Deep space internet of things (DS-IoT) aims to establish an intelligent space-based interconnected network covering the Mars and beyond. The deployment of DS-IoT relies critically on high-precision navigation technologies. The pulsar/Doppler integrated navigation can provide DS-IoT satellites with high-accuracy position and velocity information. However, the measured spectral bands are narrow, and Doppler shift can introduce truncation effects that compromise accurate Doppler velocity estimation, thereby degrading navigation accuracy. We analyze spectral distortion from truncation-induced nonlinear phase differences and propose a Fourier domain phase estimation method using ConvMixerCNN-Vision Transformer optimized sparse representation (FPE-CViT-SP). Our approach: (1) constructs a phase shift dictionary via DCT-based spectrum decomposition and shifted reconstruction; (2) formulates a sparse representation objective for optimal spectrum-dictionary matching; (3) employs a ConvMixerCNN-Vision Transformer to efficiently minimize the objective, replacing computationally intensive cross-correlation searches; and (4) applies sparse code-based super-resolution for precise Doppler estimation, effectively addressing nonlinear phase shifts. This method effectively addresses nonlinear phase shift issues through sparse representation characteristics. Furthermore, we develop a pulsar/Doppler integrated navigation system for DS-IoT satellites that significantly enhances collective positioning accuracy through inter-satellite link-based navigation data sharing. Comparative experiments demonstrate that our method achieves 32.17% and 35.29% improvement in position and velocity estimation accuracy respectively over X-ray pulsar navigation.

The performance of vehicle detection system is often affected by both internal and external noise. In this paper, a novel vehicle detection scheme called vehicle detector based on EMD-HT and multichannel GLRT (V-EHMG) is proposed, which composed of signal denoising part based on empirical mode decomposition (EMD) and signal detection part which takes advantage of the spatial–temporal relationship acquired by multiple sensors. In the first part, we propose a new denoising algorithm null-hypothesis interval threshold EMD denoising (EMD-HT) to check the similarity between the probability density function of the decomposed intrinsic mode functions (IMFs) from input signal and the generalized Gaussian distribution to determine relevant IMFs for signal reconstruction. Combined the interval threshold method, the denoised signals are acquired. In the second part, we use multichannel generalized likelihood ratio test (GLRT) detector to judge the presence of other vehicles with the denoised signals as the input. Simulation and physical experimental results show that with V-EHMG, the signal-to-noise ratio of the signal is doubled and the detection range is improved more than 40% compared with the traditional methods.

In order to receive ground signals efficiently, doubly salient permanent magnet (DSPM) motor which is applied in communication satellites as a drive needs to be activated frequently to adjust the attitude of communication satellites. Then, the initial rotor position of the motor must be estimated accurately at standstill for producing a steady and sufficient starting torque. If the rotor rotates during the estimation process, the pointing accuracy of the satellite pointing mechanism will be affected. To solve this problem, this paper proposes a novel rotor initial position estimation scheme based on the principle that different initial rotor positions can cause the variations of stator winding inductances. The identification signals, which do not cause the rotor to rotate during the identification process, are injected into the motor windings to identify the stator inductances. Thus, the variations of the winding inductances can be detected, and the initial rotor position can be estimated accurately by these variations. The proposed method is verified through experimental platform. The experimental results show that the initial rotor position is accurately estimated, and the starting performance of the DSPM is improved.

Remote sensing processing requires precise motion information provided by position and orientation system (POS), whereas gravity disturbance is normally ignored in POS solution procedure. For high-precision POS, gravity disturbance becomes a significant error source with decisive effects on the accuracy of POS. In this paper, an accurate gravity compensation method is proposed, which includes the gravity disturbance as the error states of POS Kalman filter, and the appropriate model of gravity disturbance is constructed by time series analysis coupled with the direct difference method. In verifying our gravity compensation method, POS and digital still camera combined flight experiment was conducted in 2011, where the aerial triangulation output of images was taken as a reference to evaluate the POS accuracy. Results show that the horizontal attitude accuracy of high-precision POS (0.01 °/h gyro drift) is 0.0031 ° under differential Global Positioning System condition, and the proposed method has a better performance comparable to other gravity compensation methods.