Orbital Injection Errors and Sensor Requirements for NERVA Space Launchers

Abstract

The input module of the NERVA space launcher guidance system consisting of the inertial and sensor platform is responsible for the basic accuracy if the ascent trajectory and injection efficiency. The sample rate magnitude and data filtering along the real time trajectory are the only tools available for improving the guidance accuracy up to the level of requirements to secure admissible orbital injection error and the subsequent flight corridor during the orbital ascent. Analysis of the NERVA-1 flight telemetry flow from the onboard inertial platform raises the problem of the optimal selection of the onboard sample rate and of the rate of telemetry, which are not identical. The orbit injection errors are chosen from the orbit altitude constraints and subsequent accuracy requirements for the inertial sensors are derived. They show that the accuracy requirements are moderate and may be covered with almost conventional sensors. To improve the flight guidance accuracy the rocket motor chamber pressure and thrust are measured and observation of the preflight zero drift, recording noise and of the high level of embedded noise during both powered and coast atmospheric flight is performed. Simple filtering based on frequency Fourier analysis is delivered with conclusions regarding the intelligent algorithm enhancement that are developed and implemented on the next generation of flight research drone missiles RT-759M NERVA-2, right in preparation. The main rationale of that algorithm stands in the method of discriminating between false and true information on each measuring point immediately after the data are delivered by the sensors. Learning procedure from previous preflight recordings and from gradual accumulation of concurrent data streams subjected to FF spectral analysis are combined to improve data filtering, for immediate release to the next module of the autopilot. The rate of sampling is optimized from the analysis of the previous flight, inertial data records and test stand pressure and thrust records that show the level of noise. The behavior of the electronics under the dynamical loads of the rocket flight, involving overloads of more than 20 g-s and the level of vibration during the real flight and other sources of measuring errors are also focused in the research. During simulated work of the sensor platform the algorithm has been acceptably validated and prepared for real flight test performance. Information important for the NERVA autopilot design activity is structured through the multiple variance approach.