Gao Xiao-guang

Effective Real-Time Unmanned Air Vehicle Path Planning in Presence of Threat Netting

T HE problem of accurately and efficiently planning safe paths is critical for a number of unmanned air vehicle (UAV) applications. This work focuses on UAV path planning through battlefields littered with various enemy threats, such as radar-guided surface-to-air missiles (SAMs). A particular threat may receive information from nearby threat locations with which it can communicate, and the lethality of the threat may increase based on the received information. This process is called threat netting [1,2], and its essential effect is a greater degree of lethality. This Note is devoted to the problem of real-time path planning for UAVs in the presence of threat netting, and each threat is considered a radar-guided SAM(Fig. 1); thismeans that, before themissile launch, the radarmust continuously track theUAV for some response period, and after the launch, accurate missile guidance requires that the radar system maintain tracking of the UAV during the flight of the missile [3]. Although some studies in the literature offer path planning algorithms in the presence of threat netting, there is no approach that integrates the response time and the guidance time of a SAM.Other studies offer path planning algorithms in the presence of radar-guided SAMs, but there is no approach that integrates the process of threat netting. This Note proposes a method of effective real-time UAV path planning in a radar-guided SAM netting environment and presents its results. Although the current literature on UAV path planning does not contain models of integrated threat netting specifically for radar-guided SAMs, some studies describe integrated threat netting and others address radar-guided SAMs. Brief reviews of the relevant results in these studies are as follows. References [1,2] introduce a real-time route planning technique called Sparse A* Search (SAS), which allows a number of mission-dependent route constraints to be incorporated into the search process and allows for an accurate and efficient consideration of the threat netting effect. Two steps must be performed before the search process begins. The first step involves marking the grid cells contained within the various threat intervisible regions. These cells aremarkedwith that particular threat’s identification number in different ways. If a grid cell falls withinmore than one threat’s intervisible region, then that grid cell receivesmultiplemarkings from each of the respective threat locations. The second step involves generating a threat netting cost lookup (TNCLU) table. The TNCLU table contains cost information concerning the lethality effects of netting for the various threat types and locations. Many different factors can be considered when generating the lethality values in the lookup table. Once the appropriate grid cells have been marked and the TNCLU table has been generated, the SAS algorithm is run to find a safe route. During the SAS process, both the total route cost information and the previously traversed threat information are considered. The values in the TNCLU table are computed a priori based on previous experiences with the particular threat types in various scenarios, but in general, it is hard to determine an accurate threat cost to construct the TNCLU table in advance. In these papers, another problem is that the radar cross sections (RCSs) of theUAVs are not discussed. Previousworks [3,4] have introduced a type of real-time path planning algorithm for unmanned combat air vehicles (UCAVs) in the presence of the radar-guidedSAMs. The problem is formulated using the framework of an interaction between three subsystems: the aircraft, the radar, and the missile. The main features of this integrated model are as follows. The aircraft RCS depends explicitly on both the aircraft’s aspect and its bank angles; hence, the RCS and aircraft dynamics are coupled. The probabilistic nature of radar tracking is accounted for in the model; namely, the probability that the aircraft has been continuously tracked depends on the aircraft RCS and range. Finally, the factors required tomaintain tracking before missile launch and during missile flyout are also modeled. Based on this model, the problem of UCAV path planning is formulated as a minimax optimal control problem.However, theseworks do not show how to plan theUCAVpath if a particular SAM receives information from a nearby SAM. Other works in the literature [5–7] discuss how to plan the trajectories for flight vehicles in the presence of threat zones such as radar detection regions. Reference [5] presents the linear approximation of a nonlinear radar detection risk function with integer constraints and indicator (0–1) variables and shows that this approach can yield UAV trajectories depending on the acceptable risk of detection. Reference [6] establishes a methodology tominimize the peak of the RCSs of autonomous precision guidedmunitions (APGMs) as they ingress to a selected target through a radar threat environment. This research demonstrates how route planning may be combined with the simultaneous specification of aerodynamically feasible yaw and bank angles to significantly reduce APGM observability. In [7], the detection model and the UAV’s dynamics are represented as a linear program subject to mixed integer constraints. This mixed integer linear program is then solved to search for all feasible solutions, and it produces the best path based on the user-specified parameters.

