An interplanetary autonomous navigation network, named the Internet of Spacecraft (IoS), is proposed in this paper to enable a Solar System-wide autonomous navigation capability for spacecraft. This network consists of two types of spacecraft, namely host and client spacecraft. The former provide the absolute reference for the whole network, and the latter determine their positions by communicating with the host spacecraft or other client spacecraft. To investigate the performance of IoS, a detailed application scheme that supports a flight from Earth to Mars is developed and analysed. IoS is not a simple extension of the navigation constellation. It is independent of the ground tracking system and the configuration of the network is flexible in that any spacecraft-installed identical device can be added to it. Moreover, client spacecraft whose positions have been determined can also be regarded as host spacecraft and provide further navigation information to others. This paper aims to provide technical support for future deep space exploration.

X-ray pulsar-based navigation method using one sensor (XNAVO) could fix the position of satellites by sequentially observing X-ray pulsars at the cost of loading one X-ray sensor, which drops the demand on the loading capability of satellite and is feasible for practice. However, subjected to the current research status of astronomical measure and the radiation mechanism of pulsar, there are unexpected systematic biases in XNAVO, which would greatly worsen the positioning performance of XNAVO. In addition, the systematic biases compensation methods previously proposed for X-ray pulsar-based navigation using three sensors would fail when being applied to XNAVO, due to the sequential observation strategy. In order to solve the problem, this paper introduces a positioning algorithm for XNAVO based on the modified time-differenced measurement. The propagation of systematic biases is analysed, revealing the systematic biases behave a quasi-periodical variation. Thus, a modified time-differenced measurement is proposed in accordance of the quasi-periodicity. A navigation filter that propagates sigma points to generate the improved time-differenced measurement model without linearisation has been given. The results of simulation have shown that the proposed method could reduce the major impact of investigated systematic biases.

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