A pulse phase estimation of an X-ray pulsar with the aid of vehicle orbital dynamics is proposed. The original continue-time X-ray pulsar signal model is modified to be a term of vehicle position and velocity varying with time, and a modified definition of pulse time of arrival is given. The modified signal model is further linearized around the predicted position and velocity of the vehicle to the second order. The initial phase and the coefficients of the extended signal model can be estimated by maximum likelihood estimator. Some simulations are performed to verify the method and show the method has robustness to the initial error within initial state of the vehicle and is capable of handling the phase-estimation problem for pulsars with low fluxes.

The recent flight experiments with Neutron Star Interior Composition Explorer ( NICER ) and Insight -Hard X-ray Modulation Telescope ( Insight -HXMT) have demonstrated the feasibility of X-ray pulsar-based navigation (XNAV) in the space. However, the current pulse phase estimation and navigation methods employed in the above flight experiments are computationally too expensive for handling the Crab pulsar data. To solve this problem, this paper proposes a fast algorithm of on-orbit estimating the pulse phase of Crab pulsar called X-ray pulsar navigaTion usIng on-orbiT pulsAr timiNg (XTITAN). The pulse phase propagation model for Crab pulsar data from Insight -HXMT and NICER are derived. When an exposure on the Crab pulsar is divided into several sub-exposures, we derive an on-orbit timing method to estimate the hyperparameters of the pulse phase propagation model. Moreover, XTITAN is improved by iteratively estimating the pulse phase and the position and velocity of satellite. When applied to the Crab pulsar data from NICER , XTITAN is 58 times faster than the grid search method employed by NICER experiment. When applied to the Crab pulsar data from Insight -HXMT, XTITAN is 180 times faster than the Significance Enhancement of Pulse-profile with Orbit-dynamics (SEPO) which was employed in the flight experiments with Insight -HXMT. Thus, XTITAN is computationally much efficient and has the potential to be employed for onboard computation.

This article focuses on the fully distributed dynamic event-triggered optimized backstepping (OB) containment control for a class of stochastic nonlinear high-order strict-feedback multiagent systems (MASs) based on reinforcement learning (RL) algorithm. First, an OB control scheme is proposed using the critic-actor structure. In this scheme, neural networks are applied to approximate the value function and the controllers are derived from the gradients of simple positive functions. This approach simplifies the algorithm and effectively addresses two fundamental requirements commonly encountered in RL-based optimal control: known dynamics and persistent excitation. Furthermore, the static and dynamic event-triggered mechanisms are designed in order to reduce the control actions in the highest layer for nonlinear high-order stochastic MASs. Then, the distributed event-triggered control protocols based on RL are proposed for the strict-feedback nonlinear MASs with stochastic disturbances. Finally, simulation results are shown to demonstrate the effectiveness of the theoretical analysis.

In this paper, the space debris catalogue in LEO is fulfilled using multipoint of view (MPOV) observations constructed by a spacecraft formation. The dual-point of view (DPOV) observation is employed because it has higher cost-benefit ratio than other MPOV methods, and it required that the target is detectable for multiple optical sensors simultaneously. This condition is matched by designing the orbit of formation and the installation angles of sensors without frequent attitude maneuvers. The design parameters are optimized to maximize the matching degree of observation constraints to improve the coverage performance of the formation, and the equivalent ranging error is taken into account to ensure the orbit determination accuracy. Finally, the system capabilities of different schemes are compared by simulations. For the schemes with the same number of platforms, simulation results show that the initial orbit determination accuracy of the proposed scheme is higher.

An X-ray pulsar/starlight Doppler deeply-integrated navigation method is proposed in this paper. A starlight Doppler measurement-aided phase propagation model, which can remove the orbital effect within the recorded photon Time Of Arrivals (TOAs), is derived, and guarantees that the pulse phase can be extracted from the converted photon TOAs using computationally efficient methods. Some simulations are performed by a hardware-in-loop system to verify the performance of the integrated pulse phase estimation method as well as of the integrated navigation method. The integrated pulse phase estimation method could achieve an estimation performance similar to the existing method for orbiting vehicles at the cost of much less computational complexity, is capable of handling the signals of millisecond pulsars, and is applicable to various vehicles. In addition, the proposed integrated navigation method could provide reliable positioning results for various vehicles.

Distributed state estimation plays a key role in space situation awareness via a sensor network. This paper proposes two adaptive consensus-based unscented information filters for tracking target with maneuver and colored measurement noise. The proposed filters can fulfill the distributed estimation for non-linear systems with the aid of a consensus strategy, and can reduce the impact of colored measurement noise by employing the state augmentation and measurement differencing methods. In addition, a fading factor that shrinks the predicted information state and information matrix can suppress the impact of dynamical model error induced by target maneuvers. The performances of the proposed algorithms are investigated by considering a target tracking problem using a space-based radar network. This shows that the proposed algorithms outperform the traditional consensus-based distributed state estimation method in aspects of tracking stability and accuracy.