BDA0002840109840000021
wherein,t is a time variable, τ is a time delay, L is a total number of paths, αi,lFor the first path complex signal amplitude, vi,lAnd τi,lThe doppler frequency and the time delay corresponding to the ith path, respectively, and δ (·) is a dirac function.
Further, the instantaneous propagation multipath component parameters are calculated by a space iteration expectation maximization algorithm.
The short-term spatial consistency of the instantaneous propagation multipath components is specifically a short-term distance measure between the instantaneous propagation multipath components, which is specifically as follows:
Figure BDA0002840109840000022
wherein DPt(u1,u2) Is a measure of the short-term distance between two instantaneous propagation multipaths, u1And u2The method is a four-dimensional vector representation of instantaneous propagation multipath, and specifically comprises the following steps:
Figure BDA0002840109840000023
Figure BDA0002840109840000024
wherein u is1Is the horizontal distance between the drone and the base station, u2,u3,u4Power, time delay and Doppler frequency of corresponding instantaneous propagation multipath, respectivelyTDenotes a transpose operation, t ═ t1,t2,t3,t4)TAnd the threshold values represent four preset parameters of horizontal distance, power, time delay and Doppler frequency between the base station and the unmanned aerial vehicle.
Further, when none of the four parameter portions of the short-term distance metric exceeds 1, it is determined that the two instantaneous propagation multipaths belong to a common short-term propagation multipath.
Weights are set in the short-term distance measurementw＝(w1,w2,w3,w4)TThe specific gravity of each part of the distance measure is controlled to improve the practical applicability of the algorithm.
The distance estimation metric is based on non-overlapping short-term propagation multipaths, the non-overlapping decision process of which is as follows:
Figure BDA0002840109840000031
OV(A,B)≤dOV
where A and B represent short-term propagation multipaths, OV (A, B) represents the number of instantaneous propagation multipaths containing the same number for both short-term propagation multipaths, dOVFor non-overlapping thresholds, the OVI is specifically:
Figure BDA0002840109840000032
when satisfying OV (A, B) less than or equal to dOVIt is determined that the two short-term propagation multipaths are non-overlapping.
Further, the distance evaluation metric includes a delay difference evaluation metric and a distance ratio evaluation metric, as follows:
Figure BDA0002840109840000033
Figure BDA0002840109840000034
where DH (S) is a delay-difference evaluation metric, DS (S, d)DS) Evaluating the metric for distance ratio, dDSIs a proportional threshold, S ═ S1,s2,…s|S|Is a non-overlapping short-term propagation multipath, L ═ L1,l2,…l|L|Instantaneous propagation multipath, which is non-overlapping short-term propagation multipath, increases according to the horizontal distance between the base station and the droneSequentially arranging the sampling points on the back connecting line, Q ═ Q1,q2,…q|Q|The long-term propagation multipath fitting path is satisfied with L and Q,
Figure BDA0002840109840000035
Figure BDA0002840109840000036
Figure BDA0002840109840000037
and
Figure BDA0002840109840000038
is the corresponding time delay;
the time delay difference evaluation metric is provided with a corresponding time delay difference threshold value dDHThe distance scale evaluation metric is provided with a corresponding distance scale threshold pDSAnd when the distance evaluation metrics of the two short-term propagation multi-paths are smaller than the time delay difference threshold and the distance ratio threshold at the same time, judging that the two short-term propagation multi-paths belong to the common long-term propagation multi-path.
Compared with the prior art, the invention has the following beneficial effects:
the method is based on the realistic assumption in the typical propagation environment of the low-altitude air-ground unmanned aerial vehicle communication in the urban or suburban scene, considers the potential geometric characteristics causing the propagation multipath change, carries out geometric modeling on the evolution of the propagation multipath, can effectively detect the evolution of the related propagation multipath in a long distance even under the condition that the propagation path is partially shielded, simulates the corresponding long-term propagation multipath, and improves the accuracy of the propagation multipath modeling when the low-altitude unmanned aerial vehicle communication channel is blocked in the propagation environment.
Drawings
FIG. 1 is a schematic flow diagram of the present invention;
fig. 2 is a schematic diagram of a simplified air-ground drone communication scenario in an embodiment of the present invention;
