DESIGN METHOD OF HIGH ENERGY EFFICIENCY UNMANNED AERIAL VEHICLE (UAV) GREEN DATA ACQUISITION SYSTEM

Abstract

A design method of a high energy efficiency unmanned aerial vehicle (UAV) green data acquisition system belongs to the technical field of data acquisition and optimization for UAV uplink communication. Firstly, a system optimization objective is constructed; and in a uplink communication network of a single UAV and ground sensors, the UAV receives data periodically. Secondly, according to a constructed optimization problem, the optimization objective is maximization of EE({W},{t},{S}). Finally, an original problem is decomposed into two approximate concave-convex fractional sub-problem based on a block coordinate descent method and a successive convex approximation technique to obtain a suboptimal solution; an overall iterative algorithm is proposed: in each iteration, by solving the sub-problems, wake-up scheduling S, time slot t and UAV trajectory W are alternately optimized. The solution obtained in each iteration is used as the input of next iteration. The present invention can jointly optimize the UAV flight trajectory, the sensor wake-up scheduling and the flight time slot to ensure that the transmission information amount and energy consumption of the sensors satisfy system requirements, while maximizing the energy efficiency of the system.

Claims

1. A design method of high energy efficiency unmanned aerial vehicle (UAV) green data acquisition system, comprising the following steps: step 1, constructing a system optimization objective: (1) serving a set of I ground sensors which are randomly distributed through time division multiple access (TDMA) by an unmanned aerial vehicle (UAV); (2) flying, by the UAV, at a fixed altitude H with a maximum flight speed of V.sub.m and a total cycle of T, and discretizing the cycle T into N time slots by a time discretization method, with the length of each time slot $t=\frac{T}{N};$ of the coordinate of the UAV is w[n]=[x(n),y(n)].sup.T ∈R.sup.2×1 in time slot n, wherein x(n), y(n) are the x-coordinate and y-coordinate of the UAV respectively, and R.sup.2×1 is a two-dimensional vector space; for a sensor set SI={1, 2, . . . , I} of random distribution, the coordinate of sensor i is fixed as L.sub.i=[x.sub.i,y.sub.i].sup.T∈R.sup.2×1, i∈SI, each sensor supports the energy of E.sub.i,i∈SI and the data amount to be transmitted is B.sub.i,i∈SI; if communication between the UAV and ground is line of sight (LoS) link communication, channel quality only depends on a distance between the UAV and the sensor, and power gain in unit reference distance is expressed as ρ.sub.0, then the channel power gain h.sup.i[n] of the sensor i in the time slot n conforms to a free space path loss model, i.e., $h^i\left[n\right]={\rho }_0{d^i\left[n\right]}^{-2}=\frac{{\rho }_0}{H^2+{.Math.w\left[n\right]-L_i.Math.