In this paper, we solve the problem of positioning based on the time of arrival (TOA) technique in wireless sensor networks. Different from the traditional estimation methods, the position of a target node is considered as a strategy of anchor nodes and the cognition information is introduced to enrich positional information. After proposing the concept of consensus of cognition and consensus of measurements, we re-investigate the positioning problem from a game-theoretic perspective. It is proved that the proposed game is an exact potential game which exhibits attractive properties about the Nash equilibrium (NE). Then the best response (BR) algorithm is used to achieve the NE point. The simulation results indicate that the performance of positioning with a suitable trade-off parameter is better than the performance of recursive least-squares (RLS) algorithm in terms of positional accuracy and convergence rate.

In this article, we study the problem of anti-jamming through channel selection in fading environment. Different from the most existing works which ignored the fading characteristic of the channels, we use a Markov channel model to capture the influence of variable channel transmission rate in fading environment. Then, we model the anti-jamming channel selection problem as a Markov decision process. On this basis, an online reinforcement learning algorithm (Q-learning) is proposed to select the optimal channel intelligently. Simulation results show that the proposed algorithm outperforms the sensing algorithm. The reason is that it can learn both the pattern of jamming signal and the condition of channel, so as to select the channel with better condition for data transmission. Based on the proposed algorithm, we develop a real-life dynamic spectrum anti-jamming testbed based on the USRP platform in the indoor environment to demonstrate the effectiveness of the algorithm.

Swarming is one of the important trends in the development of small multi-rotor UAVs. The stable operation of UAV swarms and air-to-ground cooperative operations depend on precise relative position information within the swarm. Existing relative localization solutions mainly rely on passively received external information or expensive and complex sensors, which are not applicable to the application scenarios of small-rotor UAV swarms. Therefore, we develop a relative localization solution based on airborne monocular sensing data to directly realize real-time relative localization among UAVs. First, we apply the lightweight YOLOv8-pose target detection algorithm to realize the real-time detection of quadcopter UAVs and their rotor motors. Then, to improve the computational efficiency, we make full use of the geometric properties of UAVs to derive a more adaptable algorithm for solving the P3P problem. In order to solve the multi-solution problem when less than four motors are detected, we analytically propose a positive solution determination scheme based on reasonable attitude information. We also introduce the maximum weight of the motor-detection confidence into the calculation of relative localization position to further improve the accuracy. Finally, we conducted simulations and practical experiments on an experimental UAV. The experimental results verify the feasibility of the proposed scheme, in which the performance of the core algorithm is significantly improved over the classical algorithm. Our research provides viable solutions to free UAV swarms from external information dependence, apply them to complex environments, improve autonomous collaboration, and reduce costs.

The problem of distributed power allocation in wireless sensor network (WSN) localization systems is investigated in this paper, using the game theoretic approach. Existing research focuses on the minimization of the localization errors of individual agent nodes over all anchor nodes subject to power budgets. When the service area and the distribution of target nodes are considered, finding the optimal trade-off between localization accuracy and power consumption is a new critical task. To cope with this issue, we propose a power allocation game where each anchor node minimizes the square position error bound (SPEB) of the service area penalized by its individual power. Meanwhile, it is proven that the power allocation game is an exact potential game which has one pure Nash equilibrium (NE) at least. In addition, we also prove the existence of an $ϵ$ -equilibrium point, which is a refinement of NE and the better response dynamic approach can reach the end solution. Analytical and simulation results demonstrate that: (i) when prior distribution information is available, the proposed strategies have better localization accuracy than the uniform strategies; (ii) when prior distribution information is unknown, the performance of the proposed strategies outperforms power management strategies based on the second-order cone program (SOCP) for particular agent nodes after obtaining the estimated distribution of agent nodes. In addition, proposed strategies also provide an instructional trade-off between power consumption and localization accuracy.

In this letter, optimal power allocation strategies are proposed to improve the localization performance in jammed wireless localization systems. For a wireless localization system, the power allocation is a key issue because it relates to network lifetime and localization accuracy. However, when jammer nodes are considered, the power allocation problem for anchor nodes becomes a new task due to the effect of jammer nodes. So in this letter, two power allocation schemes are introduced in jammed wireless localization systems. In the first scheme, the average Cramer–Rao lower bound (CRLB) of agent nodes is the objective and in the second scheme, the objective is to minimize the maximum CRLB of agent nodes. In particular, both power allocation problems can be transformed into semi-definite programs (SDP) with total and peak power constraints. Numerical results indicate that the proposed schemes can reach better performance in localization accuracy compared to uniform power allocation strategies.

The problem of efficient link selections for cooperative navigation is investigated in dynamic wireless networks, using an extended Kalman filter based overlapping coalition formation (OCF) game approach. Due to the densely deployment of nodes without proper link selections, the conventional cooperative navigation approaches usually yield high time‐frequency resources as the existing resource management methods only work properly in static localisation systems. A new link selection mechanism which is reliable with limited resources in dynamic positioning scenarios is proposed. The varying topological structure and states of every node will be taken into consideration while each node abandons a considerable number of unnecessary links. Firstly, the state of every mobile node will be estimated in real‐time. Then a selection criterion is applied to determine whether nodes are well navigated. If the criterion is satisfied, an OCF game will be employed to construct the effective link selection. Otherwise, a re‐link mechanism will be used to obtain precise state information of each node. Numerical results show that the cooperative navigation algorithm can significantly improve the positioning accuracy and the proposed OCF game approach can build a cooperative network with limited number of links at the cost of appropriate performance degradation.