In order to enhance the dynamic control precision of inertial stabilization platform (ISP), a disturbance sliding mode observer (DSMO) is proposed in this paper suppressing disturbance torques inherent within the system. The control accuracy of ISP is fundamentally circumscribed by various disturbance torques in rotating shaft. Therefore, a dynamic model of ISP incorporating composite perturbations is established with regard to the stabilization of axis in the inertial reference frame. Subsequently, an online estimator for control loop uncertainties based on the sliding mode control algorithm is designed to estimate the aggregate disturbances of various parameters uncertainties and other unmodeled disturbances that cannot be accurately calibrated. Finally, the proposed DSMO is integrated into a classical proportional-integral-derivative (PID) control scheme, utilizing feedforward approach to compensate the composite disturbance in the control loop online. The effectiveness of the proposed disturbance observer is validated through simulation and hardware experimentation, demonstrating a significant improvement in the dynamic control performance and robustness of the classical PID controller extensively utilized in the field of engineering.
This work proposes the application of an iterative learning model predictive control (ILMPC) approach based on an adaptive fault observer (FOBILMPC) for fault-tolerant control and trajectory tracking in air-breathing hypersonic vehicles. In order to increase the control amount, this online control legislation makes use of model predictive control (MPC) that is based on the concept of iterative learning control (ILC). By using offline data to decrease the linearized model’s faults, the strategy may effectively increase the robustness of the control system and guarantee that disturbances can be suppressed. An adaptive fault observer is created based on the suggested ILMPC approach in order to enhance overall fault tolerance by estimating and compensating for actuator disturbance and fault degree. During the derivation process, a linearized model of longitudinal dynamics is established. The suggested ILMPC approach is likely to be used in the design of hypersonic vehicle control systems since numerical simulations have demonstrated that it can decrease tracking error and speed up convergence when compared to the offline controller.
In order to get rid of the dependence on high-precision centrifuges in accelerometer nonlinear coefficients calibration, this paper proposes a system-level calibration method for field condition. Firstly, a 42-dimension Kalman filter is constructed to reduce impact brought by turntable. Then, a biaxial rotation path is designed based on the accelerometer output model, including orthogonal 22 positions and tilt 12 positions, which enhances gravity excitation on nonlinear coefficients of accelerometer. Finally, sampling is carried out for calibration and further experiments. The results of static inertial navigation experiments lasting 4000 s show that compared with the traditional method, the proposed method reduces the position error by about 390 m.
Visual inertial odometry (VIO) problems have been extensively investigated in recent years. Existing VIO methods usually consider the localization or navigation issues of robots or autonomous vehicles in relatively small areas. This paper considers the problem of vision-aided inertial navigation (VIN) for aircrafts equipped with a strapdown inertial navigation system (SINS) and a downward-viewing camera. This is different from the traditional VIO problems in a larger working area with more precise inertial sensors. The goal is to utilize visual information to aid SINS to improve the navigation performance. In the multi-state constraint Kalman filter (MSCKF) framework, we introduce an anchor frame to construct necessary models and derive corresponding Jacobians to implement a VIN filter to directly update the position in the Earth-centered Earth-fixed (ECEF) frame and the velocity and attitude in the local level frame by feature measurements. Due to its filtering-based property, the proposed method is naturally low computational demanding and is suitable for applications with high real-time requirements. Simulation and real-world data experiments demonstrate that the proposed method can considerably improve the navigation performance relative to the SINS.
