Comparative analysis of four different single-frequency PPP models on positioning performance and atmosphere delay retrieval

Single-frequency precise point positioning (SF-PPP) has attracted increasing attention due to its high precision and cost effectiveness. With various strategies to handle the dominant error, i.e., ionosphere delay, the ionosphere-float (IF), ionosphere-free-half (IFH), ionosphere-corrected (IC), and ionosphere-weighted (IW) SF-PPP models are certain to possess different characteristics and performance levels. This study is dedicated to assessing and comparing the four models from model characteristics, positioning performance, and atmosphere delay retrieval. The model comparison shows that IC and IW models are full-rank while IF and IFH models have a rank deficiency of size one that will result in biased estimations, which means the better solvability of IC and IW models. The experiments are carried out based on the 7-day Global Positioning System (GPS) observations collected at 57 global Multi-GNSS Experiment (MGEX) stations and Global Ionosphere Map (GIM) products. The results indicate that the IW model can accelerate SF-PPP convergence and achieve higher positioning accuracy compared to the other three SF-PPP models, especially in kinematic mode. With convergence criteria of 0.25 m in horizontal and 0.5 m in vertical, the east/north/up convergence times of IW model are 0.5/15.0/25.0 min and 0.5/16.0/36.5 min for static and kinematic modes, respectively. The IW model is able to achieve an instantaneous positioning accuracy of 0.28/0.35/0.75 m. In addition, a real kinematic test also demonstrates the best positioning solutions of IW model. Regarding troposphere delay retrieval, the IF, IFH, and IW models obtain a comparable daily accuracy of 3.0 cm on average, while the IC model achieves the worst accuracy of 8.0 cm. For precise ionosphere delay estimation, IW model only needs an average initialization time of 34.3 min, but a longer initialization time of 51.6 min is required for IF model. The daily precision of ionosphere delay estimation for IW model can reach up to 10.8 cm. At the present accuracy of GIM products, it is suggested that the IW model should be adopted for SF-PPP first due to its superior performance in positioning and atmosphere delay retrieval.     

数据融合的对流层天顶延迟估计方法

为提高对流层天顶延迟(Zenith tropospheric delay，ZTD)的估计性能，提出了基于数据融合的ZTD估计方法。估计干延迟采用Saastamoinen模型，估计湿延迟采用Askne模型，地表气象测量设备提供给两模型所需的气压、温度以及水汽压，Askne模型所需的加权温度、温度变化率和湿度变化率由全球气压和温度2w(Global pressure and temperature 2w，GPT2w)模型提供。当气象测量设备不可用时，上述所有气象参数均来自于GPT2w模型。利用国际GPS服务(International GPS service, IGS)提供的数据进行验证，结果表明：当地表测量设备存在时，所提方法较Saastamoinen模型提高了8mm；当全部气象参数来自GPT2w时，本方法较GPT2w+Saastamoinen模型提高了8.1mm；对于季节分明的测站，误差趋势同样具备季节性；对于海拔高和气候干燥的的测站，估计误差较小。     

基于虚拟慢时间的双基地ISAR成像算法

针对双基地角时变引起的逆合孔径雷达（ISAR）图像畸变和散焦问题，提出了一种基于虚拟慢时间的成像算法。首先，分析了双基地角时变对ISAR成像的影响机理。然后，基于图像对比度最大准则估计等效旋转中心位置，完成初次相位补偿。最后，通过虚拟慢时间构建基于非均匀虚拟采样的补偿系数矩阵，并通过方位向非均匀傅里叶变换得到目标的ISAR像。算法基于图像对比度最大准则解决等效旋转中心位置估计问题，通过虚拟慢时间消除转动相位项的高次项影响，利用非均匀傅里叶变换解决随机虚拟采样的谱估计问题。理论分析和仿真结果验证了算法的有效性和鲁棒性。     

基于多特征融合的航天器锂电池健康评估技术

传统的航天器蓄电池可靠性试验按照最大放电深度进行定额充放电，所构建的失效模型用于支撑航天器总体可靠性设计，不能用于在轨锂离子蓄电池健康评估任务；航天器在蓄电池遥测的采样率、精度、样本量方面无法与民用领域相比，基于高采样、大样本的民用蓄电池健康估计方法也不适用于在轨锂离子蓄电池健康评估。针对该问题，从在轨航天器蓄电池数据特性出发，挖掘在轨状态下所能提取的退化特征，并采用多特征综合评价方法，设计了一种基于多特征融合的在轨锂离子蓄电池健康评估方法，实现了在轨蓄电池放电内阻、同放电深度下的终端电压、恒压充电时间3项退化特征融合的健康量化评估，应用于某型号卫星的在轨监测与健康评估，具有良好的工程实用性，可作为国内航天器健康评估技术的参考。     

