Research Article  Open Access
Zhe Zhang, Jian Wu, Jiyang Dai, Cheng He, "Rapid Penetration Path Planning Method for Stealth UAV in Complex Environment with BB Threats", International Journal of Aerospace Engineering, vol. 2020, Article ID 8896357, 15 pages, 2020. https://doi.org/10.1155/2020/8896357
Rapid Penetration Path Planning Method for Stealth UAV in Complex Environment with BB Threats
Abstract
This paper presents the flight penetration path planning algorithm in a complex environment with Bogie or Bandit (BB) threats for stealth unmanned aerial vehicle (UAV). The emergence of rigorous air defense radar net necessitates efficient flight path planning and replanning for stealth UAV concerning survivability and penetration ability. We propose the improved AStar algorithm based on the multiple step search approach to deal with this uprising problem. The objective is to achieve rapid penetration path planning for stealth UAV in a complex environment. Firstly, the combination of singlebase radar, dualbase radar, and BB threats is adopted to different threat scenarios which are closer to the real combat environment. Besides, the multistep search strategy, the prediction technique, and path planning algorithm are developed for stealth UAV to deal with BB threats and achieve the penetration path replanning in complex scenarios. Moreover, the attitude angle information is integrated into the flight path which can meet real flight requirements for stealth UAV. The theoretical analysis and numerical results prove the validity of our method.
1. Introduction
1.1. Background and Motivation
In recent years, the emergence of new military equipment and systems, the stealth unmanned aerial vehicles (UAV) are gaining much attention due to the airdefense radar net is increasingly in the field of close and low altitude combat. Meanwhile, the survivability and combat effectiveness of stealth UAV will have a great influence on modern air warfare [1, 2]. The nose, fuselage, and tail of stealth UAV have relatively small radar crosssection (RCS), which aims to reduce the detection probability of radar system and improve the penetration ability and survivability of the stealth UAV [3]. However, previous works have mainly concentrated on the path planning in the static environment, there are many theoretical challenges and practical problems for stealth UAV on the problem of penetration path planning, such as the constraint of attitude angle and control of track point, the efficiency of path planning algorithm, and realtime performance. Besides, with regard to the Bogie or Bandit (BB) threats in the real combat environment, it is difficult for stealth UAV to achieve penetration path planning which is rapidly and safely. Therefore, the kinematics model and path planning algorithm for stealth UAV should be studied in a complex environment.
1.2. Literature Review
More recently, a lot of research has been done in the area of penetration path planning for aircraft, and so many algorithms had been proposed. In Reference [4], a new concept on the aircraft path planning for the variation of radar crosssection (RCS) was firstly presented, which indicates that the stealth UAV can significantly reduce the RCS by adjusting the attitude angle. In Reference [5], a research framework of low detectability tactical trajectory planning was proposed in the presence of multiple radardetected threat environments; the solution of the optimal trajectory is transformed into the optimal control problem with the minimum radar detection probability. A penetration trajectory optimization method considering the influence of aircraft radar RCS was proposed in Reference [6]. In Reference [7] and Reference [8], a hybrid heuristic adaptive pseudospectrum method was proposed to address the lowdetectability trajectory planning problem. In contrast, these methods usually adopt a simplified kinematic model, which aims to obtain a rough reference track, and the constraints and control conditions of radar attitude are rarely considered in the reference trajectories. In Reference [9], a path planning method for stealth UAV based on the RCS ellipsoid model was proposed to find the optimal trajectory during flight. Inversely, this model requires a large amount of computation in addressing the problem of path planning. In Reference [10], a trajectory planning method based on dynamic game theory was presented to analyze the relationship between radar and UAV. In Reference [11], a realtime air battle trajectory optimization and game model for aircraft based on a rolling timedomain control strategy were proposed in a complex environment. However, two kinds of shortages exist in the research. One was that only can be applied to the static environment with known radar position, and it rarely achieves the path planning for stealth UAV in the dynamic environment. The other was that the calculation process is relatively complicated, and the realtime performance of the path planning method is poor. Erlandsson proposed a path planning model, and the expected path cost of air mission was discussed in a hostile environment; the particle swarm optimization algorithm (PSO) was adopted to address the optimization path planning problem [12, 13]. Meanwhile, many scholars are focus on analyzing a series of optimization algorithms to address the penetration path planning problem for unmanned aerial vehicle, such as AStar algorithm [14], genetic algorithms (GA) [15], sparse AStar algorithm (SAS) [16], rapidly exploring randomtree algorithm (RRT) [17, 18], particle swarm optimization algorithm (PSO) [19, 20], black hole algorithm [21], and other combinatorial optimization algorithms [22]. However, these optimization algorithms are only applied to address the stealth path planning problem in the static combat environment, the singlebase radar is usually adopted in the combat scenario that the position of each radar is known, and the number is relatively small. Furthermore, the efficiency of the algorithm and the safety of the path are rarely optimal.