To solve the problem of providing the best initial situation for terminal guidance when multiple missiles intercept multiple targets, a group cooperative midcourse guidance law (GCMGL) considering time-to-go is proposed. Firstly, a three-dimensional (3D) guidance model is established and a cooperative trajectory shaping guidance law is given. Secondly, for estimating the unknown target maneuvering acceleration, an adaptive disturbance observer (ADO) is designed, combining finite-time theory with a radial basis function (RBF) neural network, and the convergence of the estimation error is proven using Lyapunov stability theory. Then, to ensure time-to-go cooperation among missiles within the same group and across different groups, the group consensus protocols of virtual collision point mean and the inter-group cooperative consensus protocol are designed respectively. Based on the group consensus protocols, the virtual collision point cooperative guidance law is given, and the finite-time convergence is proved by Lyapunov stability theory. Simultaneously, combined with trajectory shaping guidance law, virtual collision point cooperative guidance law and the inter-group cooperative consensus protocol, the design of GCMGL considering time-to-go is given. Finally, numerical simulation results show the effectiveness and the superiority of the proposed GCMGL.
The influence of ocean environment on navigation of autonomous underwater vehicle (AUV) cannot be ignored. In the marine environment, ocean currents, internal waves, and obstacles are usually considered in AUV path planning. In this paper, an improved particle swarm optimization (PSO) is proposed to solve three problems, traditional PSO algorithm is prone to fall into local optimization, path smoothing is always carried out after all the path planning steps, and the path fitness function is so simple that it cannot adapt to complex marine environment. The adaptive inertia weight and the “active” particle of the fish swarm algorithm are established to improve the global search and local search ability of the algorithm. The cubic spline interpolation method is combined with PSO to smooth the path in real time. The fitness function of the algorithm is optimized. Five evaluation indexes are comprehensively considered to solve the three-demensional (3D) path planning problem of AUV in the ocean currents and internal wave environment. The proposed method improves the safety of the path planning and saves energy.
As the core component of inertial navigation systems, fiber optic gyroscope (FOG), with technical advantages such as low power consumption, long lifespan, fast startup speed, and flexible structural design, are widely used in aerospace, unmanned driving, and other fields. However, due to the temperature sensitivity of optical devices, the influence of environmental temperature causes errors in FOG, thereby greatly limiting their output accuracy. This work researches on machine-learning based temperature error compensation techniques for FOG. Specifically, it focuses on compensating for the bias errors generated in the fiber ring due to the Shupe effect. This work proposes a composite model based on k-means clustering, support vector regression, and particle swarm optimization algorithms. And it significantly reduced redundancy within the samples by adopting the interval sequence sample. Moreover, metrics such as root mean square error (RMSE), mean absolute error (MAE), bias stability, and Allan variance, are selected to evaluate the model’s performance and compensation effectiveness. This work effectively enhances the consistency between data and models across different temperature ranges and temperature gradients, improving the bias stability of the FOG from 0.022 °/h to 0.006 °/h. Compared to the existing methods utilizing a single machine learning model, the proposed method increases the bias stability of the compensated FOG from 57.11% to 71.98%, and enhances the suppression of rate ramp noise coefficient from 2.29% to 14.83%. This work improves the accuracy of FOG after compensation, providing theoretical guidance and technical references for sensors error compensation work in other fields.
This paper investigates the sliding-mode-based fixed-time distributed average tracking (DAT) problem for multiple Euler-Lagrange systems in the presence of external disturbances. The primary objective is to devise controllers for each agent, enabling them to precisely track the average of multiple time-varying reference signals. By averaging these signals, we can mitigate the influence of errors and uncertainties arising during measurements, thereby enhancing the robustness and stability of the system. A distributed fixed-time average estimator is proposed to estimate the average value of global reference signals utilizing local information and communication with neighbors. Subsequently, a fixed-time sliding mode controller is introduced incorporating a state-dependent sliding mode function coupled with a variable exponent coefficient to achieve distributed average tracking of reference signals, and rigorous analytical methods are employed to substantiate the fixed-time stability. Finally, numerical simulation results are provided to validate the effectiveness of the proposed methodology, offering insights into its practical application and robust performance.
For air-to-air missiles, the terminal guidance’s precision is directly contingent upon the tracking capabilities of the roll-pitch seeker. This paper presents a combined non-singular fast terminal sliding mode control method, aimed at resolving the frame control problem of roll-pitch seeker tracking high maneuvering target. The sliding mode surface is structured around the principle of segmentation, which enables the control system’s rapid attainment of the zero point and ensure global fast convergence. The system’s state is more swiftly converged to the sliding mode surface through an improved adaptive fast dual power reaching law. Utilizing an extended state observer, the overall disturbance is both identified and compensated. The validation of the system’s stability and its convergence within a finite-time is grounded in Lyapunov’s stability criteria. The performance of the introduced control method is confirmed through roll-pitch seeker tracking control simulation. Data analysis reveals that newly proposed control technique significantly outperforms existing sliding mode control methods by rapidly converging the frame to the target angle, reduce the tracking error of the detector for the target, and bolster tracking precision of the roll-pitch seeker huring disturbed conditions.
In the existing impact time control guidance (ITCG) laws for moving-targets, the effects of time-varying velocity caused by aerodynamics and gravity cannot be effectively considered. Therefore, an ITCG with field-of-view (FOV) constraints based on biased proportional navigation guidance (PNG) is developed in this paper. The remaining flight time (time-to-go) estimation method is derived considering aerodynamic force and gravity. The number of differential equations is reduced and the integration step is increased by changing the integral variable, which makes it possible to obtain time-to-go through integration. An impact time controller with FOV constraints is proposed by analyzing the influence of the biased term on time-to-go and FOV constraint. Then, numerical simulations are performed to verify the correctness and superiority of the method.
This paper presents a fixed-time cooperative guidance method with impact angle constraints for multiple flight vehicles (MFV) to address the challenges of intercepting large maneuvering targets with difficulty and low precision. A cooperative guidance model is proposed, transforming the cooperative interception problem into a consensus problem based on the remaining flight time of the flight vehicles. First, the impact angle constraint is converted into the line of sight (LOS) angle constraint, and a new fixed-time convergent non-singular terminal sliding surface is introduced, which resolves the singularity issue of the traditional sliding surfaces. With this approach, LOS angle rate and normal overloads can converge in fixed time, ensuring that the upper bound of the system convergence time is not affected by the initial value of the system. Furthermore, the maneuvering movement of the target is considered as a system disturbance, and an extended state observer is employed to estimate and compensate for it in the guidance law. Lastly, by applying consensus theory and distributed communication topology, the remaining flight time of each flight vehicle is synchronized to ensure that they intercept the target simultaneously with different impact angles. Simulation experiments are conducted to validate the effectiveness of the proposed cooperative interception and guidance method.
The process of ground vehicle dynamic gravimetry is inevitably affected by the carrier’s maneuvering acceleration, which makes the result contain a large amount of dynamic error. In this paper, we propose a dynamic error suppression method of gravimetry based on the high-precision acquisition of external velocity for compensating the horizontal error of the inertial platform. On the basis of platform gravity measurement, firstly, the dynamic performance of the system is enhanced by optimizing the horizontal damping network of the inertial platform and selecting its parameter. Secondly, an improved federal Kalman filtering algorithm and a fault diagnosis method are designed using strapdown inertial navigation system (SINS), odometer (OD), and laser Doppler velocimeter (LDV). Simulation validates that these methods can improve the accuracy and robustness of the external velocity acquisition. Three survey lines are selected in Tianjin, China, for the gravimetry experiments with different maneuvering levels, and the results demonstrate that after dynamic error suppression, the internal coincidence accuracies of smooth and uniform operation, obvious acceleration and deceleration operation, and high-dynamic operation are improved by 70.2%, 73.6%, and 77.9% to reach 0.81 mGal, 1.30 mGal, and 1.94 mGal, respectively, and the external coincidence accuracies during smooth and uniform operation are improved by 48.6% up to 1.66 mGal. It is shown that the proposed method can effectively suppress the dynamic error, and that the accuracy improvement increases with carrier maneuverability. However, the amount of residual error that can not be entirely eliminated increases as well, so the ground vehicle dynamic gravimetry should be maintained in the carrier for smooth and uniform operation.
Vibration-induced bias deviation, which is generated by intensity fluctuations and additional phase differences, is one of the vital errors for fiber optic gyroscopes (FOGs) operating in vibration environment and has severely restricted the applications of high-precision FOGs. The conventional methods for suppressing vibration-induced errors mostly concentrate on reinforcing the mechanical structure and optical path as well as the compensation under some specific operation parameters, which have very limited effects for high-precision FOGs maintaining performances under vibration. In this work, a technique of suppressing the vibration-induced bias deviation through removing the part related to the varying gain from the rotation-rate output is put forward. Particularly, the loop gain is extracted out by adding a gain-monitoring wave. By demodulating the loop gain and the rotation rate simultaneously under distinct frequencies and investigating their quantitative relationship, the vibration-induced bias error is compensated without limiting the operating parameters or environments, like the applied modulation depth. The experimental results show that the proposed method has achieved the reduction of bias error from about 0.149°/h to 0.014°/h during the random vibration with frequencies from 20 Hz to 2000 Hz. This technique provides a feasible route for enhancing the performances of high-precision FOGs heading towards high environmental adaptability.
When the maneuverability of a pursuer is not significantly higher than that of an evader, it will be difficult to intercept the evader with only one pursuer. Therefore, this article adopts a two-to-one differential game strategy, the game of kind is generally considered to be angle-optimized, which allows unlimited turns, but these practices do not take into account the effect of acceleration, which does not correspond to the actual situation, thus, based on the angle-optimized, the acceleration optimization and the acceleration upper bound constraint are added into the game for consideration. A two-to-one differential game problem is proposed in the three-dimensional space, and an improved multi-objective grey wolf optimization (IMOGWO) algorithm is proposed to solve the optimal game point of this problem. With the equations that describe the relative motions between the pursuers and the evader in the three-dimensional space, a multi-objective function with constraints is given as the performance index to design an optimal strategy for the differential game. Then the optimal game point is solved by using the IMOGWO algorithm. It is proved based on Markov chains that with the IMOGWO, the Pareto solution set is the solution of the differential game. Finally, it is verified through simulations that the pursuers can capture the escapee, and via comparative experiments, it is shown that the IMOGWO algorithm performs well in terms of running time and memory usage.
This paper addresses the time-varying formation-containment (FC) problem for nonholonomic multi-agent systems with a desired trajectory constraint, where only the leaders can acquire information about the desired trajectory. Input the fixed time-varying formation template to the leader and start executing, this process also needs to track the desired trajectory, and the follower needs to converge to the convex hull that the leader crosses. Firstly, the dynamic models of nonholonomic systems are linearized to second-order dynamics. Then, based on the desired trajectory and formation template, the FC control protocols are proposed. Sufficient conditions to achieve FC are introduced and an algorithm is proposed to resolve the control parameters by solving an algebraic Riccati equation. The system is demonstrated to achieve FC, with the average position and velocity of the leaders converging asymptotically to the desired trajectory. Finally, the theoretical achievements are verified in simulations by a multi-agent system composed of virtual human individuals.
Enhancing the stability and performance of practical control systems in the presence of nonlinearity, time delay, and uncertainty remains a significant challenge. Particularly, a class of strict-feedback nonlinear uncertain systems characterized by unknown control directions and time-varying input delay lacks comprehensive solutions. In this paper, we propose an observer-based adaptive tracking controller to address this gap. Neural networks are utilized to handle uncertainty, and a unique coordinate transformation is employed to untangle the coupling between input delay and unknown control directions. Subsequently, a new auxiliary signal counters the impact of time-varying input delay, while a Nussbaum function is introduced to solve the problem of unknown control directions. The leverage of an advanced dynamic surface control technique avoids the “complexity explosion” and reduces boundary layer errors. Synthesizing these techniques ensures that all the closed-loop signals are semi-globally uniformly ultimately bounded (SGUUB), and the tracking error converges to a small region around the origin by selecting suitable parameters. Simulation examples are provided to demonstrate the feasibility of the proposed approach.
To better complete various missions, it is necessary to plan an optimal trajectory or provide the optimal control law for the multirole missile according to the actual situation, including launch conditions and target location. Since trajectory optimization struggles to meet real-time requirements, the emergence of data-based generation methods has become a significant focus in contemporary research. However, due to the large differences in the characteristics of the optimal control laws caused by the diversity of tasks, it is difficult to achieve good prediction results by modeling all data with one single model. Therefore, the modeling idea of the mixture of experts (MoE) is adopted. Firstly, the K-means clustering algorithm is used to partition the sample data set, and the corresponding neural network classification model is established as the gate switch of MoE. Then, the expert models, i.e., the mappings from the generation conditions to the optimal control law represented by the results of principal component analysis (PCA), are represented by Kriging models. Finally, multiple rounds of accuracy evaluation, sample supplementation, and model updating are conducted to improve the generation accuracy. The Monte Carlo simulation shows that the accuracy of the proposed model reaches 96% and the generation efficiency meets the real-time requirement.
In the field of calculating the attack area of air-to-air missiles in modern air combat scenarios, the limitations of existing research, including real-time calculation, accuracy efficiency trade-off, and the absence of the three-dimensional attack area model, restrict their practical applications. To address these issues, an improved backtracking algorithm is proposed to improve calculation efficiency. A significant reduction in solution time and maintenance of accuracy in the three-dimensional attack area are achieved by using the proposed algorithm. Furthermore, the age-layered population structure genetic programming (ALPS-GP ) algorithm is introduced to determine an analytical polynomial model of the three-dimensional attack area, considering real-time requirements. The accuracy of the polynomial model is enhanced through the coefficient correction using an improved gradient descent algorithm. The study reveals a remarkable combination of high accuracy and efficient real-time computation, with a mean error of 91.89 m using the analytical polynomial model of the three-dimensional attack area solved in just 10?4 s, thus meeting the requirements of real-time combat scenarios.
With the advantage of exceptional long-range traffic perception capabilities and data fusion computational prowess, the cloud control system (CCS) has exhibited formidable potential in the realm of connected assisted driving, such as the adaptive cruise control (ACC). Based on the CCS architecture, this paper proposes a cloud-based predictive ACC (PACC) strategy, which fully considers the road slope information and the preceding vehicle status. In the cloud, based on the dynamic programming (DP), the long-term economic speed planning is carried out by using the slope information. At the vehicle side, the real-time fusion planning of the economic speed and the preceding vehicle state is realized based on the model predictive control (MPC), taking into account the safety and economy of driving. In order to ensure the safety and stability of the vehicle-cloud cooperative control system, an event-triggered cruise mode switching method is proposed based on the state of each subsystem of the vehicle-cloud-network-map. Simulation results indicate that the PACC system can still ensure stable cruising under delays and some complex conditions. Moreover, under normal conditions, compared to the ACC system, the PACC system can further improve economy while ensuring safety and improve the overall energy efficiency of the vehicle, thus achieving fuel savings of 3% to 8%.
This paper mainly focuses on stability analysis of the nonlinear active disturbance rejection control (ADRC)-based control system and its applicability to real world engineering problems. Firstly, the nonlinear ADRC(NLADRC)-based control system is transformed into a multi-input multi-output (MIMO) Lurie-like system, then sufficient condition for absolute stability based on linear matrix inequality (LMI) is proposed. Since the absolute stability is a kind of global stability, Lyapunov stability is further considered. The local asymptotical stability can be determined by whether a matrix is Hurwitz or not. Using the inverted pendulum as an example, the proposed methods are verified by simulation and experiment, which show the valuable guidance for engineers to design and analyze the NL ADRC-based control system.
For the multicopter with more than four rotors, the rotor fault information is unobservable, which limits the application of active fault-tolerant on multicopters. This paper applies an existing fault-tolerant control method for quadcopter to multicopter with more than four rotors. Without relying on rotor fault information, this method is able to stabilize the multicopter with multiple rotor failures, which is validated on the hexacopter and octocopter using the hardware-in-the-loop simulations. Additionally, the hardware-in-the-loop simulations demonstrate that a more significant tilt angle in flight will inhibit the maximum tolerable number of rotor failures of a multicopter. The more significant aerodynamic drag moment will make it difficult for the multicopter to regain altitude control after rotor failure.
Small video satellites have unique advantages of short development cycle, agile attitude maneuver, real-time video imaging. They have broad application prospects in space debris, faulty spacecraft, and other space target detection and tracking. However, when a space target first enters the camera’s visual field, it has a relatively large angular velocity relative to the satellite, which makes it easy to deviate from the visual field and cause off-target problems. This paper proposes a novel visual tracking control method based on potential function preventing missed targets in space. Firstly, a circular area in the image plane is designed as a mandatory restricted projection area of the target and a visual tracking controller based on image error. Then, a potential function is designed to ensure continuous and stable tracking of the target after entering the visual field. Finally, the stability of the control is proved using Barbarat’s lemma. By setting the same conditions and comparing with the simulation results of the proportion-derivative (PD) control method, the results show that when there is a large relative attitude motion angular velocity between the target and the satellite, the tracking method based on potential function can ensure that the target does not deviate from the field-of-view during the tracking control process, and the projection of target is controlled to the desired position. The proposed control method is effective in eliminating tracking error and preventing off-target simultaneously.
In consideration of the field-of-view (FOV) angle constraint, this study focuses on the guidance problem with impact time control. A deep reinforcement learning guidance method is given for the missile to obtain the desired impact time and meet the demand of FOV angle constraint. On basis of the framework of the proportional navigation guidance, an auxiliary control term is supplemented by the distributed deep deterministic policy gradient algorithm, in which the reward functions are developed to decrease the time-to-go error and improve the terminal guidance accuracy. The numerical simulation demonstrates that the missile governed by the presented deep reinforcement learning guidance law can hit the target successfully at appointed arrival time.
To meet the requirements of modern air combat, an integrated fire/flight control (IFFC) system is designed to achieve automatic precision tracking and aiming for armed helicopters and release the pilot from heavy target burden. Considering the complex dynamic characteristics and the couplings of armed helicopters, an improved automatic attack system is constructed to integrate the fire control system with the flight control system into a unit. To obtain the optimal command signals, the algorithm is investigated to solve nonconvex optimization problems by the contracting Broyden Fletcher Goldfarb Shanno (C-BFGS) algorithm combined with the trust region method. To address the uncertainties in the automatic attack system, the memory nominal distribution and Wasserstein distance are introduced to accurately characterize the uncertainties, and the dual solvable problem is analyzed by using the duality theory, conjugate function, and dual norm. Simulation results verify the practicality and validity of the proposed method in solving the IFFC problem on the premise of satisfactory aiming accuracy.
As a dynamic projection to latent structures (PLS) method with a good output prediction ability, dynamic inner PLS (DiPLS) is widely used in the prediction of key performance indicators. However, due to the oblique decomposition of the input space by DiPLS, there are false alarms in the actual industrial process during fault detection. To address the above problems, a dynamic modeling method based on autoregressive-dynamic inner total PLS (AR-DiTPLS) is proposed. The method first uses the regression relation matrix to decompose the input space orthogonally, which reduces useless information for the prediction output in the quality-related dynamic subspace. Then, a vector autoregressive model (VAR) is constructed for the prediction score to separate dynamic information and static information. Based on the VAR model, appropriate statistical indicators are further constructed for online monitoring, which reduces the occurrence of false alarms. The effectiveness of the method is verified by a Tennessee-Eastman industrial simulation process and a three-phase flow system.