}^2},\forall i\in \mathrm{SI},$ d.sup.i[n] is the distance between the UAV and the sensor i in the three-dimensional space; (3) assuming that the UAV serves only one sensor in one time slot, and defining a binary variable S.sub.i[n]∈{0,1} to represent wake-up scheduling of the sensor; S.sub.i[n]=1 indicates that the UAV establishes communication with the sensor i in the time slot n; S.sub.i[n]=0 indicates that the UAV does not establish communication with the sensor i in the time slot n; then, the information transmission rate R.sub.u.sup.i[n] between the UAV and the sensor i in the time slot n is expressed as: $\begin{array}{cc}{R}_u^i\left[n\right]=S_i\left[n\right]{\log }_2\left(1+\frac{P_A{\rho }_0}{\left(H^2+{.Math.w\left[n\right]-L_i.Math.}^2\right){\sigma }^2}\right),\forall i\in & \left(1\right)\end{array}$ wherein σ.sup.2 is additive white gaussian noise (AWGN) at a receiving end of the UAV, and P.sub.A is the transmission power of a ground sensor during communication; the total information amount R(bits/Hz) transmitted in one cycle (N time slots) of serving of the UAV is expressed as: $\begin{array}{cc}\overline{R}\left(\left\{W\right\},\left\{t\right\},\left\{S\right\}\right)=\underset{n=1}{\overset{N}{.Math.}}\underset{i=1}{\overset{I}{.Math.}}{R}_u^i[\text{?}& \left(2\right)\end{array}$ $\text{?}\text{indicates text missing or illegible when filed}$ for a rotary-wing UAV, when parameters are constant, the propulsion power P(V) of the UAV is mainly related to flight speed V; the propulsion power is composed of three parts: blade profile power, parasite power and induced power, expressed as: $\begin{array}{cc}P\left(V\right)=\text{?}+\text{?}+\text{?}& \left(3\right)\end{array}$ $\text{?}\text{indicates text missing or illegible when filed}$ the speed of the time slot n is approximately expressed as $\text{?},$ $\text{?}\text{indicates text missing or illegible when filed}$ and Δ.sub.n defined as the flight distance of the time slot n; then the propulsion power P.sub.prop[n] of the time slot n is approximated expressed by the following formula: $\begin{array}{cc}{P}_{\mathrm{prop}}\left[n\right]=P_0\left(1+\frac{3{\Delta }_n^2}{{\Omega }^2{r}^2{t}^2}\right)+\frac{1}{2}{d}_0\rho \mathrm{sA}\frac{{\Delta }_n^3}{t^3}+P_i(\sqrt{1+\frac{{\Delta }_n^4}{4v_0^4{t}^4}}-\frac{{\Delta }_n^2}{2v_0^{2}t}\text{?}& \left(4\right)\end{array}$ $\text{?}\text{indicates text missing or illegible when filed}$ in the formula, P.sub.0 and P.sub.i are the blade profile power and the induced power respectively in a hovering state; Ω is blade angular velocity; r is rotor radius; d.sub.0 represents fuselage drag ratio; ρ is air density; s is rotor solidity; A is rotor disc area; v.sub.0 is mean rotor induced velocity; the above parameters are constants; the total propulsion energy E consumed by the UAV in one cycle of serving is expressed as: $\begin{array}{cc}E\left(\left\{W\right\},\left\{t\right\}\right)=\underset{n=1}{\overset{N}{.Math.}}{P}_{\mathrm{prop}}\left[n\right]t& \left(5\right)\end{array}$ according to the definition of energy efficiency, the system optimization objective is represented as: $\begin{array}{cc}\mathrm{EE}\left(\left\{W\right\},\left\{t\right\},\left\{S\right\}\right)=\frac{\overline{R}\left(\left\{W\right\},\left\{t\right\},\left\{S\right\}\right)}{E\left(\left\{W\right\},\left\{t\right\}\right)}=\text{?}& \left(6\right)\end{array}$ $\text{?