Comparison of convergence time and positioning accuracy among BDS,GPS and BDS/GPS precise point positioning with ambiguity resolution

Precise point positioning (PPP) usually takes about 30?min to obtain centimetre-level accuracy, which greatly limits its application. To address the drawbacks of convergence speed and positioning accuracy, we develop a PPP model with integrated GPS and BDS observations. Based on the method, stations with global coverage are selected to estimate the fractional cycle bias (FCB) of GPS and BDS. The short-term and long-term time series of wide-lane (WL) FCB, and the single day change of narrow-lane (NL) FCB are analysed. It is found that the range of GPS and BDS non-GEO (IGSO and MEO) WL FCB is stable at up to a 30-day-time frame. At times frame of up to 60?days, the stability is reduced a lot. Whether for short-term or long-term, the changes in the BDS GEO WL FCB are large. Moreover, BDS FCB sometimes undergoes a sudden jump. Besides, 17 and 10 stations were used respectively to investigate the convergence speed and positioning errors with six strategies: BDS ambiguity-float PPP (Bfloat), GPS ambiguity-float PPP (Gfloat), BDS/GPS ambiguity-float PPP (BGfloat), BDS ambiguity-fixed PPP (Bfix), GPS ambiguity-fixed (Gfix), and BDS/GPS ambiguity-fixed (BGfix). The average convergence speed of the ambiguity-fixed solution is greatly improved compared with the ambiguity-float solution. In terms of the average convergence time, the Bfloat is the longest and the BGfix is the shortest among these six strategies. Whether for ambiguity-float PPP or ambiguity-fixed PPP, the convergence reduction time in three directions for the combined system is the largest compared with the single BDS. The average RMS value of the Bfix in three directions (easting (E), northing (N), and up (U)) are 2.0?cm, 1.5?cm, and 5.9?cm respectively, while those of the Gfix are 0.8?cm, 0.5?cm, and 1.7?cm. Compared with single system, the BDS/GPS combined ambiguity-fixed system (BGfix) has the fastest convergence speed and the highest accuracy, with average RMS as 0.7?cm, 0.5?cm, and 1.9?cm for the E, N, U components, respectively.     

中继卫星系统仿真软件设计与应用*

设计开发中继卫星系统仿真软件,并针对理想信道条件、I/Q幅相不平衡、幅频特性、群时延、相位噪声、功放饱和条件、非线性信道条件这七种信道,仿真16QAM和16APSK的误码率性能,并对仿真结果进行分析。利用仿真平台得出的非线性信道条件下误码率需求与所需信噪比之间的关系,可以作为完善系统技术指标的重要依据,为中继卫星系统后续建设和应用提供重要参考。     

直升机驾驶员模型研究

主要阐述直升机驾驶员各特征参数的确定方法，导出了各种不同形式模型参数的换算公式以及模型参数与驾驶员评价等级之间的关系。本文的方法经过多种直升机飞行试验数据的验证是查行的，可用于各种直升机的闭环分析。     

一种使用EPROM的时间增益补偿电路

本文简要介绍了医用超声波类仪器的基本原理，分析了超声波类仪器中普遍采用的Ｔ．Ｇ．Ｃ电路的不足。为克服上述电路的不足，本文提出了该电路的一种新的设计方法并在实际中实现了该方法。     

用最大值原理求某无人机的最优上升轨迹

为改进某无人机飞行性能，应用最大值原理求解无人机在垂直平面内的最优上升轨迹，推导了无人机最经济爬升所满足的极值方程；并用此方程和最快爬升极值方程这两类目标函数求解了某无人机的最优上升轨迹，计算结果表明，经优化后的爬升轨迹可以节省燃油，延长无人机的续航时间或快速爬升到预定的终点高度具有较好的性能收益。     

基于时间序列数据挖掘的旋转机械故障预报

提出一种基于时间序列数据挖掘的故障预报新方法。把故障前兆因子作为一种暂态,根据旋转机械轴承振动的实验数据建立时间序列．利用时延嵌入的方法重构状态空间,在状态空间中使用遗传算法搜寻最优暂态束．组成暂态集。用暂态集对旋转机械轴承振动的测试数据进行分析．判断是否为故障前兆因子．从而实现故障预报。