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For classical TCAR(three carrier ambiguity resolution) algorithm is affected by ionospheric delay and measurement noise, it is difficult to reliably fix ambiguity at medium and long baselines. An improved TCAR algorithm which takes the influence of ionospheric delay into account and has good adaptive robustness is proposed. On the basis of non-geometric TCAR model, ionospheric delay is obtained by linearly combining extra-wide-lane with fixed ambiguity, and then wide-lane ambiguity is solved.Solving narrow-lane ambiguity by adaptive robust filtering by constructing optimal combination observation, which can effectively improve fixed success rate of narrow-lane ambiguity and reduce the adverse effects of gross error. Experimental results show that improved TCAR algorithm can guarantee high fixed correct rate of wide-lane ambiguity, effectively improve fixed success rate of narrow-lane ambiguity, and has good ability to resist gross error.

Invulnerability is not the only factor of importance when considering complex networks’ security. It is also critical to have an effective and reasonable repair strategy. Existing research on network repair is confined to the static model. The dynamic model makes better use of the redundant capacity of repaired nodes and repairs the damaged network more efficiently than the static model; however, the dynamic repair model is complex and polytropic. In this paper, we construct a dynamic repair model and systematically describe the energy-transfer relationships between nodes in the repair process of the failure network. Nodes are divided into three types, corresponding to three structures. We find that the strong coupling structure is responsible for secondary failure of the repaired nodes and propose an algorithm that can select the most suitable targets (nodes or links) to repair the failure network with minimal cost. Two types of repair strategies are identified, with different effects under the two energy-transfer rules. The research results enable a more flexible approach to network repair.

Studying attack strategy of complex networks is the basis of investigating network characteristics such as robustness, invulnerability, and network security. Knowing means of attack can help us take more effective measures to ensure network security. Presently, most research conclusions focus on a single vertex being attacked, and the choice of a set of attack nodes is also limited to a complete understanding of network information. In this paper, considering the effect of cascading failure, we focus on the multi-node attack strategy. Our results showed that the distance between attack targets has a great effect on the attacking effect. Taking both the average avalanche scale and maximum destruction size into account, when the distance between attack targets was 2, the network suffered the most serious damage. If the information about the network was unclear, we presented 3 kinds of conditional attack strategies. Under the condition of different tolerance coefficients and different degrees of known information, each strategy had its own unique advantages. In conclusion, the research in this paper supports the easy and quick selection of attack targets under the condition of incomplete information.

The important status of critical infrastructure makes it a target of attacks in the new era. Based on game theory, we analyze the offensive and defensive issues of critical infrastructure from a network perspective, and we propose a network offensive and defensive game model considering the impact of the cascade effect. We first establish a two-player game model for an attacker and a defender acting simultaneously. The nodes are categorized to achieve the transformation of the focus of the game from decision-making for the nodes to resource allocation, thereby simplifying the calculation space of the network game strategy. Both pure strategy and mixed strategy are carried out for the theoretical analysis and simulation of four kinds of optimization problems. With the pure strategy, the initial decision equilibrium is that the attacker's strategy is to attack the Equal Cost-Effective ratio nodes (ECE nodes), and the defender defends the High Cost-Efficient ratio nodes (HCE nodes). At this point, the defender's payoff is 0. With the mixed strategy, different optimization problems have different equilibrium solutions. In particular, the attacker adds a new equilibrium solution for the strategy with the cascading effect, which is related to the structural properties of the network. For the defender, the strategy is different when maximizing his/her own profit or minimizing the attacker's benefit. This can explain how the attacker and the defender can achieve a win-win situation.

The importance of critical infrastructure makes it a target of attacks in the new era. Using the Stackelberg game model, we analyze the offensive and defensive security issues of critical infrastructure from a network perspective. Considering the impact of cascading failure, nodes are divided into two categories according to the cost-efficiency ratio. High cost-efficiency (HCE) nodes are the initial protection nodes, and each initial protection node sets up an agent to manage the defense resources. The defense resources can achieve two effects: protection and camouflage. Complete defense can avoid failure after being attacked. Partial defenses can avoid the attacker's attack by camouflage, and the effect of camouflage is related to the amount of defense resources. We propose an evolution model to realize the optimal allocation of resources through the evolutionary game of the agents, and analyze the influence of three parameters on evolution. The results show that the initial willingness of the agent will affect the result of evolution only when it is small. If the willingness exceeds a critical value, the result of evolution is only affected by the penalty coefficient and camouflage coefficient. To optimize the overall defense effect, the penalty coefficient and camouflage coefficient must cooperate, which is explained by the causal relationship between the agent's behavior and decisions.