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In future naval warfare, the accurate attack of underwater intelligent vehicles on targets will become more and more important. Therefore, it is very important to study the guiding method which has the key influence on tracking targets. In the guidance process of intelligent navigation system, the estimation of guidance information by the agent is influenced by confrontation and interference, which often results in the decrease of the guidance accuracy of UUV under the single guidance law. In this paper, the estimated information is classified according to the different types of guidance target information combining to the advantages of three guidance methods, namely fixed advance angle, extended proportional guidance law and adaptive sliding mode guidance law. Then the fuzzy weight coefficient calculation method is used to get the weight coefficients of all kinds of information to form the adaptive weighted guidance law. The target tracking effects of each guidance method is simulated and compared in the simulation environment of the target maneuver and the target non-maneuver respectively.

The initial satellite telemetry data acquired by ground stations usually contain noise and outlier interference. In order to ensure the accurate analysis of satellite status, the telemetry data need to be filtered. In this paper, a sliding window optimal tracking differentiator filtering (SWOTDF) method for satellite telemetry data is proposed. Aiming at the problem of parameter selection during the filtering of the optimal tracking differentiator, the amplitude-frequency characteristics of the maximum tracking differentiator are analyzed by sine sweep frequency method, and the mapping relationship between tracking factors and filtering effects is established. On this basis, the telemetry data are divided by sliding windows, and the relationship between local stability of data in each window and tracking factors is further analyzed. The calculation method of local data tracking factor is given to realize dynamic optimal tracking differentiator filtering of telemetry data in each window. Experimental results show that the SWOTDF method can effectively avoid the limitations of traditional digital filters in processing nonlinear telemetry data, and can effectively filter out noise and outliers in satellite telemetry data.

When the target is tracked based on the current traditional reference point guidance method by standoff mode, if the angle between the relative speed of the UAV and the target and the tangential direction of the corresponding point on the circular expected flight path of UAV is close to 90 degrees, and UAV is close to the desired path, the efficiency of the UAV converging to the circular desired path is not high enough. In this paper, the small angle hypothesis for reference point guidance method tracking desired circular path is studied. According to this, we designed a maneuvering entry guiding strategy for UAV standoff tracking fixed/moving target based on reference point guidance method. Simulation results show effectiveness of the guidance strategy. UAV might quickly converge to the circular desired path.

Aiming at the static unstable Hypersonic vehicle, a method for designing H∞ autopilot to satisfy gain margin and phase margin is proposed. Structured H∞ synthesis is used to synthesize the autopilot under the fixed structure. The gain margin and phase margin were constrained by using H∞ norm of the complementary of the scaled plant. The requirement for the gain margin and phase margin is represented by the distance between the open loop Nyquist curve and the circle that represents the gain margin and phase margin in the complex plane. The distance is adjusted by tuning the parameter of performance specifications automatically to reduce the conservatism of the controller. The present method is applicable to both static stable vehicle and static unstable vehicle, and a three loops acceleration tracking autopilot is design. The numerical simulations have demonstrated that the autopilot satisfys the performance specifications, gain margin and phase margin simultaneously.

Considering the difficulty of carrier synchronization and the low tracking accuracy in high-dynamic environments, the joint open-loop acquisition and closed-loop tracking carrier synchronization algorithm is presented. Firstly, the algorithm derives the coarse estimation of Doppler frequency and Doppler rate via open-loop acquisition, then the residual Doppler frequency and Doppler rate are confined into a small range. Secondly, the residual Doppler rate is tracked by using a third-order phase-locked loop(PLL). The present algorithm has the advantages of fast acquisition and high tracking accuracy. Finally, the numerical simulation is conducted to verify the present synchronization algorithm. The simulation results indicate that at the signal to noise ratio(SNR) of and normalized Doppler frequency and Doppler rate ranging from to, the bit-error-rate(BER) performance degradation is as low as comparing with theoretical value.

The estimation of underwater multi-target state has always been the difficult problem of active sonar target tracking.In order to get the variable number of target and their state, the random finite set theory is applied to multi-target tracking system.This theory not only effectively avoids the problem of multi-target tracking data association, and also realizes the estimation of time-varying number of targets and their states.Due to Probability Hypothesis Density(PHD) recursion propagates cardnality distribution with only a single parameter, a new generalization of the PHD recursion called Cardinalized Probability Hypothesis Density(CPHD) recursion, which jointly propagates the intensity function and the cardnality distribution, while have a big computation than PHD.Also there did not have closed-form solution for PHD recursion and CPHD recursion, so for linear Gaussian multi-target tracking system, the Gaussian Mixture Probability Hypothesis Density and Gaussian Mixture Cardinalized Probability Hypothesis Density(GM-CPHD) filter algorithm is put forward.GM-CPHD is more accurate than GM-PHD in estimation of the time-varying number of targets.In this paper, we use the ellipse gate tracking strategy to reduce computation in GM-CPHD filtering algorithm.At the same time, according to the characteristics of underwater target tracking, using active sonar equation, we get the relationship between detection probability, distance and false alarm, when fixed false alarm, analytic formula of the relationship between adaptive detection probability and distance is obtained, we puts forward the adaptive detection probability GM-CPHD filtering algorithm.Simulation shows that the combination of ellipse tracking gate strategy and adaptive detection probability GM-CPHD filtering algorithm can realize the estimation of the time-varying number of targets and their state more accuracy in dense clutter environment.

To remove the funitional payload variation, the attitude oscillation and trajectory tracking error of civil UAV in complex environment, a combined inner and outer control strategy for the civil UAV is designed based on the interval system control theory and dynamic tracking error minimization principle, which can guarantee the attitude stabilization in inner loop and the heading tracking in outer loop, thus solving the control effect deterioration and trajectory error drawback of the existing UAV with large scale model variation. The simulation results show that, the proposed combined control strategy can keep flight state's stabilization and effective trajectory tracking with good closed-loop robustness. This study is useful to develop effective and automatic civil UAV system's flight control strategy.

For the issue of limited filtering accuracy of interactive multiple model particle filter algorithm caused by the resampling particles don't contain the latest observation information, we made improvements on interactive multiple model particle filter algorithm in this paper based on mixed kalman particle filter algorithm. Interactive multiple model particle filter algorithm is proposed. In addition, the composed methods influence to tracking accuracy are discussed. In the new algorithm the system state estimation is generated with unscented kalman filter (UKF) first and then use the extended kalman filter (EKF) to get the proposal distribution of the particles, taking advantage of the measure information to update the particles' state. We compare and analyze the target tracking performance of the proposed algorithm of IMM-MKPF in this paper, IMM-UPF and IMM-EPF through the simulation experiment. The results show that the tracking accuracy of the proposed algorithm is superior to other two algorithms. Thus, the new method in this paper is effective. The method is of important to improve tracking accuracy further for maneuvering target tracking under the non-linear and non-Gaussian circumstances.