FIG. 3 is a diagram illustrating results of fitting a quadratic function curve to a propagation delay curve of a scattering path of a communication signal according to an embodiment of the present invention;
FIG. 4 is a diagram illustrating a distance estimation metric in accordance with an embodiment of the present invention;
FIG. 5 is a schematic diagram of an initial latency power spectrum in an embodiment of the present invention;
FIG. 6 is a diagram illustrating the results of identifying short-term propagation multipaths in an embodiment of the present invention;
FIG. 7 is a diagram illustrating the results of identifying long-term propagation multipath in an embodiment of the present invention;
FIG. 8 is a graph comparing the results of identifying propagation multipath evolution with theoretical calculations in an embodiment of the present invention;
FIG. 9 is a diagram illustrating the results of identifying the propagation multi-path evolution in the Doppler domain according to an embodiment of the present invention;
FIG. 10 is a schematic flow chart of the present invention for identifying short-term propagation multipath;
fig. 11 is a flow chart of long-term short-term propagation multipath according to the present invention.
Detailed Description
The invention is described in detail below with reference to the figures and specific embodiments. The present embodiment is implemented on the premise of the technical solution of the present invention, and a detailed implementation manner and a specific operation process are given, but the scope of the present invention is not limited to the following embodiments.
Examples
As shown in fig. 1, a geometric prior model-based low-altitude air-ground unmanned aerial vehicle channel multipath tracking method specifically includes the following steps:
s1, acquiring instantaneous propagation multipath component parameters of communication signals between the base station and the unmanned aerial vehicle;
s2, according to the parameters of the instantaneous propagation multipath components, identifying the evolution of the short-term propagation multipath components through the short-term space consistency of the instantaneous propagation multipath components to obtain short-term propagation multipath;
and S3, determining distance evaluation measurement according to the prior information of the short-term propagation multipath, judging the short-term propagation multipath belonging to the same blocked long-term propagation multipath according to the distance evaluation measurement, and combining the corresponding short-term propagation multipath to obtain the long-term propagation multipath.
The types of paths to which the instantaneous propagation multipath belongs include direct paths and scattered paths.
The propagation time of the scattering path is in particular the sum of the square root of the hyperbola and a constant part.
The instantaneous propagation multipath component parameters include time delay, doppler frequency and power.
The instantaneous propagation multipath component is specifically a time-varying channel impulse response, and the calculation formula is as follows:
Figure BDA0002840109840000051
wherein t is a time variable, τ is a time delay, L is a total path number, αi,lFor the first path complex signal amplitude, vi,lAnd τi,lThe doppler frequency and the time delay corresponding to the ith path, respectively, and δ (·) is a dirac function.
The instantaneous propagation multipath component parameters are calculated by a space iteration expectation maximization algorithm.
As shown in fig. 10, the short-term spatial coherence of the instantaneous propagation multipath components is specifically a short-term distance measure between the instantaneous propagation multipath, which is specifically as follows:
Figure BDA0002840109840000052
wherein DPt(u1,u2) Is a measure of the short-term distance between two instantaneous propagation multipaths, u1And u2The method is a four-dimensional vector representation of instantaneous propagation multipath, and specifically comprises the following steps:
Figure BDA0002840109840000053
Figure BDA0002840109840000054
wherein u is1Is the horizontal distance between the drone and the base station, u2,u3,u4Power, time delay and Doppler frequency of corresponding instantaneous propagation multipath, respectivelyTDenotes a transpose operation, t ═ t1,t2,t3,t4)TAnd the threshold values represent four preset parameters of horizontal distance, power, time delay and Doppler frequency between the base station and the unmanned aerial vehicle.
When none of the four parameter portions of the short-term distance metric exceeds 1, it is determined that the two instantaneous propagation multipaths belong to a common short-term propagation multipath.
The short-term distance measurement is provided with a weight w ═ (w ═ w1,w2,w3,w4)TThe specific gravity of each part of the distance measure is controlled to improve the practical applicability of the algorithm.