}\text{indicates text missing or illegible when filed}$ step 2, constructing an optimization problem according to the energy efficiency formula in step 1, wherein an optimization objective is maximization of EE({W},{t},{S}), and constraints comprise UAV trajectory constraints, sensor wake-up scheduling constraints, sensor energy constraint and data amount constraint to construct the following optimization problem: $\begin{array}{cc}\underset{S,W,t}{\max }\mathrm{EE}\left(\left\{W\right\},\left\{t\right\},\left\{S\right\}\right)& \left(7a\right)\end{array}$ $\begin{array}{cc}s.t.w\left[1\right]=w\left[N\right]& \left(7b\right)\end{array}$ $\begin{array}{cc}{.Math.w\left[n+1\right]-w\left[n\right].Math.}^2\le \gamma H^2,n=1,.Math.N\text{?}& \left(7c\right)\end{array}$ $\begin{array}{cc}.Math.w\left[n+1\right]-w\left[n\right].Math.\le V_{m}t,n=1,.Math.N-\text{?}& \left(7d\right)\end{array}$ $\begin{array}{cc}\underset{i=1}{\overset{I}{.Math.}}{S}_i\left[n\right]=1& \left(7e\right)\end{array}$ $\begin{array}{cc}{S}_i\left[n\right]\in \left\{0,1\right\},\forall i\in \mathrm{SI},\forall n& \left(7f\right)\end{array}$ $\begin{array}{cc}\underset{n=1}{\overset{N}{.Math.}}\left(R_u^i\left[n\right]t\right)\ge B_i,\forall i\in \mathrm{SI}& \left(7g\right)\end{array}$ $\begin{array}{cc}\underset{n=1}{\overset{N}{.Math.}}\left(S_i\left[n\right]P_{A}t\right)\ge E_i,\forall i\in \mathrm{SI}& \left(7h\right)\end{array}$ $\text{?}\text{indicates text missing or illegible when filed}$ in the above optimization problem, formulas (7b)-7(d) are trajectory constraints, V.sub.m is the maximum speed of the UAV and the UAV returns to an initial position after flying by a cycle; formulas (7e) and (7f) are the sensor wake-up scheduling constraints; formula (7g) is the data amount constraint of the sensor and B.sub.i is the data amount to be transmitted by the sensor i; formula (7h) is the sensor energy constraint and E.sub.i is maximum energy supported by the sensor i in each cycle; step 3, decomposing an original problem (7) into two sub-problems according to a block coordinate descent method; for the two sub-problems, approximately converting two non-convex problems into two convex optimization problems and calculating the problems by a successive convex approximation technique, as follows: (1) optimization sub-problem of wake-up scheduling S and time slot t fixing UAV trajectory W so that the sub-problem is the non-convex optimization problem of wake-up scheduling S and time slot t; firstly, for a binary variable S, slacking S to a continuous variable within a range [0,1]; then, introducing an auxiliary variable z[n] to satisfy ${z\left[n\right]}^2=\sqrt{t^4+\frac{{\Delta }_n^4}{4v_0^4}}-\frac{{\Delta }_n^2}{2v_0^2},\forall n,i.e.,t^4={z\left[n\right]}^4+\frac{{\Delta }_n^2}{v_0^2}{z\left[n\right]}^2,\forall n;$ using z[n] to replace the third term of the propulsion power P.sub.prop[n] in formula (4) to obtain the UAV propulsion power P.sub.prop.sup.A[n] under the sub-problem; introducing an auxiliary variable R_t[i] to satisfy ${\mathrm{R\_t}\left[i\right]}^2=\underset{n=1}{\overset{N}{.Math.}}{R}_u^i\left[n\right]t,\forall i;$ after introducing the auxiliary variables, applying the successive convex approximation technique for non-convex constraints, converting hyperbolic constraints into SOCP and approximating the original non-convex sub-problem as a convex problem, expressed as: $\begin{array}{cc}\underset{\underset{\underset{\mathrm{R\_t}\left[i\right].}{z\left[n\right],}}{S,W,t}}{\max }\frac{\underset{i=1}{\overset{I}{.Math.}}{\mathrm{R\_t}}_{\mathrm{lb}}\left[i\right]}{\underset{n=1}{\overset{N}{.Math.}}{P}_{\mathrm{prop}}^A\left[n\right]t}& \left(8a\right)\end{array}$ $\begin{array}{cc}s.t..Math.w\left[n+1\right]-w\left[n\right].Math.\le V_{m}t,n=1,.Math.N-1& \left(8b\right)\end{array}$ $\begin{array}{cc}\underset{i=1}{\overset{I}{.Math.}}{S}_i\left[n\right]\le 1,\forall n& \left(8c\right)\end{array}$ $\begin{array}{cc}0\le S_i\left[n\right]\le 1,\forall i,\forall n& \left(8d\right)\end{array}$ $\begin{array}{cc}{\mathrm{R\_t}}_{\mathrm{lb}}\left[i\right]\ge B_i\forall i& \left(8e\right)\end{array}$ $\begin{array}{cc}\underset{n=1}{\overset{N}{.Math.}}\left(S_i\left[n\right]P_A\right)\le {E_i\left(\frac{1}{t}\right)}_{\mathrm{lb}},\forall i& \left(8f\right)\end{array}$ $\begin{array}{cc}{t}^4\le {z^{\left(r\right)}\left[n\right]}^4+4{z^{\left(r\right)}\left[n\right]}^3\left(z\left[n\right]-z^{\left(r\right)}\left[n\right]\right)+\frac{{\Delta }_n^2}{v_0^2}\left({z^{\left(r\right)}\left[n\right]}^2+2z^{\left(r\right)}\left[n\right]\left(z\left[n\right]-z^{\left(r\right)}\left[n\right]\right)\right),& \left(8g\right)\end{array}$ $\begin{array}{cc}.Math.{\left[2\mathrm{R\_t}\left[i\right],\underset{n=1}{\overset{N}{.Math.}}{R}_u^i\left[n\right]-t\right]}^{†}.Math.\le \underset{n=1}{\overset{N}{.Math.}}{R}_u^i\left[n\right]+t,\forall i& \left(8h\right)\end{array}$ in the sub-problem (8), P.sub.prop.sup.A[n] is the propulsion power after the auxiliary variable z[n] is introduced, and is a convex function of t and z[n]; R_t.sub.lb[i] is the lower bound of first-order taylor expansion of the auxiliary variable R_t[i].sup.2, and is a linear function of $\mathrm{R\_t}\left[i\right];{\left(\frac{1}{t}\right)}_{\mathrm{lb}}$ is the lower bound of first-order taylor expansion of $\frac{1}{t},$ and has a linear relationship with t; the constraints of the sub-problem (8) are convex constraints; the optimization objective (8a) is a standard concave-convex fractional programming problem with concave numerator over convex denominator; because the constraint range is reduced by the successive convex approximation technique, the optimal solution of the convex problem after approximation is the lower bound of the optimal solution of an original sub-problem; (2) optimization sub-problem of UAV trajectory W fixing wake-up scheduling S and time slot t so that the sub-problem is a non-convex optimization problem of the UAV trajectory W; introducing an auxiliary variable y[n] to satisfy ${y\left[n\right]}^2=\sqrt{t^4+\frac{{\Delta }_n^4}{4v_0^4}}-\frac{{\Delta }_n^2}{2v_0^2},\forall n,i.e.,\frac{t^4}{{y\left[n\right]}^2}={y\left[n\right]}^2+\frac{{\Delta }_n^2}{v_0^2},\forall n;$ using y[n] to replace the third term of the propulsion power P.sub.prop[n] in formula (4) to obtain the UAV propulsion power P.sub.prop.sup.B[n] under the sub-problem; after introducing the auxiliary variables, applying the successive convex approximation technique for non-convex constraints, and approximating the original non-convex sub-problem as a convex problem, expressed as: $\begin{array}{cc}\underset{W,y\left[n\right]}{\max }\frac{\underset{n=1}{\overset{N}{.Math.}}\underset{i=1}{\overset{I}{.Math.}}{R}_u^{i,\mathrm{lb}}\left[n\right]t}{\underset{n=1}{\overset{N}{.Math.}}{P}_{\mathrm{prop}}^B\left[n\right]t}& \left(9a\right)\end{array}$ $\begin{array}{cc}s.t.w\left[1\right]=w\left[N\right]& \left(9b\right)\end{array}$ $\begin{array}{cc}{.Math.w\left[n+1\right]-w\left[n\right].Math.}^2\le \gamma H^2,n=1,.Math.N-\text{?}& \left(9c\right)\end{array}$ $\begin{array}{cc}.Math.w\left[n+1\right]-w\left[n\right].Math.\le V_{m}t,n=1,.Math.N-1& \left(9d\right)\end{array}$ $\begin{array}{cc}\underset{n=1}{\overset{N}{.Math.}}\left(R_u^{i,\mathrm{lb}}\left[n\right]t\right)\ge B_i,\forall i& \left(9e\right)\end{array}$ $\begin{array}{cc}\frac{t^4}{{y\left[n\right]}^2}\le \left({y^{\left(r\right)}\left[n\right]}^2\right)+2y^{\left(r\right)}\left[n\right](y\left[n\right]-y^{\left(r\right)}.Math.\text{?}& \left(9f\right)\end{array}$ $\begin{array}{cc}& \left(9g\right)\end{array}$ $\text{?}\text{indicates text missing or illegible when filed}$ in the sub-problem (9), P.sub.prop.sup.B[n] is the propulsion power after the auxiliary variable y[n] is introduced, and is a convex function of w[n]; R.sub.u.sup.i,lb[n] is the lower bound of first-order taylor expansion of the information transmission rate R.sub.u.sup.i[n] on ∥w[n]−L.sub.i∥,and is a concave function of w[n]; the solving method of the sub-problem (9) is the same as that of the sub-problem (8); the optimal solution of the convex problem after approximation is the lower bound of the optimal solution of the original sub-problem; (4) overall iterative algorithm design in each iteration, by solving the sub-problem (8) and the sub-problem (9), alternately optimizing wake-up scheduling S, the time slot t and the UAV trajectory W; using the solution obtained in each iteration as the input of next iteration; the termination condition for iteration is that the increase of optimization values of one iteration and the previous iteration is less than a set threshold; the details are as follows: 4.1) setting an iteration termination threshold ε, an initial trajectory w.sup.0 and an iteration index r=0; 4.2) in the r+1 iteration, using the trajectory w.sup.r obtained from the r iteration to solve the sub-problem (8) to obtain the optimization result of the sub-problem (8) of the r+1 iteration, namely, wake-up scheduling S.sup.r+1 and time slot t.sup.r+1; 4.3) solving the sub-problem (9) by the given w.sup.r, S.sup.r+1 and t.sup.r+1 to obtain the optimization result of the sub-problem (9) of the r+1 iteration, namely trajectory w.sup.r+1; 4.4) if the increase of an optimization target value is greater than a threshold ε, then updating the iteration index r=r+1; skipping back to step 4.2) for the next iteration; and if the increase of the target value is less than the threshold ε, terminating the iteration.

