基于皮卫星的分布式干扰系统及其关键技术

在对皮卫星的特点总结的基础上,提出了一种基于皮卫星的天基干扰系统,给出了干扰系统的原理性方案,对其中的关键技术进行了分析。     

皮卫星姿态估计及粗太阳敏感器标定算法研究

粗太阳敏感器是一种由多个光电池片配置组成的模拟式太阳敏感器,它结构简单、资源要求少,在皮卫星平台应用较为广泛。光电池光伏输出特性是粗太阳敏感器测量的基础,使用余弦关系近似等效会引入很大的误差。通过实际测试和深入分析,建立了更高精度的粗太阳敏感器输出特性等效数值模型,并以此为基础,推导了卫星姿态估计及粗太阳敏感器标定的联合在轨实时算法。算法采用多级耦合结构,由1个扩展卡尔曼滤波器(EKF)和6个卡尔曼滤波器组成,同时估计卫星姿态、卫星角速度以及卫星6个面共30个标定参数。仿真表明,和常规的EKF姿态估计算法相比,联合算法的运算量只增加了一半,而估计精度却提高了一个量级。     

Magnetometer Compensation Scheme and Experimental Results on ZDPS-1A Pico-satellite

In a pico-satellite with small volume, measurements from on-board three-axis magnetometer (TAM) are not accurate, as it can be easily disturbed by other electronic systems. To improve its accuracy, a scheme of compensation methods is introduced in this article. The scheme is based on an improved measurement model of pico-satellite TAM, and it mainly consists of three steps. First, in satellite design stage, several techniques are recommended to simplify the afterwards compensations. Then after satellite assembly, TAM ground tests and pre-launch calibration with least-square batch filter are introduced to improve magnetometer performance. At the end, a post-launch calibration with unscented Kalman filter (UKF) is implemented with in-orbit data. The compensation scheme is used in the development of Chinese pico-satellite ZDPS-1A made by Zhejiang University. Results show that with the introduced compensation scheme, the maximum error of ZDPS-1A TAM can be reduced from 80 mG to 6 mG (1 G=10-4 T).     

Space Flight Validation of Design and Engineering of the ZDPS-1A Pico-satellite

The ZDPS-1A pico-satellites are the first satellites in China within the 1-10 kg mass range that are successfully operated on orbit. Unlike common pico-satellites, they are designed to be "larger but stronger" with more powerful platforms and unique payloads so as to bear a better promise for real applications. Through their space flight mission, the functionality and performance of the two flight models are tested on orbit and validated to be mostly normal and in consistency with design and ground tests with only several inconforming occasions. Moreover, they have worked properly on orbit for one year so far, well exceeding their life expectancy of three months. Therefore, the space flight mission has reached all its goals, and verified that the design concept and the engineering process of the picosatellites are sufficient in allowing them the desired functionality and performance in, and the adaption to the launch procedure and the low-Earth orbit space environment. In the foreseeable future, the platform together with the design concept and the engineering process of the pico-satellites are expected to be applied to more complicated real space applications.     

Pico-satellite Autonomous Navigation with Magnetometer and Sun Sensor Data

This article presents a near-Earth satellite orbit estimation method for pico-satellite applications with light-weight and low-power requirements. The method provides orbit information autonomously from magnetometer and sun sensor, with an extended Kalman filter (EKF). Real-time position/velocity parameters are estimated with attitude independently from two quantities: the measured magnitude of the Earth’s magnetic field, and the measured dot product of the magnetic field vector and the sun vector. To guarantee the filter’s effectiveness, it is recommended that the sun sensor should at least have the same level of accuracy as magnetometer. Furthermore, to reduce filter’s computation expense, simplification methods in EKF’s Jacobian calculations are introduced and testified, and a polynomial model for fast magnetic field calculation is developed. With these methods, 50% of the computation expense in dynamic model propagation and 80% of the computation burden in measurement model calculation can be reduced. Tested with simulation data and compared with original magnetometer-only methods, filter achieves faster convergence and higher accuracy by 75% and 30% respectively, and the suggested simplification methods are proved to be harmless to filter’s estimation performance.