As shown in fig. 11, the distance estimation metric is based on non-overlapping short-term propagation multipaths, and the non-overlapping decision process of the short-term propagation multipaths is as follows:
Figure BDA0002840109840000061
OV(A,B)≤dOV
where A and B represent short-term propagation multipaths, OV (A, B) represents the number of instantaneous propagation multipaths containing the same number for both short-term propagation multipaths, dOVFor non-overlapping thresholds, the OVI is specifically:
Figure BDA0002840109840000062
when satisfying OV (A, B) less than or equal to dOVIt is determined that the two short-term propagation multipaths are non-overlapping.
As shown in fig. 4, the distance evaluation metric includes a delay difference evaluation metric and a distance ratio evaluation metric, as follows:
Figure BDA0002840109840000063
Figure BDA0002840109840000064
where DH (S) is a delay-difference evaluation metric, DS (S, d)DS) Evaluating the metric for distance ratio, dDSIs a proportional threshold, S ═ S1,s2,…s|S|Is a non-overlapping short-term propagation multipath, L ═ L1,l2,…l|L|The sampling points on the rear connecting line are arranged according to the sequence that the horizontal distance between the base station and the unmanned aerial vehicle is increased, wherein the instantaneous propagation multipath is the non-overlapped short-term propagation multipath, and Q is (Q)1,q2,…q|Q|The long-term propagation multipath fitting path is satisfied with L and Q,
Figure BDA0002840109840000065
Figure BDA0002840109840000066
Figure BDA0002840109840000067
and
Figure BDA0002840109840000068
is the corresponding time delay;
the time delay difference evaluation metric is provided with a corresponding time delay difference threshold value dDHThe distance scale evaluation metric is provided with a corresponding distance scale threshold pDSAnd when the distance evaluation metrics of the two short-term propagation multi-paths are smaller than the delay difference threshold and the distance ratio threshold at the same time, judging that the two short-term propagation multi-paths belong to the common long-term propagation multi-path.
As shown in fig. 2, a simplified communication scenario between an air-ground drone and a ground base station is shown in the scenario that the drone communicates with the ground base station during low-altitude uniform-speed flight. Wherein point B represents the location of the base station and has a height hB(ii) a The blue dotted line represents the uniform flight track of the unmanned plane U, and the flight height is always hU(ii) a S represents a scatterer in the propagation environment and has a height hSThe horizontal distance between the distance base station and the flight path of the unmanned aerial vehicle is dSAnd dU(ii) a A non-reflection obstacle is arranged in the propagation environment, and the horizontal distance from the flight path of the unmanned aerial vehicle is dW。
In fig. 2, the dotted line is a direct path, the solid line is a scattering path, and the total distance of the signal from the base station to the unmanned aerial vehicle is dps, comprising two parts:
Figure BDA0002840109840000069
and
Figure BDA00028401098400000610
neglecting the power loss of the signal in the transmission process, the propagation time of the direct path signal is:
Figure BDA00028401098400000611
wherein, c0Represents the speed of light, and the propagation time of the scattering path is:
Figure BDA0002840109840000071
Figure BDA0002840109840000072
wherein:
Figure BDA0002840109840000073
Figure BDA0002840109840000074
thus, the following results were obtained:
Figure BDA0002840109840000075
obtained in the above formula
Figure BDA0002840109840000076
It can be simplified to a standard hyperbolic equation, namely:
Figure BDA0002840109840000077
wherein, A is 1, C is 1, D is-2Ds,E＝0,
Figure BDA0002840109840000078
Based on this conclusion, dps can be expressed as the sum of the square root of the hyperbola and a constant part, i.e.:
Figure BDA0002840109840000079
the above model shows that the propagation time evolution of the signal scattering path can be modeled as the sum of the square root of a hyperbola and a constant part. This model is equally valid for direct paths of signals, which can be seen as a special case of scattered paths, i.e. the case where the scatterer is co-located with the base station. For ease of computational processing, this hyperbola is approximated using a curve of a quadratic function. The result of fitting the propagation time evolution of the signal scattering path with a quadratic curve is shown in fig. 3, where the solid line represents the square root curve of a hyperbola, the dashed line represents the fitted quadratic curve, and the different curves are based on the horizontal distances d between the different scatterers and the trajectory of the droneUAnd (4) obtaining the product. As can be seen from FIG. 3, for different dUThe quadratic function curve can be well approximated to a hyperbola, and the degree of freedom of establishing the model is greatly reduced by the approximation.
