Quality management for space systems in ISRO

In a little over four decades, the Indian Space Program has carved a niche for itself with the unique application driven program oriented towards National development. The end-to-end capability approach of the space projects in the country call for innovative practices and procedures in assuring the quality and reliability of space systems. The System Reliability (SR) efforts initiated at the start of the projects continue during the entire life cycle of the project encompassing design, development, realisation, assembly, testing and integration and during launch. Even after the launch, SR groups participate in the on-orbit evaluation of transponders in communication satellites and camera systems in remote sensing satellites. SR groups play a major role in identification, evaluation and inculcating quality practices in work centres involved in the fabrication of mechanical, electronics and propulsion systems required for Indian Space Research Organization's (ISRO's) launch vehicle and spacecraft projects. Also the reliability analysis activities like prediction, assessment and demonstration as well as de-rating analysis, Failure Mode Effects and Criticality Analysis (FMECA) and worst-case analysis are carried out by SR groups during various stages of project realisation. These activities provide the basis for project management to take appropriate techno-managerial decisions to ensure that the required reliability goals are met. Extensive test facilities catering to the needs of the space program has been set up. A system for consolidating the experience and expertise gained for issue of standards called product assurance specifications to be used in all ISRO centres has also been established.     

Integrated drought monitoring index: A tool to monitor agricultural drought by using time-series datasets of space-based earth observation satellites

In this study, integrated drought monitoring index (IDMI) was proposed as a tool to assess and monitor the spatio-temporal dynamics of agricultural drought during the northeast monsoon season for the period from 2000 to 2016 in Tamil Nadu state, south-eastern part of Indian peninsula. The IDMI is characterized as the principal component of precipitation condition index (PCI), soil moisture condition index (SMCI), temperature condition index (TCI), and vegetation condition index (VCI) derived from time-series satellite observations of climate hazards group infra-red precipitation with stations (CHIRPS), European space agency climate change initiative (ESA-CCI) and moderate resolution imaging spectroradiometer (MODIS). The study shows that in the year 2016, about 44.4 and 17.8% of Tamil Nadu state was under extreme and severe drought conditions, respectively. Sensitivity analysis of the study shows that PCI is the most influential parameter to IDMI, followed by VCI and TCI. The validation of IDMI with 3-month standardized precipitation index (SPI) by using Pearson correlation test shows a strong positive correlation between IDMI and 3-month SPI with correlation coefficient (r) value of 0.73 and 0.77 for the wet (2005) and dry year (2016), respectively. The study clearly demonstrates the potential of IDMI derived from time-series datasets of earth observation satellites as a tool in assessment and monitoring of spatio-temporal dynamics of agricultural drought. The proposed IDMI could be effectively used as a reliable tool to monitor agricultural drought and develop its mitigation strategies to minimise the adverse effects of drought on agriculture, water resources, and livelihoods of the people.     

Estimation of singularities for intercept point forecasting

In this paper, a recursive approach (an algorithm for estimation of singularity (AES)) is proposed to forecast the intercept point of a target and the pursuing interceptor recognized as the estimated singularity of a nodal cubic curve fitted to the data. The data comprises direction of arrival (DOA) estimates of both target and interceptor obtained at regular intervals of time using the maximum likelihood (ML) DOA estimation method. The estimates of coefficients of the cubic polynomial are given by a recursive least squares solution. From these coefficients, closed-form solutions for angle of interception and intercept time are obtained which are the forecasted coordinates of the intercept point. Experimental results demonstrate performance of the proposed algorithm