1.3. Contribution
In this paper, we focus on analyzing the penetration path planning for stealth UAV based on an improved AStar algorithm, which aims to achieve rapid penetration path planning in the dynamic combat environment with BB threats. The novelty of this method is summarized as follows: firstly, the main idea of the modelbased predictive control (MPC) and learning realtime AStar algorithm (LRTA) is integrated into the path to devise the improved AStar algorithm. Additionally, for the BB threats in a complex environment, the improved AStar algorithm is applied for the penetration path replanning with fixed altitude to improve the survivability and combat effectiveness of stealth UAV. Moreover, numerical simulation results are applied to demonstrate the validity of the improved AStar algorithm in the presence of the radar net with BB threats.
1.4. Paper Organization
The paper is organized as follows. Section 2 describes the mathematical model of stealth UAV. Section 3 discusses the detection probability calculation of multiple radar net, which is close to the real combat environment. Section 4 presents the improvement of the path planning algorithm, includes improved AStar algorithm, LRTAStar algorithm, and DStar algorithm. Our numerical results and evaluation are presented in Section 5. Section 6 states our conclusions and future work.
2. The System Modeling
2.1. Kinematics
It is assumed that the stealth UAV can be defined as a particle with attitude information and ignores the influence of wind and other external conditions [1]. We are focus on studying the control of the flight path. The stealth UAV moves in a horizontal plane at a constant altitude . Therefore, the kinematics model can be given by where and are the Cartesian coordinates of the aircraft, is the heading angle, is the constant speed, is the input signal, and is the acceleration normal to the flight path vector.
Specify radar locations in the plane (on the ground) by the coordinates . The range from the th radar to the stealth UAV is given by
Additionally, let be the azimuth, aspect, and roll angles, respectively, measured to the th radar, is the acceleration of gravity.
2.2. Dynamic RCS Features
Radar crosssection (RCS) is a measure of ability for UAV to reflect electromagnetic radiation emitted by radar [23]; the RCS of the nose, fuselage, and tail of the stealth UAV is smaller than conventional UAV, and the RCS value will be changed due to different radar bands and radar polarization modes. It plays an important role in the penetration path of stealth UAV in combat. For the path planning problem, RCS is usually defined as a constant, but it is not reasonable in practical application. By convention [24], it is modeled as a function of the aspect angle and roll angle as viewed from the radar. Therefore, the dynamic RCS model is adopted to an ellipsoid [1], which is given by where represents the RCS value of UAV, represents aspect angle, represents roll angle, is relatively small frontal RCS, is a larger beam aspect RCS, and is relatively large RCS when viewed from above or below.
3. Radar Detecting Probability
When the acquired target information meets the common track criteria, the radar system confirms the detection of the target and transmits the acquired target information to the information fusion center. The detection probability of target in a singlebase radar, dualbase radar, and a radar net system is depicted as follows.
3.1. SingleBase Radar
For the singlebase radar, the detection probability of the UAV is only related to the distance from the UAV to the radar center. In a period, the radar instantaneous detection probability can be expressed as follows: where is the instantaneous detection probability of the single base radar, is given by Eq.(2), and and are the performance parameters of specific radar, respectively.
3.2. DualBase Radar
The dualbase radar system is considered to be effective systems to counter the four major threats due to their technical characteristics of transmitterreceiver separation and passive reception. For the performance of the radar system, it has the advantages that singlebase radar can not match, such as a larger detection range and higher accuracy. Dualbase radars are usually deployed in modern air defense systems; therefore, we focus on analyzing the dualbase radar system.
The transmitter is also employed as a receiver for dualbase radar systems, which are T/RR systems. A transmitter can work synchronously with other splitter receivers in the dualbase radar system. The receiver synchronizes with the transmitter in time, phase, and space. The configuration of the dualbase radar system is depicted in Figure 1.
where represents the position of the target, represents the transmitter, represents the receiver, represents the distance between the transmitter and receiver, represents the distance between the target and transmitter, represents the distance between the target and receiver, and represents the dualbase angle.
Additionally, is defined as the detection range of the bistatic radar, that is . where and . is a constant when the radar parameters are given. Hence, the maximum detection range of the bistatic radar can be obtained by where represents the peak power of the transmitter, represents the antenna gain, represents the system loss, represents Boltzmann constant, is the noise temperature of the receiver, is the signal to noise ratio of the receiver, and represents the wavelength. Similarly, the detection probability of the bistatic radar under exponential distribution is given by
3.3. Detection Probability of Networked Radar System
For a complete netted radar system, the radar net can improve the detection probability of UAV, significantly. The detection probability of a networked radar system mainly refers to the target detection probability calculated by the information fusion center. The rank K fusion rule is widely adopted in modern networked radar systems. Therefore, we adopt the rank K fusion rule to analyze the detection probability of networked radar [25, 26].
It is assumed that the number of radars in the netted radar system is , which includes singlebase radar and bistatic radar. When the number of radar detected in the system exceeds the detection threshold according to the rank K fusion rule, that means, the target has been detected by the radar system, and the approximate value of the optimal detection threshold can be depicted as follows.
The radar makes a local judgment based on the detection of the stealth UAV, and the judgment results are either 0 or 1, which depends on whether the local threshold detection target exists or not. and are binary assumptions, where represents target does not exist and represents target exists. Therefore, the decision value of the th radar ) can be expressed as follows:
Additionally, the local decision result is passed to the information fusion center of the radar system to form a global decision matrix , that is, . And the radar information fusion rule for the network is denoted as ; the decision rule for rank K fusion is given by
Meanwhile, the total detection probability of the network radar system on the UAV is given by where is a set of local decision vectors that make the fusion center judge “1”, is a set of local decision vectors that make the fusion center judge “0”, and is the discovery probability of the first radar in the radar net.
4. Path Planning Algorithm
Astar algorithm is an effective search method to solve the shortest path in a static road network, which is widely used to settle the path planning problems of many agents. The closer the estimated distance is to the actual value, the faster the final search speed is [27–29]. This paper discusses the improvement method of the AStar algorithm to address the penetration path planning problem for stealth UAV.
4.1. The AStar Algorithm
The main idea of the standard AStar algorithm is as follows. Firstly, select the appropriate heuristic function, estimate the generation value of the extensible search points in the search area, comprehensively. Moreover, compare the different cost values of each point. Moreover, consider the operation time and distance cost of the track point search. Finally, find an optimal path. In the AStar algorithm, the operation of the OPEN list and CLOSE list are usually performed to achieve the storage and update of track points.
However, the standard AStar algorithm that is applied to find the optimal path for stealth UAV has many disadvantages: (a) the route obtained by the Astar algorithm only has the position of stealth UAV, and it can not reflect the characteristics of the dynamic RCS and attitude angle information. (b) Unknown path cost estimation is rarely calculated in the process of path planning accurately, and the computation time is too long to find a globally optimal path. (c) the performance of realtime is poor, and the algorithm can not deal with BB threats. Therefore, we are focus on improving the performance of the AStar algorithm.
4.2. The DStar Algorithm
The DStar algorithm is developed by the AStar algorithm and the Dijkstra algorithm [30, 31], which is suitable for addressing the path planning problem in unknown environments. The main idea of the algorithm is to search the reverse path from the destination to the start, and the heuristic function expression of the DStar algorithm is given by where represents the actual cost of the path from the destination to state , and is the estimated cost of the path from the state to the starting point.
The main steps of the DStar algorithm are given as follows. Step 1: the value of all states are set to NEW, and are set to infinity for all states, is set to zero, and the destination is added to the OPEN list. Step 2: keep performing the search until the location of which is removed from the OPEN list, if the CLOSE list contains the tag value of the state , the complete path sequence will be obtained. In contrast, path planning has failed in the threat scenario. Step 3: if the path exists, the state can be employed to point to the destination by the back pointer. Besides, if the changes, the current path cost will be immediately recalculated. Step 4: insert the affected state which near the new threat into the OPEN list, and then return to Step 2. The pseudocodes of the DStar algorithm are depicted as follows Algorithm 1.

4.3. Improved AStar Algorithm
We are focus on analyzing the learning realtime AStar algorithm (LRTAStar) which satisfies the requirements of realtime planning in a dynamic environment [32, 33]. In contrast, the path obtained by adopting the LRTAStar algorithm is composed of a series of the tortuous track; it is difficult to achieve accurate track tracking control due to the limited maneuverability. Besides, in the flight process of stealth UAV, LRTAStar algorithm is easy to fall into a local dead loop, which leads to the failure of path searching. Therefore, further combined with the idea of modelbased predictive control (MPC) [34, 35], an improved AStar algorithm with the multistep optimal search is proposed in this section.
MPC is an optimization control method, which is mainly composed of model prediction, feedback correction, and rolling optimization. The principle of the MPC system is depicted in Figure 2.
The closedloop output prediction is given by where is the actual control quantity on the system at moment , is the reference track softened by the input filter, is the predicted output value of the model, is the closedloop output prediction, and is the error correction coefficient.
According to Eq. (13), the optimal control law for multistep prediction is given by where is the length of the prediction domain; is the length of the control domain; is the output prediction error, and is the weighting coefficient of the control variable.
In the process of a multistep optimization search, the track cost of each predicted track is considered as the cumulative value of the costs of predicted track nodes. Therefore, the objective function of UAV flight path planning can be obtained according to the optimal control law of the MPC system. The cost of the predicted flight path in steps from the th node is given by where is the radar threat cost at the th prediction point on the current predicted track segment, which can be obtained by Eq. (11). is the weight of threat cost; is the distance cost weighted matrix, is the control sequence, and is the distance cost between the coordinate of destination and the coordinate of th track point and .
The stealth UAV is limited by its maneuverability in the process of a multistep search, where is sought by the current track point and heading angle, and the cost function for the node has a minimum value. Therefore, add the parameter to the heuristic function of the original algorithm, which aims to simplify the operation, reduce the search time and ensure the optimality of path planning, and the new heuristic function expression in the improved AStar algorithm is given by
Where is the ratio of the path cost of the current state to the path cost of an unknown region, is the current node, is the adjacent extended node, and is the cost from the current node to the adjacent node, and . The pseudocode of the improved AStar algorithm is depicted as follows Algorithm 2, and the flow chart of the improved AStar algorithm is depicted in Figure 3.

5. Numerical Results
Numerical simulations of penetration path planning are performed by employing the improved AStar algorithm, LRTAStar algorithm, and DStar algorithm in different threat scenarios, which aims to verify the effectiveness of the improved AStar algorithm. The simulation experiments are conducted with MATLAB2020Ra software and a Windows 10 system. The parameters of flight are depicted in Table 1, the value is determined by the number of radar , and it will change when there is a BB threat in the threat scenario. Additionally, it is considered as the probability state of radar high detection when exceeds 0.4, and is performed to evaluate the safety degree of the track segment. The RCS data of a certain type of stealth UAV is depicted in Figure 4.

5.1. The First Threat Scenario
The threat region has a range of ; the geographical location and types of threat sources are presented in Table 2. The coordinate of the starting point is (5,5) km, and the coordinate of the target point is (90,90) km. The numerical results of penetration path planning which is performed by different algorithms in the first scenario are depicted in Figure 5. The variation of heading angle and roll angle which is applied by the improved AStar algorithm in the first scenario is depicted in Figure 6, the detection probability of netted radar in the first threat scenario is depicted in Figure 7, and the statistical result of flight in the first scenario is presented in Table 3.

(a) Improved AStar algorithm
(b) LRTAStar algorithm
(c) DStar algorithm
(a) The variation of heading angle
(b) The variation of roll angle
(a) Original planning
(b) Replanning

Figure 5 demonstrates the penetration paths for stealth UAV which is performed by employing three different algorithms in a scenario. From Figure 7 and Table 3, we can infer that the stealth UAV can achieve penetration path planning by employing in the first scenario in which a static or dynamic environment. However, compared with the other two algorithms, the paths obtained by employing the improved AStar algorithm has a smaller value of and takes less time. Figure 6 demonstrates that the improved AStar algorithm can accurately adjust the attitude angle and reduce the value of RCS on the paths, and this algorithm achieves the rapid and safe path penetration planning for stealth UAV. Besides, the paths of the improved AStar algorithm is shorter than the LRTAStar algorithm, and the improved AStar algorithm has higher path planning efficiency and safety, which further proves the effectiveness of the improved AStar algorithm.
5.2. The Second Threat Scenario
The threat region has a range of ; the geographical location and types of threat sources are presented in Table 4. The coordinate of the starting point is (5,5) km, and the coordinate of the target point is (90,90) km. The numerical results of penetration path planning which is performed by different algorithms in the second scenario are depicted in Figure 8. The variation of heading angle and roll angle which is performed by an improved AStar algorithm in the second scenario is depicted in Figure 9, the detection probability of netted radar in the second threat scenario is depicted in Figure 10, and the statistical result of flight in the second scenario is presented in Table 5.

(a) Improved AStar algorithm
(b) LRTAStar algorithm
(c) DStar algorithm
(a) The variation of heading angle
(b) The variation of roll angle
(a) Original planning
(b) Replanning

Figure 8 describes the penetration paths for stealth UAV which is performed by applying three different algorithms in a scenario with more complex threat sources, and from Figure 10 and Table 5. For the original path planning, the penetration paths which are safe by employing all three algorithms in the scenario. However, for the path replanning in the presence of BB threats, the path replanning has failed in the second threat scenario, because the of paths by adopting the LRTAStar algorithm and Dstar algorithm are higher than 0.5. Inversely, stealth UAV can still achieve the path replanning in the second scenario with two BB threats by using an improved AStar algorithm. Moreover, the improved AStar algorithm takes less time and has higher path planning efficiency than the other two algorithms whether in the original path planning or replanning, and it further proves the validity of the algorithm. Figure 9 indicates that when the improved AStar algorithm is performed to penetration path planning, the variation of heading angle and roll angle are within the preset range, which can satisfy the real flight requirements.
5.3. The Third Threat Scenario
The threat region has a range of ; the geographical location and types of threat sources are presented in Table 6. The coordinate of the starting point is (5,5) km, and the coordinate of the target point is (90,90) km. The numerical results of penetration path planning which is performed by different algorithms in the third scenario are depicted in Figure 11. The variation of heading angle and roll angle which is performed by an improved AStar algorithm in the third scenario is depicted in Figure 12, the detection probability of netted radar in the third threat scenario is depicted in Figure 13, and the statistical result of flight in the third scenario is presented in Table 7.

(a) Improved AStar algorithm
(b) LRTAStar algorithm
(c) DStar algorithm
(a) The variation of heading angle
(b) The variation of roll angle
(a) Original planning
(b) Replanning

Figure 11 describes the penetration paths for stealth UAV which are performed by applying three different algorithms in a scenario with a large number of threat sources and BB threats; from Figure 13 and Table 7, we can see that the paths by adopting the LRTAStar algorithm and Dstar algorithm are higher than 0.5 no matter in the original path planning or path replanning. Obviously, stealth UAV can rarely achieve the penetration path planning which is safely and rapidly by employing these two algorithms. However, the of paths by adopting improved AStar algorithms are always lower than 0.5; therefore, stealth UAV can still achieve penetration path planning and replanning by using the improved AStar algorithm in dynamic threat scenarios. Furthermore, paths of the improved AStar algorithm have higher efficiency and safety, which further proves the validity of the improved AStar algorithm in the scenario with high threat density. Figure 12 indicates that stealth UAV can meet constraints of attitude angle in the field of close and low altitude combat.
6. Conclusions
This paper presented a new solution for path replanning for stealth unmanned aerial vehicle in a complex radar net environment with BB threats.
We are focus on analyzing the kinematics model of stealth UAV, threat source in penetration environment, and path planning algorithm. Further combined with modelbased predictive control (MPC) and the LRTAStar algorithm, an improved AStar algorithm is proposed to achieve penetration path planning and replanning for stealth UAV. Meanwhile, the attitude angle information of stealth UAV is added to the algorithm, which demonstrates the variation characteristics of dynamic RCS. Further combined with the kinematics analysis of stealth UAV and the detection performance analysis of radar net, the original paths, and the replanning paths can satisfy the real flight requirements.
Compared with the other two algorithms, the stealth UAV can rapidly achieve the penetration path planning by employing the improved AStar algorithm in a different complex environment with BB threats, improve the survivability of stealth UAV, and the efficiency of penetration path planning.
The threat scenarios are composed of singlebase radar, dualbase radar, and BB threats, which are closer to the real combat environment. The model and the improved AStar algorithm proposed in this paper can quickly generate better penetration paths in the combat area under a dynamic environment, exhibiting certain military application value.
In future work, we are focus on the realtime rapid penetration path planning for stealth UAV account for a threedimensional complex dynamic environment with the terrain and unknown motion targets in the field of close and low altitude combat.
Data Availability
No data were used to support this study.
Conflicts of Interest
The authors declare that there is no conflict of interest regarding the publication of this paper.
Acknowledgments
This work was supported in part by the National Natural Science Foundation of China under grant 61663032, Aeronautical Science Foundation of China under grant 2016ZC56003, and Innovation Special Fund of Nanchang Hangkong University for Graduate Student under grant YC2019026.
References
 F. H. Zeitz III, “UCAV path planning in the presence of radar guided surface to air missile threats, [Ph.D. dissertation],” Tech. Rep., Dept. Elect.Eng., University of Michigan, Detroit, MI, USA, 2005. View at: Google Scholar
 B. Yan, R. Liu, P. Dai, M. Xing, and S. Liu, “A rapid penetration trajectory optimization method for hypersonic vehicles,” International Journal of Aerospace Engineering, vol. 2019, Article ID 1490342, 11 pages, 2019. View at: Publisher Site  Google Scholar
 E. Sepulveda and H. Smith, “Technology challenges of stealth unmanned combat aerial vehicles,” The Aeronautical Journal, vol. 121, no. 1243, pp. 1261–1295, 2017. View at: Publisher Site  Google Scholar
 F. W. Moore, “Radar crosssection reduction via route planning and intelligent control,” IEEE Transactions on Control Systems Technology, vol. 10, no. 5, pp. 696–700, 2002. View at: Publisher Site  Google Scholar
 T. Inanc, M. K. Muezzinoglu, K. Misovec, and R. M. Murray, “Framework for lowobservable trajectory generation in presence of multiple radars,” Journal of guidance, control, and dynamics, vol. 31, no. 6, pp. 1740–1749, 2008. View at: Publisher Site  Google Scholar
 Q. Xu, J. Ge, and T. Yang, “Optimal Design of Cooperative Penetration Trajectories for Multiaircraft,” International Journal of Aerospace Engineering, vol. 2020, Article ID 8490531, 12 pages, 2020. View at: Publisher Site  Google Scholar
 H. Liu, J. Chen, L. Shen, and S. Chen, “Low observability trajectory planning for stealth aircraft to evade radars tracking,” Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering, vol. 228, no. 3, pp. 398–410, 2014. View at: Google Scholar
 S. Chen, H. Liu, J. Chen, and L. Shen, “Penetration trajectory planning based on radar tracking features for UAV,” Aircraft Engineering and Aerospace Technology, vol. 85, no. 1, pp. 62–71, 2013. View at: Publisher Site  Google Scholar
 J. J. Ruz, O. Arévalo, M. Jesús, and G. Pajares, “Using MILP for UAVs trajectory optimization under radar detection risk,” in Proceedings of the IEEE Conference on Emerging Technologies and Factory Automation 2006, pp. 957–960, Prague, Czech Republic, 2006. View at: Google Scholar
 R. Wang and J. Liu, “Adaptive formation control of quadrotor unmanned aerial vehicles with bounded control thrust,” Chinese Journal of Aeronautics, vol. 30, no. 2, pp. 807–817, 2017. View at: Publisher Site  Google Scholar
 J. Zhang, Q. Hu, D. Wang, and W. Xie, “Robust attitude coordinated control for spacecraft formation with communication delays,” Chinese Journal of Aeronautics, vol. 30, no. 3, pp. 1071–1085, 2017. View at: Publisher Site  Google Scholar
 T. Erlandsson, “Route planning for air missions in hostile environments,” The Journal of Defense Modeling and Simulation: Applications, Methodology, Technology, vol. 12, no. 3, pp. 289–303, 2015. View at: Publisher Site  Google Scholar
 T. Erlandsson and L. Niklasson, “Automatic evaluation of air mission routes with respect to combat survival,” Information Fusion, vol. 20, pp. 88–98, 2014. View at: Publisher Site  Google Scholar
 A. Le, V. Prabakaran, V. Sivanantham, and R. Mohan, “Modified astar algorithm for efficient coverage path planning in tetris inspired selfreconfigurable robot with integrated laser sensor,” Sensors, vol. 18, no. 8, 2018. View at: Publisher Site  Google Scholar
 M. A. Mohammed, M. K. Abd Ghani, R. I. Hamed, S. A. Mostafa, M. S. Ahmad, and D. A. Ibrahim, “Solving vehicle routing problem by using improved genetic algorithm for optimal solution,” Journal of Computational Science, vol. 21, pp. 225–262, 2017. View at: Publisher Site  Google Scholar
 Y. Lu, Z. Xue, G. S. Xia, and L. Zhang, “A survey on visionbased UAV navigation,” Geospatial Information Science, vol. 21, no. 1, pp. 21–32, 2018. View at: Publisher Site  Google Scholar
 A. Majeed and S. Lee, “A fast global flight path planning algorithm based on space circumscription and sparse visibility graph for unmanned aerial vehicle,” Electronics, vol. 7, no. 12, p. 375, 2018. View at: Publisher Site  Google Scholar
 Y. Li, R. Cui, Z. Li, and D. Xu, “Neural network approximation based nearoptimal motion planning with kinodynamic constraints using RRT,” IEEE Transactions on Industrial Electronics, vol. 65, no. 11, pp. 8718–8729, 2018. View at: Publisher Site  Google Scholar
 S. Shao, Y. Peng, C. He, and Y. du, “Efficient path planning for UAV formation via comprehensively improved particle swarm optimization,” ISA Transactions, vol. 97, pp. 415–430, 2020. View at: Publisher Site  Google Scholar
 Y. Zhao, Z. Zheng, and Y. Liu, “Survey on computationalintelligencebased UAV path planning,” KnowledgeBased Systems, vol. 158, pp. 54–64, 2018. View at: Publisher Site  Google Scholar
 P. Kumar, S. Garg, A. Singh, S. Batra, N. Kumar, and I. You, “MVOBased 2D path planning scheme for providing quality of service in UAV environment,” IEEE Internet of Things Journal, vol. 5, no. 3, pp. 1698–1707, 2018. View at: Publisher Site  Google Scholar
 A. Bakdi, A. Hentout, H. Boutami, A. Maoudj, O. Hachour, and B. Bouzouia, “Optimal path planning and execution for mobile robots using genetic algorithm and adaptive fuzzylogic control,” Robotics and Autonomous Systems, vol. 89, pp. 95–109, 2017. View at: Publisher Site  Google Scholar
 A. Laučys, S. Rudys, M. Kinka et al., “Investigation of detection possibility of UAVs using low cost marine radar,” Aviation, vol. 23, no. 2, pp. 48–53, 2019. View at: Publisher Site  Google Scholar
 C. Fang, “The simulation and analysis of quantum radar cross section for threedimensional convex targets,” IEEE Photonics Journal, vol. 10, no. 1, pp. 1–8, 2018. View at: Publisher Site  Google Scholar
 C. Fang, H. Tan, Q.F. Liu et al., “The calculation and analysis of the bistatic quantum radar cross section for the typical 2D plate,” IEEE Photonics Journal, vol. 10, no. 2, pp. 1–14, 2018. View at: Publisher Site  Google Scholar
 C. Zhang, X. Jiang, and D. Chen, “RCS promotion in orbital angular momentum imaging radar with rotational antenna,” IET Radar, Sonar & Navigation, vol. 13, no. 12, pp. 2140–2144, 2019. View at: Publisher Site  Google Scholar
 F. Duchoň, A. Babinec, M. Kajan et al., “Path planning with modified a star algorithm for a mobile robot,” Procedia Engineering, vol. 96, pp. 59–69, 2014. View at: Publisher Site  Google Scholar
 C. Liu, Q. Mao, X. Chu, and S. Xie, “An improved Astar algorithm considering water current, traffic separation and berthing for vessel path planning,” Applied Sciences, vol. 9, no. 6, p. 1057, 2019. View at: Publisher Site  Google Scholar
 Y. Li, T. Ma, P. Chen, Y. Jiang, R. Wang, and Q. Zhang, “Autonomous underwater vehicle optimal path planning method for seabed terrain matching navigation,” Ocean Engineering, vol. 133, pp. 107–115, 2017. View at: Publisher Site  Google Scholar
 T. T. Mac, C. Copot, D. T. Tran, and R. D. Keyser, “A hierarchical global path planning approach for mobile robots based on multi objective particle swarm optimization,” Applied Soft Computing, vol. 59, pp. 68–76, 2017. View at: Publisher Site  Google Scholar
 F. A. Raheem and U. I. Hameed, “Path planning algorithm using D heuristic method based on PSO in dynamic environment,” American Scientific Research Journal for Engineering, Technology, and Sciences, vol. 49, no. 1, pp. 257–271, 2018. View at: Google Scholar
 K. Singh, K. Singh, L. H. Son, and A. Aziz, “Congestion control in wireless sensor networks by hybrid multiobjective optimization algorithm,” Computer Networks, vol. 138, pp. 90–107, 2018. View at: Publisher Site  Google Scholar
 Y. Zhao, S. Yin, D. Li, Q. Yu, and P. Ranjitkar, “Improving motorway mobility and environmental performance via vehicle trajectory databased control,” IEEE Access, vol. 8, pp. 86862–86869, 2020. View at: Publisher Site  Google Scholar
 Z. Zhang, J. Wu, J. Dai, and C. He, “A novel realtime penetration path planning algorithm for stealth UAV in 3D complex dynamic environment,” IEEE Access, vol. 8, 2020. View at: Publisher Site  Google Scholar
 A. Afram, F. JanabiSharifi, A. S. Fung, and K. Raahemifar, “Artificial neural network (ANN) based model predictive control (MPC) and optimization of HVAC systems: a state of the art review and case study of a residential HVAC system,” Energy and Buildings, vol. 141, pp. 96–113, 2017. View at: Publisher Site  Google Scholar
Copyright
Copyright © 2020 Zhe Zhang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.