An initial delay power spectrum is shown in fig. 5, where the X-axis represents the horizontal distance between the drone and the base station and the Y-axis is the delay of the propagation multipath; each point in the graph represents an instantaneous propagation multipath; the gray value of the dot indicates the magnitude of the received power.
As shown in fig. 6, the result of identifying short-term propagation multipath by applying the algorithm of step S2 is shown, where the X-axis represents the horizontal distance between the drone and the base station, and the Y-axis represents the time delay of propagation multipath; each point in the graph represents an instantaneous propagation multipath; the gray value of the dot indicates the magnitude of the received power; the curve represents the identified short-term propagation multipath and the gray value of the curve represents the average power of all instantaneous propagation multipaths in the short-term propagation multipath.
As shown in fig. 7, the result of identifying long-term propagation multipath by applying the algorithm of step S3 is shown, where the X axis represents the horizontal distance between the drone and the base station, and the Y axis represents the time delay of propagation multipath; each point in the graph represents an instantaneous propagation multipath; the gray value of the dot indicates the magnitude of the received power; the thin solid line represents the short-term propagation multipath identified by applying the algorithm of step S2; the thick solid line represents the long-term propagation multipath identified by applying the algorithm of step 3. For the sake of clarity and simplicity, only long-term propagation multipaths containing a number of short-term propagation multipaths greater than 1 are drawn here.
As shown in fig. 8, the comparison between the algorithm-identified propagation multi-path evolution result and the theoretical calculation result provided by the present invention is shown, wherein the X-axis represents the horizontal distance between the unmanned aerial vehicle and the base station, and the Y-axis represents the time delay of the propagation multi-path; each point in the graph represents an instantaneous propagation multipath; the gray value of the dot indicates the magnitude of the received power; the solid line represents the identified long-term propagation multipath; A. b, C, D four long-term propagation multipaths correspond to A, B, C, D four scatterers, respectively, in the measurement environment of FIG. 7; the dashed line represents the theoretical propagation multipath evolution curve obtained from the actual measured environmental data. It can be seen that the algorithm provided identifies that the propagation multipath evolution result is well matched with the theoretical calculation result, and can clearly show the phenomena of the long-term propagation multipath in the whole process caused by the approach and the distance to the scatterer.
Fig. 9 shows the result of the algorithm provided by the present invention identifying propagation multipath evolution in the doppler domain, wherein the X axis represents the horizontal distance between the drone and the base station, and the Y axis represents the doppler frequency of propagation multipath; each point in the graph represents an instantaneous propagation multipath; the gray value of the dot indicates the magnitude of the received power; the solid line represents the region where the identified long-term propagation multipath is active (alive), the dashed line represents the region where the identified long-term propagation multipath is inactive (dead), and A, B, C, D correspond to A, B, C, D four scatterers, respectively, in the measurement environment of fig. 7. Fig. 9 clearly shows the change of the doppler frequency of long-term propagation multipath caused by the approach and the distance of the unmanned aerial vehicle to the scatterer, which is consistent with the practical situation, and proves the effectiveness of the algorithm proposed by the present invention and the reasonability of the result.
In addition, it should be noted that the specific implementation examples described in this specification may have different names, and the above contents described in this specification are only illustrations of the structures of the present invention. All equivalent or simple changes in the structure, characteristics and principles of the invention are included in the protection scope of the invention. Various modifications or additions may be made to the described embodiments or methods may be similarly employed by those skilled in the art without departing from the scope of the invention as defined in the appending claims.