Research on the self-defence electronic jamming decision-making based on the discrete dynamic Bayesian network

Abstract The manner and conditions of running the decision-making system with self-defense electronic jammingare given. After proposing the scenario of applying discrete dynamic Bayesian network to the decision making withself-defense electronic jamming, a decision-making model with self-defense electronic jamming based on the discretedynamic Bayesian network is established. Then jamming decision inferences by the aid of the algorithm of discretedynamic Bayesian network are carried on. The simulating result shows that this method is able to synthesizedifferent targets which are not predominant. In this way, various features at the same time, as well as the samefeature appearing at different time complement mutually; in addition, the accuracy and reliability of electronicjamming decision making are enhanced significantly.

Resource match cost based multi-UAV decentralized coalition formation in an unknown region

This paper proposes an algorithm for the decentralized coalition formation of multiple heterogeneous UAVs that cooperatively perform a search and attack mission to neutralize the static or dynamic ground targets in a highly uncertain region where no prior information is available about the targets, and no centralized communication link with the UAVs is possible. In such cooperative missions, if the detecting UAV does not have enough resources to neutralize a target then a coalition of UAVs may needs to be formed that fulfills the target resource requirement. This coalition formation is computationally complex due to the combinatorial nature of the problem and is NP-Hard. Therefore, solutions with low computational complexity are required. The proposed decentralized coalition formation algorithm is sub-optimal and is computationally less complex. The proposed algorithm is based on the resource match cost criteria, in which the algorithm iteratively selects the final coalition members from the responding UAVs which are closest in resource match to the target resource requirement. After the selection of every single coalition member, the new resource match cost on the basis of new target resource requirement is calculated for the remaining responding UAVs and the above procedure is repeated until the target resource requirement is satisfied. The main objective of the algorithm is that, the coalition formed must be of minimum size and closest in match to the target resource requirement, so that more UAVs and resources must remain available for the search of other targets. The proposed solution enables the UAVs to form parallel coalitions to neutralize multiple targets and to utilize their resources more effectively. The performance of the proposed algorithm is evaluated through simulation tests and the results are compared with one of the reference sub-optimal decentralized coalition formation algorithm. The results show that the proposed algorithm is more effective and determines the near optimal set of member UAVs for the decentralized coalition formation. However, the proposed algorithm is naive with less computational complexity and can produce sub-optimal solutions which in most cases are near optimal.

Multi-UAVs cooperative control in communication relay

A kind of Multi-UAVs cooperative control algorithm under a disaster search and rescue scenario is presented. The states of UAVs are separated as search and relay. In search state, UAVs constantly look for wireless signals from the ground rescue workers by planning path under communication constraints. In relay state, UAVs share information without delay by selecting information route and planning UAVs flight path. Simulation results show that this kind of multi-UAVs cooperative control algorithm could plan path for UAVs efficiently and optimize communication performance of relay network.

Coalition Formation for Multiple Heterogeneous UAVs in Unknown Environment

To improve the effectiveness of multiple heterogeneous unmanned aerial vehicles (UAVs) cooperative with each other as a team to search and prosecute targets in unknown environment, a novel coalition formation method is presented in this paper. First, the coalition formation model is established based on minimizing the target prosecution delay and the size of the coalition with the constraint of required resources and simultaneous strike. Second, since solving the coalition formation optimization problem is computationally intensive, we develop a multistage sub-optimal coalition formation algorithm that has low computational complexity. Third, in order to enable multiple cooperative UAVs accomplish the search and prosecute missions autonomously, a distributed autonomous control strategy is proposed which is based on the finite state machine. The simulation result of a scenario shows the rationality, validity and high real-time performance of the method of coalition formation in multiple heterogeneous UAVs cooperative search and prosecutes in the unknown environment. Monte Carlo method is employed to validate the impact of the number of UAVs and targets on the performance of the coalition formation algorithm.

Cooperative target tracking algorithm for a couple of UAVs under communication constraints

One integrated algorithm for a couple of Unmanned Aerial Vehicles (UAVs) to follow a ground target cooperatively is presented. By introducing the funnel function and parameter freezing methods, communication and detection constraints are steadily maintained. Besides, cooperative strategy for measurement fusion purifies the noised geolocation data of the target. A simulation combining all proposed schemes is executed and the effectiveness is verified.

JDF and SJDF: Two DOA estimators for wideband jamming

It is proved that Two-sided Correlation Transformation (TCT) algorithm is essentially a narrowband processing method. In order to locate the wideband jamming source in counter-countermeasures and electron reconnaissance domain, a new bearing finding algorithm for wideband jamming named Jamming Direction Finding (JDF) algorithm is proposed, and the Simplified JDF (SJDF) is deduced. It shows that if there is only one signal, JDF and SJDF get higher performance than TCT with theory and simulation results. It also shows that JDF is obviously better than SJDF when Signal-to-Noise Ratio (SNR) is low, but when SNR is high SJDF can performs closely to JDF. However, many signals which have non-overlapping spectra in the space, SJDF can get better performance than JDF. JDF and SJDF can be applied in counter-countermeasures and electron reconnaissance domain.

Design and operation strategies of the system for destroying time-sensitive target based on system effectiveness

Abstract To improve the effect of destroying time-sensitive target (TST), a method of operational effectiveness evaluation is presented and some influential factors are analyzed based on the combat flow of system for destroying TST. Considering the possible operation modes of the system, a waved operation mode and a continuous operation mode are put forward at first. At the same time, some relative formulas are modified. In examples, the influential factors and operation modes are analyzed based on the system effectiveness. From simulation results, some design and operation strategies of the system for destroying time sensitive targets are concluded, which benefit to the improvement of the system effectiveness.

Design method of organizational structure for MAVs and UAVs heterogeneous team with adjustable autonomy

The increasingly complex battlefield environment requests much closer connection in a team having both manned and unmanned aerial vehicles (MAVs and UAVs). This special heterogeneous team structure causes demands for effective organizational structure design solutions. Implementing adjustable autonomy in the organizational structure, the expected evaluation function is established based on the physical resource, intelligent resource, network efficiency, network vulnerability and task execution reliability. According to the above constraints, together with interaction latency, decision-making information processing capacity, and decision-making latency, we aim to find a preferential organizational structure. The proposed organizational structure includes cooperative relationships, supervisory control relationships, and decision-making authorization relationships. In addition, by considering the influence on the intelligent support capabilities and the task execution reliability created by adjustable autonomy, it helps to build the proposed organizational structure designed with certain degree of flexibility to deal with the potential changes in the unpredictable battlefield environment. Simulation is conducted to confirm our design to be valid. And the method is still valid under different battlefield environments and interventions.

Bayesian approach to learn Bayesian networks using data and constraints

One of the essential problems on Bayesian networks (BNs) is parameter learning. When purely data-driven methods fail to work, incorporating supplemental information, like expert judgments, can improve the learning of BN parameters. In practice, expert judgments are provided and transformed into qualitative parameter constraints. Moreover, prior distributions of BN parameters are also useful information. In this paper we propose a Bayesian approach to learn parameters from small datasets by integrating both parameter constraints and prior distributions. First, the feasible parameter region is derived from constraints. Then, using the prior distribution, a posterior distribution over the feasible region is developed based on the Bayes theorem. Finally, the parameter estimations are taken as the mean values of the posterior distribution. Learning experiments on standard BNs reveal that the proposed method outperforms most of the existing methods.