Description

DESCRIPTION OF DRAWINGS

[0033] FIG. 1 is a schematic diagram of a single UAV uplink communication of data acquisition.

[0034] FIG. 2 is a flight trajectory diagram when the transmitted data amounts B are 50 bps/Hz and 130 bps/Hz respectively.

[0035] FIG. 3 is a flight speed diagram when the transmitted data amounts B are 50 bps/Hz and 130 bps/Hz respectively.

[0036] FIG. 4 shows the influence of the amounts B of transmitted data on flight cycles of three different solutions.

[0037] FIG. 5 shows the influence of the amounts B of transmitted data on energy efficiency of three different solutions.

[0038] FIG. 6 shows the influence of sensor energy Eon flight cycles of three different solutions.

[0039] FIG. 7 shows the influence of sensor energy Eon energy efficiency of three different solutions.

DETAILED DESCRIPTION

[0040] The present invention is described below in detail in combination with the drawings and embodiments.

Embodiment 1

[0041] It is assumed that a UAV serves 6 ground sensors which are randomly distributed. The UAV flies at a fixed altitude H=100 m with a maximum flight speed V.sub.m=50 m/s. One cycle T is fixedly divided into N=60 time slots. The coordinates of the sensors are expressed with a matrix as L=[−1100,500;−425,400;600,1100;200,200;800,−400;−700,−600].sup.T. Additive white gaussian noise (AWGN) at the receiving end of the UAV is σ.sup.2=−110 dBm, and power gain of reference distance is p.sub.0=−60 dB. The transmission power of the ground sensors is P.sub.A=0.1 W. If the UAV flies above the sensors, the channel power gain is

[00016]$h^i\left[n\right]=\frac{{\rho }_0}{H^2},\forall i\in \mathrm{SI}.$

In the case, the information transmission rate R.sub.u.sup.i[n] in formula (1) is the maximum, and the maximum is R.sub.u.sup.i[n]=9.9672 bps/Hz.

[0042] For the parameters in formula (3), the parameter values of the classic rotary-wing UAV is taken in embodiment 1: blade angular velocity Ω=300 r/s; rotor radius r=0.4 m; fuselage drag ratio d.sub.0=0.6; air density ρ=1.225 kg/m.sup.3; rotor solidity s=0.05; rotor disc area A=0.503 m.sup.2; mean rotor induced velocity v.sub.0=4.03 m/s. Then, in formula (3), the velocity that minimizes the propulsion power P(V) is V.sub.min=10.0125 m/s.

[0043] In this scenario, the present invention assumes that each sensor needs to transmit the same amount of data and has the same energy constraints. Namely, B.sub.i=B and E.sub.i=E. The above parameters are substituted into the optimization problem (7) for solving, to obtain the trajectory design for maximizing energy efficiency proposed in the present invention, as shown in FIG. 2, and the corresponding flight speed is shown in FIG. 3. The UAV flight trajectory is relatively smooth, and the flight speed changes little and fluctuates around the minimum energy speed V.sub.min. When B=50 bps/Hz, the UAV only flies within a small range. When B becomes larger than 130 bps/Hz, the flight distance and flight time of the UAV become larger. The UAV hovers near user 2 and User 4 for a period of time. The purpose is to maintain good channel quality and transmit more information while flying at minimum energy speed, so that the UAV can hover near the users.

Embodiment 2

[0044] According to the design scenario of embodiment 1, in order to demonstrate the superiority of the present invention, this section proposes two other benchmark solutions and compares the performance. Solution 1: energy efficiency maximization solution (the present invention). Solution 2: flying—hovering solution. Solution 3: energy efficiency maximization solution under fixed circular trajectories.

[0045] FIG. 4 and FIG. 5 respectively show the curves of flight cycle and energy efficiency changing with B. It can be seen that solution 1 has an obvious advantage in energy efficiency despite of longer flight cycle compared than solution 2. Compared with solution 3, in solution 1, the UAV has higher maneuverability and can fly to an appropriate position to communicate, so the cycle is shorter and the energy efficiency is higher. When B is increased, the flight cycles of the three solutions are increased, and the energy efficiency of solution 1 is decreased. This is because the UAV needs more time to transmit data to satisfy the increasing demand for B. In order to achieve high energy efficiency, a trade-off is needed between the total amount R({W},{t},{S}) of transmitted data and energy consumption E({W},{t}), and when the cycles are increased, R({W},{t},{S}) and E({W},{t}) are increased. When E({W},{t}) is increased faster, the energy efficiency is decreased.

[0046] FIG. 6 and FIG. 7 further compare the curves of the flight cycles and energy efficiency changing with E in the three solutions. When E is increased, the energy efficiency of the three solutions is increased respectively. This is because as E is increased, the sensor has more energy to transmit data, and the trade-off between the total amount R({W},{t},{S}) of transmitted data and the energy consumption E({W},{t}) increases R({W},{t},{S}) faster, so the energy efficiency is increased. This further explains that the present invention can effectively realize high energy efficiency green communication of the UAV.

[0047] The above embodiments only express the implementation of the present invention, and shall not be interpreted as a limitation to the scope of the patent for the present invention. It should be noted that, for those skilled in the art, several variations and improvements can also be made without departing from the concept of the present invention, all of which belong to the protection scope of the present invention.