Claims (10)

1. A low-altitude air-ground unmanned aerial vehicle channel multipath tracking method based on a geometric prior model is characterized by comprising the following steps:
s1, acquiring instantaneous propagation multipath component parameters of communication signals between the base station and the unmanned aerial vehicle;
s2, according to the instantaneous propagation multipath component parameters, identifying the short-term propagation multipath component evolution through the short-term space consistency of the instantaneous propagation multipath components to obtain short-term propagation multipath;
s3, determining distance evaluation measurement according to the prior information of the short-term propagation multipath, judging the short-term propagation multipath belonging to the same blocked long-term propagation multipath according to the distance evaluation measurement, and combining the corresponding short-term propagation multipath to obtain the long-term propagation multipath.
2. The method of claim 1, wherein the types of paths of the instantaneous propagation multipath include a direct path and a scattered path.
3. The method for multipath tracking of the unmanned aerial vehicle channel in the low altitude space based on the geometric prior model is characterized in that the propagation time of the scattering path is the sum of the square root of a hyperbola and a constant part.
4. The method for low-altitude space-ground unmanned aerial vehicle channel multipath tracking based on the geometric prior model of claim 1, wherein the instantaneous propagation multipath component parameters comprise time delay, Doppler frequency and power.
5. The method for tracking the multipath of the channel of the unmanned aerial vehicle in the low air space based on the geometric prior model according to claim 4, wherein the instantaneous propagation multipath component is specifically a time-varying channel impulse response, and a calculation formula is as follows:
Figure FDA0002840109830000011
wherein t is a time variable, τ is a time delay, L is a total path number, αi,lFor the first path complex signal amplitude, vi,lAnd τi,lThe doppler frequency and the time delay corresponding to the ith path, respectively, and δ (·) is a dirac function.
6. The method for tracking the multipath of the channel of the unmanned aerial vehicle in the low air space based on the geometric prior model is characterized in that the instantaneous propagation multipath component parameters are calculated by a space iteration expectation maximization algorithm.
7. The method for tracking the multipath of the channel of the unmanned aerial vehicle in the low air space based on the geometric prior model according to claim 1, wherein the short-term spatial consistency of the instantaneous propagation multipath components is specifically a short-term distance measure between the instantaneous propagation multipath components, and is specifically as follows:
Figure FDA0002840109830000021
wherein DPt(u1,u2) Is a measure of the short-term distance between two instantaneous propagation multipaths, u1And u2The method is a four-dimensional vector representation of instantaneous propagation multipath, and specifically comprises the following steps:
Figure FDA0002840109830000022
Figure FDA0002840109830000023
wherein u is1Is the horizontal distance between the drone and the base station, u2,u3,u4Power, time delay and Doppler frequency of corresponding instantaneous propagation multipath, respectivelyTDenotes a transpose operation, t ═ t1,t2,t3,t4)TAnd the threshold values represent four preset parameters of horizontal distance, power, time delay and Doppler frequency between the base station and the unmanned aerial vehicle.
8. The geometric prior model-based low-altitude air-ground unmanned aerial vehicle channel multipath tracking method according to claim 7, wherein when none of the four parameter portions of the short-term distance metric exceeds 1, it is determined that two instantaneous propagation multipaths belong to a common short-term propagation multipath.
9. The method for tracking the multipath of the channel of the unmanned aerial vehicle in the low air space based on the geometric prior model as claimed in claim 1, wherein the distance estimation metric is based on non-overlapping short-term propagation multipath, and the non-overlapping decision process of the short-term propagation multipath is as follows:
Figure FDA0002840109830000024
OV(A,B)≤dOV
where A and B represent short-term propagation multipaths, OV (A, B) represents the number of instantaneous propagation multipaths containing the same number for both short-term propagation multipaths, dOVFor non-overlapping thresholds, the OVI is specifically:
Figure FDA0002840109830000025
when satisfying OV (A, B) less than or equal to dOVIt is determined that the two short-term propagation multipaths are non-overlapping.
10. The geometric prior model-based low-altitude air-ground unmanned aerial vehicle channel multipath tracking method according to claim 9, wherein the distance evaluation metric comprises a time delay difference evaluation metric and a distance ratio evaluation metric as follows:
Figure FDA0002840109830000026
Figure FDA0002840109830000031
where DH (S) is a delay-difference evaluation metric, DS (S, d)DS) Evaluating the metric for distance ratio, dDSIs a proportional threshold, S ═ S1,s2,…s|S|Is not heavyStacked short-term propagation multipath, L ═ L1,l2,…l|L|The sampling points on the rear connecting line are arranged according to the sequence that the horizontal distance between the base station and the unmanned aerial vehicle is increased, wherein the instantaneous propagation multipath is the non-overlapped short-term propagation multipath, and Q is (Q)1,q2,…q|Q|The long-term propagation multipath fitting path is satisfied with L and Q,
Figure FDA0002840109830000032
Figure FDA0002840109830000033
Figure FDA0002840109830000034
and
Figure FDA0002840109830000035
is the corresponding time delay;
the time delay difference evaluation metric is provided with a corresponding time delay difference threshold value dDHThe distance scale evaluation metric is provided with a corresponding distance scale threshold pDSAnd when the distance evaluation metrics of the two short-term propagation multi-paths are smaller than the time delay difference threshold and the distance ratio threshold at the same time, judging that the two short-term propagation multi-paths belong to the common long-term propagation multi-path.
CN202011488728.5A 2020-12-16 2020-12-16 Low-altitude air-ground unmanned aerial vehicle channel multipath tracking method based on geometric prior model Active CN112616132B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202011488728.5A CN112616132B (en) 2020-12-16 2020-12-16 Low-altitude air-ground unmanned aerial vehicle channel multipath tracking method based on geometric prior model

