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.
With the development of positioning technology, location services are constantly in demand by people. As a primary location service pedestrian navigation has two main approaches based on radio and inertial navigation. The pedestrian navigation based on radio is subject to environmental occlusion leading to the degradation of positioning accuracy. The pedestrian navigation based on micro-electro-mechanical system inertial measurement unit (MIMU) is less susceptible to environmental interference, but its errors dissipate over time. In this paper, a chest card pedestrian navigation improvement method based on complementary correction is proposed in order to suppress the error divergence of inertial navigation methods. To suppress attitude errors, optimal feedback coefficients are established by pedestrian motion characteristics. To extend navigation time and improve positioning accuracy, the step length in subsequent movements is compensated by the first step length. The experimental results show that the positioning accuracy of the proposed method is improved by more than 47% and 44% compared with the pure inertia-based method combined with step compensation and the traditional complementary filtering combined method with step compensation. The proposed method can effectively suppress the error dispersion and improve the positioning accuracy.
To investigate the real-time mean orbital elements (MOEs) estimation problem under the influence of state jumping caused by non-fatal spacecraft collision or protective orbit transfer, a modified augmented square-root unscented Kalman filter (MASUKF) is proposed. The MASUKF is composed of sigma points calculation, time update, modified state jumping detection, and measurement update. Compared with the filters used in the existing literature on MOEs estimation, it has three main characteristics. Firstly, the state vector is augmented from six to nine by the added thrust acceleration terms, which makes the filter additionally give the state-jumping-thrust-acceleration estimation. Secondly, the normalized innovation is used for state jumping detection to set detection threshold concisely and make the filter detect various state jumping with low latency. Thirdly, when sate jumping is detected, the covariance matrix inflation will be done, and then an extra time update process will be conducted at this time instance before measurement update. In this way, the relatively large estimation error at the detection moment can significantly decrease. Finally, typical simulations are performed to illustrated the effectiveness of the method.
The industrial Internet of Things (IIoT) is a new industrial idea that combines the latest information and communication technologies with the industrial economy. In this paper, a cloud control structure is designed for IIoT in cloud-edge environment with three modes of 5G. For 5G based IIoT, the time sensitive network (TSN) service is introduced in transmission network. A 5G logical TSN bridge is designed to transport TSN streams over 5G framework to achieve end-to-end configuration. For a transmission control protocol (TCP) model with nonlinear disturbance, time delay and uncertainties, a robust adaptive fuzzy sliding mode controller (AFSMC) is given with control rule parameters. IIoT workflows are made up of a series of subtasks that are linked by the dependencies between sensor datasets and task flows. IIoT workflow scheduling is a non-deterministic polynomial (NP)-hard problem in cloud-edge environment. An adaptive and non-local-convergent particle swarm optimization (ANCPSO) is designed with nonlinear inertia weight to avoid falling into local optimum, which can reduce the makespan and cost dramatically. Simulation and experiments demonstrate that ANCPSO has better performances than other classical algorithms.
The observation error model of the underwater acoustic positioning system is an important factor to influence the positioning accuracy of the underwater target. For the position inconsistency error caused by considering the underwater target as a mass point, as well as the observation system error, the traditional error model best estimation trajectory (EMBET) with little observed data and too many parameters can lead to the ill-condition of the parameter model. In this paper, a multi-station fusion system error model based on the optimal polynomial constraint is constructed, and the corresponding observation system error identification based on improved spectral clustering is designed. Firstly, the reduced parameter unified modeling for the underwater target position parameters and the system error is achieved through the polynomial optimization. Then a multi-station non-oriented graph network is established, which can address the problem of the inaccurate identification for the system errors. Moreover, the similarity matrix of the spectral clustering is improved, and the iterative identification for the system errors based on the improved spectral clustering is proposed. Finally, the comprehensive measured data of long baseline lake test and sea test show that the proposed method can accurately identify the system errors, and moreover can improve the positioning accuracy for the underwater target positioning.
Missile interception problem can be regarded as a two-person zero-sum differential games problem, which depends on the solution of Hamilton-Jacobi-Isaacs (HJI) equation. It has been proved impossible to obtain a closed-form solution due to the nonlinearity of HJI equation, and many iterative algorithms are proposed to solve the HJI equation. Simultaneous policy updating algorithm (SPUA) is an effective algorithm for solving HJI equation, but it is an on-policy integral reinforcement learning (IRL). For online implementation of SPUA, the disturbance signals need to be adjustable, which is unrealistic. In this paper, an off-policy IRL algorithm based on SPUA is proposed without making use of any knowledge of the systems dynamics. Then, a neural-network based online adaptive critic implementation scheme of the off-policy IRL algorithm is presented. Based on the online off-policy IRL method, a computational intelligence interception guidance (CIIG) law is developed for intercepting high-maneuvering target. As a model-free method, intercepting targets can be achieved through measuring system data online. The effectiveness of the CIIG is verified through two missile and target engagement scenarios.
The laser-guided bomb (LGB) is an air-to-ground precision-guided weapon that offers high hit rates, great power, and ease of use. LGBs are guided by semi-active laser ground-seeking technology, which means that atmospheric conditions can affect their accuracy. The spatial release region (SRR) of LGBs is difficult to calculate precisely, especially when there is a poor field of view. This can result in a lower real hit probability. To increase the hit probability of LGBs in tough atmospheric situations, a novel method for calculating the SRR has been proposed. This method is based on the transmittance model of the 1.06 μm laser in atmospheric species and the laser diffuse reflection model of the target surface to determine the capture target time of the laser seeker. Then, it calculates the boundary ballistic space starting position by ballistic model and gets the spatial scope of the spatial release region. This method can determine the release region of LGBs based on flight test data such as instantaneous velocity, altitude, off-axis angle, and atmospheric visibility. By more effectively employing aircraft release conditions, atmospheric visibility and other factors, the SRR calculation method can improve LGB hit probability by 9.2%.
