This study focuses on optimizing the baseline selection for ground-based deep space network interferometry in deep space exploration missions.By establishing mathematical models,developing numerical simulations,and conducting sensitivity analysis,a systematic methodology is proposed for baseline selection strategies.Firstly,a geometric model for deep space interferometry was constructed by quantitatively analyzing the coupling relationships among baseline length,orientation,and orbital parameters.Subsequently,through sensitivity analysis,the comprehensive effects of the orbit type,observation geometry,and baseline parameters on orbit determination accuracy were systematically investigated.Finally,the effectiveness of optimizing the observations was evaluated.The research demonstrated that interferometric sensitivity is proportional to the projected baseline length,and orbit determination accuracy can be effectively enhanced by optimizing the baseline configuration schemes.
Interplanetary probes operate in weak-signal mode for an extended period before reaching their mission orbits,posing significant challenges to interferometry processing.To address this issue,a robust interferometry strategy for weak signal detection in interplanetary exploration is proposed based on the principles of interferometry technology and signal system mechanisms.First,a multi-signal fusion processing method for probe downlink signals was developed by leveraging the continuous transmission characteristics of downlink telemetry signals.Vector summation of the telemetry and carrier signals was utilized to enhance the processing accuracy of the carrier signals.Second,based on mathematical mechanisms,a carrier-frequency-aided delta-differential one-way ranging(DOR)beacon detection method was designed to improve the probability of successful DOR beacon detection.Third,the configuration of the correlation processing parameters was optimized by replacing the conventional ″short-time segment stacking″ integration time configuration strategy with a ″long-time segment stacking″ strategy.Under the premise of unchanged total integration time,this optimization enhances the accumulation capability of weak signals.Verification tests were conducted using the measured data from China's interplanetary probes.The results demonstrate that,after optimization of downlink signal fusion processing,the random error of the probe's residual time delay is reduced by approximately 10％.With carrier frequency assistance,the success rate of DOR beacon detection is significantly improved,and by optimizing the correlation processing parameter configuration,the cross-spectrum effectiveness of DOR beacons is clearly enhanced.Through comprehensively optimized processing,the validity rate of interferometric observables for weak signals is increased by approximately 30％.
In operational on-orbit observation missions,the proximity observation factors of the servicing spacecraft,including viewing sparsity,illumination conditions,and imaging quality,are the primary controllable elements that affect the performance of three-dimensional(3D)reconstruction.In this study,a dynamic model is integrated with the Blender rendering platform to design four types of proximity observation trajectories:co-moving flight,natural orbiting,V-bar maneuvering,and spiral approach.Various initial conditions,such as the target structure,orbital altitude,and observation distance,are configured to construct diverse observation scenarios and generate simulated image sequences.Subsequently,a parametric observation model based on 3D Gaussian Splatting is introduced to iteratively learn the implicit 3D representation of noncooperative space targets.Through adaptive density control,the number and spatial distribution of Gaussian primitives are dynamically adjusted,thus enabling the differentiable rendering of novel views and dense point clouds.Finally,by evaluating COLMAP convergence rates and image-quality metrics,the optimal combination of observation factors is identified,and the suitability of different orbital configurations for 3D reconstruction tasks is analyzed.The results indicate that image sequences acquired via V-bar maneuvering exhibit greater interframe parallax,achieving a COLMAP convergence rate of 91.16％.Moreover,the texture complexity of the target surface affects reconstruction quality.Additionally,the 3DGS algorithm effectively reconstructs complete and dense point clouds,achieving a Peak Signal-to-Noise Ratio of 39.81 dB on the test dataset of the Tango satellite.