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202011488728.5A CN112616132B (en) 2020-12-16 2020-12-16 Low-altitude air-ground unmanned aerial vehicle channel multipath tracking method based on geometric prior model

Publications (2)

Publication Number Publication Date
CN112616132A true CN112616132A (en) 2021-04-06
CN112616132B CN112616132B (en) 2022-04-01

Family

ID=75239742

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202011488728.5A Active CN112616132B (en) 2020-12-16 2020-12-16 Low-altitude air-ground unmanned aerial vehicle channel multipath tracking method based on geometric prior model

Country Status (1)

Country Link
CN (1) CN112616132B (en)

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113489560A (en) * 2021-05-12 2021-10-08 东南大学 Geometric random modeling method for non-stationary air-ground MIMO channel of unmanned aerial vehicle
CN113567946A (en) * 2021-07-20 2021-10-29 同济大学 Real target and virtual image detection method for millimeter wave radar

Citations (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20150270885A1 (en) * 2015-06-08 2015-09-24 Donald C.D. Chang MIMO Systems with Active Scatters and their Performance Evaluation
CN107992069A (en) * 2017-11-29 2018-05-04 上海无线电设备研究所 A kind of Design of Guidance Law method of unmanned plane path following control
CN109194415A (en) * 2018-08-31 2019-01-11 北京遥测技术研究所 Unmanned Aerial Vehicle Data Link middle width strip ofdm signal angle-measuring method and angle measuring system
CN109412673A (en) * 2018-06-22 2019-03-01 南京航空航天大学 Geometry stochastic channel model and real-time emulation method towards UAV Communication
CN109932716A (en) * 2019-03-03 2019-06-25 中国人民解放军空军工程大学 A kind of low target micro-Doppler feature extracting method
US20190204123A1 (en) * 2018-01-03 2019-07-04 General Electric Company Systems and methods associated with unmanned aerial vehicle targeting accuracy
CN110515068A (en) * 2019-09-24 2019-11-29 中国人民解放军国防科技大学 Non-line-of-sight area moving target detection system based on multipath signals
US20200201316A1 (en) * 2018-12-21 2020-06-25 Airbus Defence and Space GmbH Method For Operating An Unmanned Aerial Vehicle As Well As An Unmanned Aerial Vehicle

Patent Citations (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20150270885A1 (en) * 2015-06-08 2015-09-24 Donald C.D. Chang MIMO Systems with Active Scatters and their Performance Evaluation
CN107992069A (en) * 2017-11-29 2018-05-04 上海无线电设备研究所 A kind of Design of Guidance Law method of unmanned plane path following control
US20190204123A1 (en) * 2018-01-03 2019-07-04 General Electric Company Systems and methods associated with unmanned aerial vehicle targeting accuracy
CN109412673A (en) * 2018-06-22 2019-03-01 南京航空航天大学 Geometry stochastic channel model and real-time emulation method towards UAV Communication
CN109194415A (en) * 2018-08-31 2019-01-11 北京遥测技术研究所 Unmanned Aerial Vehicle Data Link middle width strip ofdm signal angle-measuring method and angle measuring system
US20200201316A1 (en) * 2018-12-21 2020-06-25 Airbus Defence and Space GmbH Method For Operating An Unmanned Aerial Vehicle As Well As An Unmanned Aerial Vehicle
CN109932716A (en) * 2019-03-03 2019-06-25 中国人民解放军空军工程大学 A kind of low target micro-Doppler feature extracting method
CN110515068A (en) * 2019-09-24 2019-11-29 中国人民解放军国防科技大学 Non-line-of-sight area moving target detection system based on multipath signals