In this study, the problem of measuring noise pollution distribution by the intertial-based integrated navigation system is effectively suppressed. Based on nonlinear inertial navigation error modeling, a nested dual Kalman filter framework structure is developed. It consists of unscented Kalman filter (UKF) master filter and Kalman filter slave filter. This method uses nonlinear UKF for integrated navigation state estimation. At the same time, the exact noise measurement covariance is estimated by the Kalman filter dependency filter. The algorithm based on dual adaptive UKF (Dual-AUKF) has high accuracy and robustness, especially in the case of measurement information interference. Finally, vehicle-mounted and ship-mounted integrated navigation tests are conducted. Compared with traditional UKF and the Sage-Husa adaptive UKF (SH-AUKF), this method has comparable filtering accuracy and better filtering stability. The effectiveness of the proposed algorithm is verified.
Tracking the fast-moving object in occlusion situations is an important research topic in computer vision. Despite numerous notable contributions have been made in this field, few of them simultaneously incorporate both object’s extrinsic features and intrinsic motion patterns into their methodologies, thereby restricting the potential for tracking accuracy improvement. In this paper, on the basis of efficient convolution operators (ECO) model, a speed-accuracy-balanced model is put forward. This model uses the simple correlation filter to track the object in real-time, and adopts the sophisticated deep-learning neural network to extract high-level features to train a more complex filter correcting the tracking mistakes, when the tracking state is judged to be poor. Furthermore, in the context of scenarios involving regular fast-moving, a motion model based on Kalman filter is designed which greatly promotes the tracking stability, because this motion model could predict the object’s future location from its previous movement pattern. Additionally, instead of periodically updating our tracking model and training samples, a constrained condition for updating is proposed, which effectively mitigates contamination to the tracker from the background and undesirable samples avoiding model degradation when occlusion happens. From comprehensive experiments, our tracking model obtains better performance than ECO on object tracking benchmark 2015 (OTB100), and improves the area under curve (AUC) by about 8% and 32% compared with ECO, in the scenarios of fast-moving and occlusion on our own collected dataset.
Detumbling operation toward a rotating target with nutation is meaningful for debris removal but challenging. In this study, a deformable end-effector is first designed based on the requirements for contacting the nutating target. A dual-arm robotic system installed with the deformable end-effectors is modeled and the movement of the end-tips is analyzed. The complex operation of the contact toward a nutating target places strict requirements on control accuracy and controller robustness. Thus, an improvement of the tracking error transformation is proposed and an adaptive sliding mode controller with prescribed performance is designed to guarantee the fast and precise motion of the effector during the contact detumbling. Finally, by employing the proposed effector and the controller, numerical simulations are carried out to verify the effectiveness and efficiency of the contact detumbling toward a nutating target.
This study presents a kinematic calibration method for exoskeletal inertial motion capture (EI-MoCap) system with considering the random colored noise such as gyroscopic drift. In this method, the geometric parameters are calibrated by the traditional calibration method at first. Then, in order to calibrate the parameters affected by the random colored noise, the expectation maximization (EM) algorithm is introduced. Through the use of geometric parameters calibrated by the traditional calibration method, the iterations under the EM framework are decreased and the efficiency of the proposed method on embedded system is improved. The performance of the proposed kinematic calibration method is compared to the traditional calibration method. Furthermore, the feasibility of the proposed method is verified on the EI-MoCap system. The simulation and experiment demonstrate that the motion capture precision is significantly improved by 16.79% and 7.16% respectively in comparison to the traditional calibration method.
An algorithm to track multiple sharply maneuvering targets without prior knowledge about new target birth is proposed. These targets are capable of achieving sharp maneuvers within a short period of time, such as drones and agile missiles. The probability hypothesis density (PHD) filter, which propagates only the first-order statistical moment of the full target posterior, has been shown to be a computationally efficient solution to multitarget tracking problems. However, the standard PHD filter operates on the single dynamic model and requires prior information about target birth distribution, which leads to many limitations in terms of practical applications. In this paper, we introduce a nonzero mean, white noise turn rate dynamic model and generalize jump Markov systems to multitarget case to accommodate sharply maneuvering dynamics. Moreover, to adaptively estimate newborn targets’ information, a measurement-driven method based on the recursive random sampling consensus (RANSAC) algorithm is proposed. Simulation results demonstrate that the proposed method achieves significant improvement in tracking multiple sharply maneuvering targets with adaptive birth estimation.
Angles-only relative orbit determination for space non-cooperative targets based on passive sensor is subject to weakly observable problem of the relative state between two spacecraft. Previously, the evidence for angles-only observability was found by using cylindrical dynamics, however, the solution of orbit determination is still not provided. This study develops a relative orbit determination algorithm with the cylindrical dynamics based on differential evolution. Firstly, the relative motion dynamics and line-of-sight measurement model for near-circular orbit are established in cylindrical coordinate system. Secondly, the observability is qualitatively analyzed by using the dynamics and measurement model where the unobservable geometry is found. Then, the angles-only relative orbit determination problem is modeled into an optimal searching frame and an improved differential evolution algorithm is introduced to solve the problem. Finally, the proposed algorithm is verified and tested by a set of numerical simulations in the context of high-Earth and low-Earth cases. The results show that initial relative orbit determination (IROD) solution with an appropriate accuracy in a relative short span is achieved, which can be used to initialize the navigation filter.
