Range measurement has found multiple applications in deep space missions. With more and further deep space ex- ploration activities happening now and in the future, the requirement for range measurement has risen. In view of the future ranging requirement, a novel x-ray polarized ranging method based on the circular polarization modulation is proposed, termed as x-ray circularly polarized ranging （XCPolR）. XCPolR utilizes the circular polarization modulation to process x-ray signals and the ranging information is conveyed by the circular polarization states. As the circular polarization states present good stability in space propagation and x-ray detectors have light weight and low power consumption, XCPolR shows great potential in the long-distance range measurement and provides an option for future deep space ranging. In this paper, we present a detailed illustration of XCPolR. Firstly, the structure of the polarized ranging system is described and the signal models in the ranging process are established mathematically. Then, the main factors that affect the ranging accuracy, including the Doppler effect, the differential demodulation, and the correlation error, are analyzed theoretically. Finally, numerical simulation is carded out to evaluate the performance of XCPolR.
The log-polar transform （LPT） is introduced into the star identification because of its rotation invariance. An improved autonomous star identification algorithm is proposed in this paper to avoid the circular shift of the feature vector and to reduce the time consumed in the star identification algorithm using LPT. In the proposed algorithm, the star pattern of the same navigation star remains unchanged when the stellar image is rotated, which makes it able to reduce the star identification time. The logarithmic values of the plane distances between the navigation and its neighbor stars are adopted to structure the feature vector of the navigation star, which enhances the robustness of star identification. In addition, some efforts are made to make it able to find the identification result with fewer comparisons, instead of searching the whole feature database. The simulation results demonstrate that the proposed algorithm can effectively acceldrate the star identification. Moreover, the recognition rate and robustness by the proposed algorithm are better than those by the LPT algorithm and the modified grid algorithm.
Weak L1 signal acquisition in a high dynamic environment primarily faces a challenge： the integration peak is neg- atively influenced by the possible bit sign reversal every 20 ms and the frequency error. The block accumulating semi-coherent integration of correlations （BASIC） is a state-of-the-art method, but calculating the inter-block conjugate products restricts BASIC in a low signal-to-noise ratio （SNR） acquisition. We propose a block zero-padding method based on a discrete chirp-Fourier transform （DCFT） for parameter estimations in weak signal and high dynamic environments. Compared with the conventional receiver architecture that uses closed-loop acquisition and tracking, it is more suitable for open-loop acquisition. The proposed method combines DCFT and block zero-padding. In this way, the post-correlation signal is coherently post-integrated with the bit sequence stripped off, and the high dynamic parameters are precisely estimated using the threshold set based on a false alarm probability. In addition, the detection performance of the proposed method is analyzed. Simulation results show that compared with the BASIC method, the proposed method can precisely detect the high dynamic parameters in lower SNR when the length of the received signal is fixed.
To improve the link efficiency and decrease the payloads in space explorations, a novel simultaneous communication and ranging method based on x-ray communication（XCOM） is proposed in this paper. A delicate signal symbol structure is utilized to achieve simultaneous data transmission and range measurement. With the designed symbol structure, the ranging information is imbedded into the communication signal and transmitted with it simultaneously. The range measurement is realized by the two-way transmission of the range information. To illustrate the proposed method, firstly, the principle of the method is introduced and the signal processing procedure is presented. Then, the performance of the proposed method is analyzed theoretically in various aspects, including the acquisition probability, the bit error rate, the ranging jitter,etc. Besides, numerical experiments are conducted to verify the proposed method and evaluate the system performance.The simulation results show that the proposed method is feasible and that the system performance is influenced by the parameters concerning the signal symbol structure. Compared with the previous methods, the proposed method improves the link efficiency and is beneficial for system miniaturization and integration, which could provide a potential option for future deep space explorations.
X-ray pulsar navigation(XPNAV) is an attractive method for autonomous navigation of deep space in the future. Currently, techniques for estimating the phase of X-ray pulsar radiation involve the maximization of the general non-convex object functions based on the average profile from the epoch folding method. This results in the suppression of useful information and highly complex computation. In this paper, a new maximum likelihood(ML) phase estimation method that directly utilizes the measured time of arrivals(TOAs) is presented. The X-ray pulsar radiation will be treated as a cyclo-stationary process and the TOAs of the photons in a period will be redefined as a new process, whose probability distribution function is the normalized standard profile of the pulsar. We demonstrate that the new process is equivalent to the generally used Poisson model. Then, the phase estimation problem is recast as a cyclic shift parameter estimation under the ML estimation, and we also put forward a parallel ML estimation method to improve the ML solution. Numerical simulation results show that the estimator described here presents a higher precision and reduces the computational complexity compared with currently used estimators.
Hua ZHANGLu-ping XUYang-he SHENRong JIAOJing-rong SUN