Non-Patent Citations (6)

* Cited by examiner, † Cited by third party
Title
DAVID W. MATOLAK: "Unmanned Aircraft Systems", 《IEE VEHICULAR TECHNOLOGY MAGAZINE》 *
JAEHWUI BAE: "Study on Air-to-Ground multipath channel and mobility influences in UAV based broadcasting", 《ICTC 2018》 *
JOSE RODR´ ´IGUEZ-PINEIRO: "Geometry-based MPC Tracking and Modeling Algorithm for Time-Varying UAV Channels", 《RESEARCHGATE》 *
XIANG CHENG: "A 3D Geometry-Based Stochastic Model for Unmanned Aerial Vehicle MIMO Ricean Fading Channels", 《IEEE INTERNET OF THINGS JOURNAL》 *
付晓旭: "无人机MIMO信道统计建模研究", 《信息科技辑》 *
贾汝冰: "无人机场景下的宽带信道建模与仿真", 《信息科技辑》 *

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113489560A (en) * 2021-05-12 2021-10-08 东南大学 Geometric random modeling method for non-stationary air-ground MIMO channel of unmanned aerial vehicle
CN113489560B (en) * 2021-05-12 2022-07-08 东南大学 Geometric random modeling method for non-stationary air-ground MIMO channel of unmanned aerial vehicle
CN113567946A (en) * 2021-07-20 2021-10-29 同济大学 Real target and virtual image detection method for millimeter wave radar
CN113567946B (en) * 2021-07-20 2023-08-29 同济大学 Method for detecting real target and virtual image of millimeter wave radar

Also Published As

Publication number Publication date
CN112616132B (en) 2022-04-01

Similar Documents

Publication Publication Date Title
CN112616132B (en) Low-altitude air-ground unmanned aerial vehicle channel multipath tracking method based on geometric prior model
US8878725B2 (en) System and method for geolocation of multiple unknown radio frequency signal sources
CN105137424B (en) Real beam scanning radar angle ultra-resolution method under a kind of clutter background
CN108717174B (en) Information theory-based passive cooperative positioning method for predicting rapid covariance interaction fusion
CN111954153B (en) Intelligent taxi calling navigation positioning method based on UWB positioning
CN110749891B (en) Self-adaptive underwater single beacon positioning method capable of estimating unknown effective sound velocity
CN110389327A (en) The more external illuminators-based radars of multistation are biradical away from localization method under receiving station's location error
KR101063318B1 (en) Position measurement system and method of mobile terminal
CN112162244B (en) Event trigger target tracking method under related noise and random packet loss environment
CN110389326A (en) The more external illuminators-based radar moving target localization methods of multistation under a kind of reception station error
CN109001700B (en) Radar foresight imaging method for realizing target contour reconstruction
Zhao et al. Particle filtering for positioning based on proximity reports
CN103852749A (en) Robust waveform optimization method for improving MIMO-STAP detection performance
CN108761384B (en) Target positioning method for robust sensor network
CN108322933B (en) Node accelerated gradient descent positioning method
CN112911502B (en) Base station longitude and latitude estimation method based on MDT technology and RSRP ranging
CN113825236A (en) Method for integrating sensing, computing and communication in wireless network
CN108549079A (en) Triantennary interference delay Doppler radar altimeter landform spy shows independent positioning method
CN111505573A (en) Track generation method and device of distributed positioning system
CN113654554B (en) Fast self-adaptive dynamic inertial navigation real-time resolving denoising method
CN116131981A (en) Air-ground channel modeling simulation method integrating unmanned aerial vehicle characteristics
CN106443623B (en) A kind of sky-wave OTH radar target and Ionospheric Parameters combined estimation method
Fink et al. Refinement of weighted centroid localization using a regular infrastructure topology
CN113189585A (en) Motion error compensation algorithm based on unmanned aerial vehicle bistatic SAR system
Kilpatrick et al. Sidelobe suppression and super resolution for MIMO imaging radar

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant