CN115691225A - Unmanned aerial vehicle path planning method based on orthogonal time-frequency-space modulation - Google Patents
Unmanned aerial vehicle path planning method based on orthogonal time-frequency-space modulation Download PDFInfo
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Abstract
The invention discloses an unmanned aerial vehicle path planning method based on orthogonal time-frequency-space modulation, which belongs to the technical field of wireless communication and comprises the following steps: s1: establishing a mathematical model of the energy consumption of the unmanned aerial vehicle; s2: establishing a mathematical model of the communication system of the unmanned aerial vehicle and the ground base station, and calculating the corresponding receiving signal states of the base station under different parameters; s3: establishing a mathematical model of the flight distance required by the unmanned aerial vehicle to traverse the ground base station, and searching the sequence of the shortest path of the unmanned aerial vehicle traversing each signal point; s4: defining relevant limiting conditions and establishing a corresponding mathematical model; s5: and determining a mathematical model of the flight distance of the unmanned aerial vehicle according to the traversal sequence and the limiting condition model of the unmanned aerial vehicle and the base station communication system, and searching the sequence of the unmanned aerial vehicle with the shortest path between signal points according to the Sequence Quadratic Programming (SQP) algorithm.
Description
Technical Field
The invention belongs to the technical field of wireless communication, and particularly relates to an unmanned aerial vehicle path planning method based on orthogonal time-frequency-space modulation.
Background
According to the existing research results, when mobile communication is performed, the Orthogonal Frequency Division Multiplexing (OFDM) modulation method is affected by the doppler effect, so that orthogonality among subcarriers is lost, and communication performance degradation phenomena such as increase of signal-to-noise ratio (SNR) required for achieving the same Bit Error Rate (BER) occur.
At present, an Orthogonal Frequency Division Multiplexing (OFDM) multi-carrier modulation scheme is mainly adopted for unmanned aerial vehicle communication, and communication data streams are dispersed onto multiple orthogonal subcarriers to overcome intersymbol interference caused by channel multipath. However, the drone is usually in a moving state when communicating, and the relative ground speed can reach 120 kilometers per hour, which causes the communication link to suffer from serious doppler shift. Therefore, the unmanned aerial vehicle has rapid time-varying property and frequency selectivity relative to a wireless channel of a ground base station, and the OFDM modulation method cannot adapt to the situation, so that serious inter-carrier crosstalk will be encountered, meanwhile, the channel estimation cost is very high, and the reliability of a communication link is severely limited.
Disclosure of Invention
In view of the above, the present invention provides an unmanned aerial vehicle path planning method based on orthogonal time-frequency space modulation, which optimizes unmanned aerial vehicle trajectory planning with minimum energy consumption as a target under the constraints of a bit error rate threshold and a maximum transmission rate under the premise of considering doppler compensation.
In order to achieve the purpose, the invention provides the following technical scheme:
an unmanned aerial vehicle path planning method based on orthogonal time-frequency space modulation comprises the following steps:
s1: establishing a mathematical model of the energy consumption of the unmanned aerial vehicle according to the flight and state parameters of the unmanned aerial vehicle;
s2: establishing a mathematical model of the unmanned aerial vehicle and a ground base station communication system according to the unmanned aerial vehicle channel state parameters, and calculating the receiving signal states corresponding to the base stations under different parameters;
s3: establishing a mathematical model of the flight distance required by the unmanned aerial vehicle to traverse the ground base station according to the number and the position parameters of the base stations, and searching the sequence of the shortest distance between signal points traversed by the unmanned aerial vehicle according to an ant colony algorithm;
s4: according to the requirements of the unmanned aerial vehicle and a base station communication system, relevant limiting conditions are determined and a corresponding mathematical model is established;
s5: and determining a mathematical model of the flight distance of the unmanned aerial vehicle according to the traversal sequence and the limiting condition model of the unmanned aerial vehicle and the base station communication system, and searching the sequence of the shortest path of the unmanned aerial vehicle between the signal points according to the Sequence Quadratic Programming (SQP) algorithm.
Further, in step S1, the mathematical model of the energy consumption of the unmanned aerial vehicle is:
in the formula, E is the total energy consumption of the unmanned aerial vehicle, E fly Representing the flight energy consumption of the unmanned aerial vehicle, E trans Denotes unmanned aerial vehicle communication energy consumption, κ 1 、κ 2 Constants representing parameters of the drone, v represents the speed of the drone, d (τ) n ) In time slot tau n The moving distance of the medium unmanned aerial vehicle, T is the total moving time of the unmanned aerial vehicle, k 3 Constant determined by the speed of the drone and other parameters, d all Is the total distance of movement of the drone.
Further, in step S2, the mathematical model of the communication system between the unmanned aerial vehicle and the ground base station is as follows:
in the formula, x [ k, l]，y[k，l]Respectively, the transmit signal and the receive signal of the OTFS system, X [ n, m ]]From x [ k, l]IFFFT to obtain, Y [ n, m ]]From y [ k, l]The signal obtained by FFFT conversion is equivalent to a transmitting signal and a receiving signal of an OFDM system, s (t) and r (t) are equivalent to an actual transmitting signal and an actual receiving signal, wherein h (tau, v) is a channel function,
representing a noise function.
Further, the step S3 specifically includes the following steps:
let P 0 Setting P for the starting point and the end point of the flight path of the unmanned aerial vehicle i Is the ith AP coordinate, where i ∈ [1, Q)]，P i And j (th)The distance between the difference points of the APs is: d ij ＝||P i P j And | l, the position transition matrix between two points is represented as P:
wherein J k (i) Indicating that the unmanned aerial vehicle has not visited yet in the traversal and can go from P i The AP set directly reached by the point, eta (i, j) is a heuristic algorithm, and eta (i, j) =1/D ij Calculating, τ (i, j) represents P i And P j Amount of pheromone on the inter-path:
wherein (C) k ) -1 Indicating that the drone is at P i The length of the path that has been experienced.
Further, the step S4 specifically includes the following steps:
s41: based on the BER threshold and the Doppler velocity V of the received signal doppler And solving the maximum range of the communication between the unmanned aerial vehicle and each signal source according to the corresponding SNR value:
wherein P is source Representing drone signal power, P range Represents the SNR value corresponding to the BER threshold, where 0 Representing a gain coefficient, wherein alpha is a distance loss coefficient, and G is a Rayleigh fading channel function;
s42: the unmanned plane will finish the signal point range of M Data The communication volume of (2) divides the unmanned aerial vehicle track into two parts of a signal range and a signal range, and sets the two partsIn the ith signal range, the corresponding coordinate is P in (i, s) where i ∈ [1, Q ]]，s∈[1，S]Namely, the following conditions are satisfied:
M i ≥M Data
wherein M is i For the total traffic in the ith signal range, the transmission rate follows the restriction of shannon's equation, which is calculated as follows:
C(d)＝W*log 2 (1+P d )，
where d (i, s) represents the drone coordinate P in (i, s) distance from the ith AP, W representing the channel bandwidth;
s43: considering the constraint of Doppler effect, let θ i，s Indicating the direction of motion of the drone and P in (i, s) and P i Angle between the connecting lines of two points, theta i，s Satisfy the requirement of
Vθ i，s ≤V doppler ，
θ i，s ≤θ 0 ，
Wherein theta is 0 ＝arccos(V doppler /V)，θ i，s The calculation formula is as follows:
unmanned aerial vehicle has minimum flying height h min Is provided with Z i，s Representing unmanned aerial vehicle track point P i，s A height coordinate which satisfies:
Z i，s ≥h min 。
further, the step S5 specifically includes the following steps:
s51: calculating the total distance between the unmanned aerial vehicle in the signal range and outside the signal range:
L total ＝sum(L out ，L in) ，
wherein L is out The track distance of the unmanned aerial vehicle is shown outside the communication range, and the calculation formula is as follows:
L in the track distance of the unmanned aerial vehicle in the communication range is represented, and the calculation formula is as follows:
s52: and (3) synthesizing the limiting conditions to obtain an unmanned aerial vehicle track optimization equation:
min L total ＝sum(L out ，L in )
s.t.d i，s ≤d range ，
θ i，s ≥θ 0 ，
Z i，s ≥h min ，
and solving the shortest moving distance of the unmanned aerial vehicle according to the SQP algorithm, so that the energy consumption of the unmanned aerial vehicle is minimum.
The invention has the beneficial effects that: the method can complete the transmission task of traversing each base station within a shorter flight distance, thereby enabling the energy consumption of the unmanned aerial vehicle to be lower.
Additional advantages, objects, and features of the invention will be set forth in the description which follows and in part will become apparent to those having ordinary skill in the art upon examination of the following or may be learned from practice of the invention. The objectives and other advantages of the invention may be realized and attained by the means of the instrumentalities and combinations particularly pointed out hereinafter.
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In order to make the object, technical scheme and beneficial effect of the invention more clear, the invention provides the following drawings for explanation:
fig. 1 is a flowchart of an unmanned aerial vehicle path planning method based on orthogonal time-frequency-space modulation according to the present invention.
Detailed Description
As shown in fig. 1, the present invention provides an unmanned aerial vehicle path planning method based on orthogonal time-frequency-space modulation, which includes the following steps:
s1: establishing a mathematical model of the energy consumption of the unmanned aerial vehicle according to the flight and state parameters of the unmanned aerial vehicle:
in the formula, E is the total energy consumption of the unmanned aerial vehicle, E fly Representing the flight energy consumption of the unmanned aerial vehicle, E trans Denotes unmanned aerial vehicle communication energy consumption, κ 1 、κ 2 Constants representing parameters of the drone, v represents the speed of the drone, d (τ) n ) In time slot tau n The moving distance of the medium unmanned plane, T is the total moving time of the unmanned plane, k 3 As a constant determined by the speed of the drone and other parameters, d all Is an unmanned planeThe total distance traveled;
s2: establishing a mathematical model of the unmanned aerial vehicle and a ground base station communication system according to the unmanned aerial vehicle channel state parameters, and calculating the receiving signal states corresponding to the base stations under different parameters:
in the formula, x [ k, l]，y[k，l]Respectively, the transmit signal and the receive signal of the OTFS system, X [ n, m ]]From x [ k, l]IFFFT to obtain, Y [ n, m ]]From y [ k, l]The signal obtained by FFFT conversion is equivalent to the transmitting signal and the receiving signal of an OFDM system, s (t) and r (t) are equivalent to the actual transmitting signal and the actual receiving signal, wherein h (tau, v) is a channel function,
representing a noise function;
s3: establishing a mathematical model of the flight distance required by the unmanned aerial vehicle to traverse the ground base station according to the number and the position parameters of the base stations, and searching the sequence of the shortest distance between signal points traversed by the unmanned aerial vehicle according to an ant colony algorithm;
let P 0 Setting P for the starting point and the end point of the flight path of the unmanned aerial vehicle i Is the ith AP coordinate, where i ∈ [1,Q ]]，P i The distance between the difference point and the jth AP is: d ij ＝||P i P j And | l, the position transition matrix between two points is represented as P:
wherein J k (i) Indicating that the unmanned aerial vehicle has not visited yet in the traversal and can go from P i The set of APs to which the point is directly connected, η (i, j) is a heuristic algorithm, and η (i, j) =1/D ij Calculating, τ (i, j) represents P i And P j Amount of pheromone on the inter-path:
wherein (C) k ) -1 Indicates that the drone is in P i The length of the path that has been traversed;
s4: according to the requirements of the unmanned aerial vehicle and a base station communication system, relevant limiting conditions are determined and a corresponding mathematical model is established;
s41: based on the threshold of BER and Doppler velocity V of the received signal doppler And solving the maximum range of the communication between the unmanned aerial vehicle and each signal source according to the corresponding SNR value:
wherein P is source Representing the signal power of the drone, P range Represents the SNR value corresponding to the BER threshold, where 0 Representing a gain coefficient, wherein alpha is a distance loss coefficient, and G is a Rayleigh fading channel function;
s42: the unmanned plane will finish the signal points within the range of each signal point with the size of M Data The unmanned aerial vehicle track is divided into two parts, namely a signal range and a signal range, and the unmanned aerial vehicle track passes through S points in the ith signal range, and the corresponding coordinate is P in (i, s) where i ∈ [1, Q)]，s∈[1，S]Namely, the following conditions are satisfied:
M i ≥M Data
wherein M is i For the total traffic in the ith signal range, the transmission rate follows the restriction of shannon's equation, which is calculated as follows:
C(d)＝W*log 2 (1+P d )，
where d (i, s) represents the drone coordinate P in (i, s) distance from the ith AP, W representing the channel bandwidth;
s43: considering the constraint of Doppler effect, let θ i，s Indicating the direction of motion of the drone and P in (i, s) and P i Angle between the connecting lines of two points, theta i，s Satisfy the requirement of
Vθ i，s ≤V doppler ，
θ i，s ≤θ 0 ，
Wherein theta is 0 ＝arccos(V doppler /V)，θ i，s The calculation formula is as follows:
unmanned aerial vehicle has minimum flying height h min Is provided with Z i，s Representing unmanned aerial vehicle track point P i，s A height coordinate satisfying:
Z i，s ≥h min
s5: according to the traversal sequence and the limiting condition model of the unmanned aerial vehicle and the base station communication system, a mathematical model of the flight distance of the unmanned aerial vehicle is determined, and the sequence of the shortest path of the unmanned aerial vehicle between signal points is searched according to a Sequence Quadratic Programming (SQP) algorithm;
s51: calculating the total distance between the unmanned aerial vehicle in the signal range and the unmanned aerial vehicle outside the signal range:
L total ＝sum(L out ，L in )，
wherein L is out The track distance of the unmanned aerial vehicle is shown outside the communication range, and the calculation formula is as follows:
L in the track distance of the unmanned aerial vehicle in the communication range is represented, and the calculation formula is as follows:
s52: and (3) synthesizing the limiting conditions to obtain an unmanned aerial vehicle track optimization equation:
min L total ＝sum(L out ，L in )
s.t.d i，s ≤d range ，
θ i，s ≥θ 0 ，
Z i，s ≥h min ，
and solving the shortest moving distance of the unmanned aerial vehicle according to the SQP algorithm, so that the energy consumption of the unmanned aerial vehicle is minimum.
Finally, it is noted that the above-mentioned preferred embodiments illustrate rather than limit the invention, and that, although the invention has been described in detail with reference to the above-mentioned preferred embodiments, it will be understood by those skilled in the art that various changes in form and detail may be made therein without departing from the scope of the invention as defined by the appended claims.
Claims (6)
1. An unmanned aerial vehicle path planning method based on orthogonal time-frequency space modulation is characterized in that: the method comprises the following steps:
s1: establishing a mathematical model of the energy consumption of the unmanned aerial vehicle according to the flight and state parameters of the unmanned aerial vehicle;
s2: establishing a mathematical model of the unmanned aerial vehicle and a ground base station communication system according to the unmanned aerial vehicle channel state parameters, and calculating the receiving signal states corresponding to the base stations under different parameters;
s3: establishing a mathematical model of the flight distance required by the unmanned aerial vehicle to traverse the ground base station according to the number and the position parameters of the base stations, and searching the sequence of the shortest distance between signal points traversed by the unmanned aerial vehicle according to an ant colony algorithm;
s4: according to the requirements of the unmanned aerial vehicle and a base station communication system, relevant limiting conditions are determined and a corresponding mathematical model is established;
s5: and determining a mathematical model of the flight distance of the unmanned aerial vehicle according to the traversal sequence and the limiting condition model of the unmanned aerial vehicle and the base station communication system, and searching the sequence of the shortest path of the unmanned aerial vehicle between the signal points according to the Sequence Quadratic Programming (SQP) algorithm.
2. The unmanned aerial vehicle path planning method based on orthogonal time-frequency-space modulation according to claim 1, wherein: s1, the energy consumption mathematical model of the unmanned aerial vehicle is as follows:
in the formula, E is the total energy consumption of the unmanned aerial vehicle, E fly Representing the flight energy consumption of the unmanned aerial vehicle, E trans Denotes unmanned aerial vehicle communication energy consumption, κ 1 、κ 2 Constants representing parameters of the drone, v represents the speed of the drone, d (τ) n ) In time slot tau n The moving distance of the medium unmanned plane, T is the total movement of the unmanned planeTime, κ 3 Constant determined by the speed of the drone and other parameters, d all Is the total distance of travel of the drone.
3. The unmanned aerial vehicle path planning method based on orthogonal time-frequency-space modulation according to claim 2, wherein: in the step S2, the mathematical model of the communication system between the unmanned aerial vehicle and the ground base station is as follows:
in the formula, x [ k, l]，y[k，l]Respectively, the transmit signal and the receive signal of the OTFS system, X [ n, m ]]From x [ k, l]IFFFT to obtain, Y [ n, m ]]From y [ k, l]The signal obtained by FFFT conversion is equivalent to the transmitting signal and the receiving signal of an OFDM system, s (t) and r (t) are equivalent to the actual transmitting signal and the actual receiving signal, wherein h (tau, v) is a channel function,
representing a noise function.
4. The method of claim 3, wherein the method comprises: the step S3 specifically includes the following steps:
let P 0 Setting P for the starting point and the end point of the flight path of the unmanned aerial vehicle i Is the ith AP coordinate, where i ∈ [1,Q ]]，P i The distance between the difference point and the jth AP is: d ij ＝||P i P j And | l, the position transition matrix between two points is represented as P:
wherein J k (i) Indicating that the unmanned aerial vehicle has not visited yet in the traversal and can go from P i Set of APs to which points are directly directed, η (i, j) is a heuristic algorithm, using η (j)i，j)＝1/D ij Calculating, τ (i, j) represents P i And P j Amount of pheromones on the inter-path:
wherein (C) k ) -1 Indicates that the drone is in P i The length of the path that has been experienced.
5. The method of claim 4, wherein the method comprises: the step S4 specifically includes the following steps:
s41: based on the threshold of BER and Doppler velocity V of the received signal doppler And solving the maximum range of the communication between the unmanned aerial vehicle and each signal source according to the corresponding SNR value:
wherein P is source Representing the signal power of the drone, P range Represents the SNR value corresponding to the BER threshold, where 0 Representing a gain coefficient, wherein alpha is a distance loss coefficient, and G is a Rayleigh fading channel function;
s42: the unmanned plane will finish the signal points within the range of each signal point with the size of M Data The unmanned aerial vehicle track is divided into two parts, namely a signal range and a signal range, and the unmanned aerial vehicle track passes through S points in the ith signal range, and the corresponding coordinate is P in (i, s) where i ∈ [1, Q)]，s∈[1，S]Namely, the following conditions are satisfied:
M i ≥M Data
wherein M is i For the total traffic in the ith signal range, the transmission rate follows the limitation of Shannon's formulaThe calculation formula is as follows:
C(d)＝W*log 2 (1+P d )，
where d (i, s) represents the drone coordinate P in (i, s) distance from ith AP, W represents channel bandwidth;
s43: considering the constraint of Doppler effect, let θ i，s Indicating the direction of motion of the drone and P in (i, s) and P i Angle between the connecting lines of two points, theta i，s Satisfy the requirement of
Vθ i，s ≤V doppler ，
θ i，s ≤θ 0 ，
Wherein theta is 0 ＝arccos(V doppler /V)，θ i，s The calculation formula is as follows:
unmanned aerial vehicle has minimum flying height h min Is provided with Z i，s Show unmanned aerial vehicle track point P i，s A height coordinate satisfying:
Z i，s ≥h min 。
6. the method of claim 5, wherein the method comprises: the step S5 specifically includes the following steps:
s51: calculating the total distance between the unmanned aerial vehicle in the signal range and outside the signal range:
L total ＝sum(L out ，L in )，
wherein L is out The track distance of the unmanned aerial vehicle is shown outside the communication range, and the calculation formula is as follows:
L in the track distance of the unmanned aerial vehicle in the communication range is represented, and the calculation formula is as follows:
s52: and (3) synthesizing the limiting conditions to obtain an unmanned aerial vehicle track optimization equation:
min L total ＝sum(L out ，L in )
and solving the shortest moving distance of the unmanned aerial vehicle according to the SQP algorithm, so that the energy consumption of the unmanned aerial vehicle is minimum.
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