Spacecraft autonomous relative navigation is widely required in the field of on-orbit services. In previous works, it has been shown that the monocular sequential images (MSIs) is a cost-effective autonomous relative navigation method. However, given the lack of size and initial orbit of non-cooperative targets, the relative navigation system (RNS) with MSIs cannot ensure to estimate the entire relative motion states during the whole observation process. It is critical to perform an online observability analysis for RNS with MSIs to ensure autonomous relative motion estimation validity and accuracy. An analytical nonlinear observability analysis method is proposed in this paper to improve the computational efficiency. We study the attributes of the relative orbit dynamics model and Lie algebra operations, and find that the observability matrix is only related to the limited states. The relationship among those states, rank, and the Frobenius norm of observability matrix is demonstrated, which simplify the observability criteria conditions and the degree measurements in analytical forms. In addition, based on the proposed method, we analyze the influence of the orbital manifolds on the observability degree of RNS with MSIs. Several suggestions are proposed for orbital manifolds detecting. The conclusion is a theoretical supplement to the past numerical research results. Finally, we implement the numerical simulations, and the current study’s findings verified the effectiveness of the proposed observability analysis method.

This study proposes a complete method for estimating the relative motion parameters of noncooperative targets in space and analyzes the system observability for the optically autonomous relative navigation problem of noncooperative targets in space. First, using the installation offset of the onboard monocular camera, a relative navigation system model is established herein by observing the three feature points of the target using the line-of-sight direction of the feature points as the measurement information and combining the target's relative orbital dynamics, attitude kinematics, and dynamics. Second, the system observability is analyzed based on the system observability matrix and combined with the null-space theory. The conditions under which the system does not satisfy the observability are analyzed through the definition of observability. An iterative extended Kalman filter based on this system model is then derived to realize the complete estimation of the target relative position, relative velocity, target attitude, angular velocity, inertia ratio, and position of the feature points under the target body frame. Finally, the effect of the camera offset on the system observability is analyzed through a numerical simulation. The results verify that an appropriate camera offset can make the monocular relative navigation system fully observable. The only existing unobservable state variable is found at 0 camera offset.

Due to the influence of the complex underwater environment, the sound speed constantly changes, resulting in the acoustic signal propagation trajectory being curved, which greatly affects the positioning accuracy of the underwater long baseline (LBL) system. In this paper, an improved LBL positioning method based on a B-spline surface for fitting the effective sound speed table (ESST) is proposed. Firstly, according to the underwater sound speed profile, the discrete ESST of each measurement station is constructed before the positioning test, and then, the node position of the B-spline surface is optimized by particle swarm optimization (PSO) to accurately fit the discrete ESST. Based on this, the improved LBL positioning method is constructed. In the underwater positioning test, the effective sound speed can be quickly found by measuring the time of arrival (TOA) of the acoustic signal and the target depth, and moreover, the target position parameters can be quickly and accurately estimated. The numerical simulation results show that the improved positioning method proposed in this paper can effectively improve the LBL positioning accuracy and provide the theoretical basis and the technical support for the underwater navigation and positioning.

Measurement anomalies may cause a sudden system performance degradation, especially in location systems based on time differences of arrivals (TDOA) due to its limited number of redundant measurement instruments. Conventional approaches mitigate measurement anomalies through data processing techniques such as anomaly detection and system reconfiguration algorithms. However, these approaches may prove inadequate when anomalies affect critical system configurations. This study presents an innovative reconfigurability-enhanced deployment to reduce degradation in the presence of abnormal measurements. First, we introduce the model, algorithm, and performance evaluation of the TDOA location, along with a geometric dilution of precision metric to quantify the impact of system deployment independently of measurement uncertainty. Then, we elaborate on measurement anomalies and the existed strategies for system diagnosis and reconfiguration. Following this, we provide detailed analyses of system degradation risks under various strategies. Subsequently, we establish reconfigurability to assess the system’s tolerance to anomalies. An adapted evolutionary algorithm is proposed to design a optimal deployment that enhances reconfigurability. Finally, in accordance with real applications, we conduct simulations and semi-physical experiments to evaluate the performance of different deployments under anomalies. The results demonstrate that the proposed deployment effectively mitigates degradation risks while maintaining system performance.

Underwater navigation practice shows that the long baseline survey has the characteristics of coplanar configuration, flat geometry, and large refraction error, which brings challenges to underwater positioning. To address this challenge, this paper proposes a high-precision real-time range-difference location algorithm based on underwater long baseline measurement. Firstly, the system error sources of long baseline positioning are analyzed in detail, the propagation models of different system errors are constructed, and the effects of system error sources on the rangefinder are described. Secondly, the limitations of traditional range iterative location algorithms and geometric analytic location algorithms in long baseline locations are analyzed. Then, using the strategy of converting the long baseline range information into the distance difference information, a high-precision real-time distance difference location algorithm based on long baseline measurement is presented. Finally, the feasibility of the algorithm is analyzed from the perspective of precision analysis. Numerical simulation results show that compared with the two traditional long-baseline positioning algorithms, the proposed algorithm has higher positioning accuracy and potential application value in the field of underwater real-time positioning.