To solve the finite-time error-tracking problem in missile guidance, this paper presents a unified design approach through error dynamics and free-time convergence theory. The proposed approach is initiated by establishing a desired model for free-time convergent error dynamics, characterized by its independence from initial conditions and guidance parameters, and adjustable convergence time. This foundation facilitates the derivation of specific guidance laws that integrate constraints such as leading angle, impact angle, and impact time. The theoretical framework of this study elucidates the nuances and synergies between the proposed guidance laws and existing methodologies. Empirical evaluations through simulation comparisons underscore the enhanced accuracy and adaptability of the proposed laws.
To solve the problem that multiple missiles should simultaneously attack unmeasurable maneuvering targets, a guidance law with temporal consistency constraint based on the super-twisting observer is proposed. Firstly, the relative motion equations between multiple missiles and targets are established, and the topological model among multiple agents is considered. Secondly, based on the temporal consistency constraint, a cooperative guidance law for simultaneous arrival with finite-time convergence is derived. Finally, the unknown target maneuvering is regarded as bounded interference. Based on the second-order sliding mode theory, a super-twisting sliding mode observer is devised to observe and track the bounded interference, and the stability of the observer is proved. Compared with the existing research, this approach only needs to obtain the sliding mode variable which simplifies the design process. The simulation results show that the designed cooperative guidance law for maneuvering targets achieves the expected effect. It ensures successful cooperative attacks, even when confronted with strong maneuvering targets.
Final velocity and impact angle are critical to missile guidance. Computationally efficient guidance law with comprehensive consideration of the two performance merits is challenging yet remains less addressed. Therefore, this paper seeks to solve a type of optimal control problem that maximizes final velocity subject to equality point constraint of impact angle constraint. It is proved that the crude problem of maximizing final velocity is equivalent to minimizing a quadratic-form cost of curvature. The closed-form guidance law is henceforth derived using optimal control theory. The derived analytical guidance law coincides with the widely-used optimal guidance law with impact angle constraint (OGL-IAC) with a set of navigation parameters of two and six. On this basis, the optimal emission angle is determined to further increase the final velocity. The derived optimal value depends solely on the initial line-of-sight angle and impact angle constraint, and thus practical for real-world applications. The proposed guidance law is validated by numerical simulation. The results show that the OGL-IAC is superior to the benchmark guidance laws both in terms of final velocity and missing distance.
The quantum entangled photon-pair source, as an essential component of optical quantum systems, holds great potential for applications such as quantum teleportation, quantum computing, and quantum imaging. The current workhorse technique for preparing photon pairs involves performing spontaneous parametric down conversion (SPDC) in bulk nonlinear crystals. However, the current power consumption and cost of preparing entangled photon-pair sources are relatively high, posing challenges to their integration and scalability. In this paper, we propose a low-power system model for the quantum entangled photon-pair source based on SPDC theory and phase matching technology. This model allows us to analyze the performance of each module and the influence of component characteristics on the overall system. In our experimental setup, we utilize a 5 mW laser diode and a typical type-II barium metaborate (BBO) crystal to prepare an entangled photon-pair source. The experimental results are in excellent agreement with the model, indicating a significant step towards achieving the goal of low-power and low-cost entangled photon-pair sources. This achievement not only contributes to the practical application of quantum entanglement lighting, but also paves the way for the widespread adoption of optical quantum systems in the future.
In this paper, a method for spoofing detection based on the variation of the signal’s carrier-to-noise ratio (CNR) is proposed. This method leverages the directionality of the antenna to induce varying gain changes in the signals across different incident directions, resulting in distinct CNR variations for each signal. A model is developed to calculate the variation value of the signal CNR based on the antenna gain pattern. This model enables the differentiation of the variation values of the CNR for authentic satellite signals and spoofing signals, thereby facilitating spoofing detection. The proposed method is capable of detecting spoofing signals with power and CNR similar to those of authentic satellite signals. The accuracy of the signal CNR variation value calculation model and the effectiveness of the spoofing detection method are verified through a series of experiments. In addition, the proposed spoofing detection method works not only for a single spoofing source but also for distributed spoofing sources.
