In-Flight Alignment and Calibration of Inertial Measurement Units?Part II: Experimental Results

This is the second part of a two-part paper which summarizes work pursued by the author in 1967 [2]. The paper describes the application of minimum-variance estimation techniques for in-flight alignment and calibration of an inertial measurement unit (IMU) relative to another IMU and/or some other reference. The first paper [1] formulates the problem, and this paper reports numerical results and analyses. The approach taken is to cast the problem into the framework of Kalman-Bucy estimation theory, where velocity and position differences between the two IMU's are used as observations and the IMU parameters of interest become part of the state vector. Instrument quantization and computer roundoff errors are considered as measurement noise, and environmental induced random accelerations are considered as state noise. In this paper, numerical results for three important IMU error parameter configurations are presented and discussed. The main results of the paper determine the effects of state and observation noise levels and the nominal trajectory on the identifications of the errors for these configurations. A discussion of the minimum number of trajectory maneuvers and of the optimal trajectory maneuvering is given.     

Remarks on ?Comments on 'Signal Resolution via Digital Inverse Filtering'?

Published comments on a recent paper criticized the use of inverse filtering as applied to resolution of overlapping radar signal returns. It is shown that an inadequate model of the inverse filter was assumed by the critic, which lead him to predict excessive time sidelobes at the filter output. It is demonstrated, by computer simulation, that the time sidelobes at the output of the true inverse filter are down 30 dB or more.     

Application of a Highly Parallel Processor to Radar Data Processing

The application of a highly parallel computer known as PEPE (Parallel Element Processing Ensemble) to radar data processing is described. The PEPE computer consists of a large number of identical processing elements which operate in parallel and are controlled by a common control unit. Each of the processing elements is assigned to a particular radar target such that many targets can be tracked in parallel. PEPE is designed to augment a conventional computer rather than to stand alone. The total data processing load associated with a radar tracking system is distributed between PEPE and the sequential machine in a manner than maximizes the overall system efficiency and desensitizes the system performance to fluctuations in traffic levels. The use of PEPE provides very high data processing throughput potential to a radar data processing system.     

Application of the Volterra Series to the Analysis and Design of an Angle Track Loop

A typical function of an angle tracking loop is to keep a radar antenna pointed at a target. The error in pointing is directly related to successful operation of the tracking device; therefore, its behavior is of interest. For a tracker with a general polynomial nonlinearity, an arbitrary initial pointing error, and a bounded deterministic input, a method is developed for finding upper bounds on the magnitude of the tracking error using Volterra series techniques. Convergence regions of the Volterra series are also obtained. Applications of these results are made to a second-order tracking device.     

Theory of LDV Tracking Systems

The application of frequency-tracking systems to the analysis of laser doppler velocimeter (LDV) signals degraded by background noise has been studied both theoretically and experimentally. Expressions are derived for both the correlation function and the expected value of the phase derivative in the general case of noise off center from the Doppler frequency, and these results are specialized to specific cases of practical interest. Laboratory measurements of output signal-to-noise ratio (SNR) and dc error, for varying input SNR and noise center frequency offset, show good agreement with the theoretical predictions.     

Comments on the Cost and Performance of Military Systems

Editor's Note: Mr. Fowler was the banquet speaker at the Tri Service Radar Symposium held at West Point in 1977. We invited Mr. Fowl to share his speech with the AES readership by providing this paper. The material and style correspond closely to his oral presentation.     

The use of model-based test requirements throughout the productlife cycle

Test requirements, which are generally collected in multiple disparate formats throughout the life cycle of an electronic product, could be used in various applications that reduce test and development cycle times and increase the confidence in the final test program. Unfortunately, test requirements are seldom captured in a consistent format that may be processed by a computer, thus eliminating the possibility of using such requirements in an engineering application. Additionally, such an approach disallows test requirements captured in one segment of the product life cycle to be reused in subsequent life cycle stages. This paper describes a model-based methodology, specifically the Test Requirements Model (TeRM), which can be shown to facilitate the transfer of test-related product information between various stages of the life cycle. This transportability, in conjunction with an exchange format that can be processed by a computer, permits test requirement information to support value-added applications in the engineering process throughout the life cycle of a product     

Characteristics of a successful space system engineer

The Far Ultraviolet Spectroscopic Explorer (FUSE) satellite was launched on June 24, 1999, on a three-year mission to explore the universe using the technique of high-resolution spectroscopy in the far-ultraviolet spectral region. The FUSE instrument comprises many subsystems, each of which contributes in an essential way to the success of the mission. The instrument system engineer oversees the engineering of all elements in such a complex technical project. In performing system engineering for the FUSE instrument's command, telemetry, data processing and data storage functions, and in leading the engineering efforts for the development of the FUSE instrument on-board computer, the author has learned valuable lessons about the characteristics that are prerequisite to success for a space system engineer. These characteristics fall under various categories of acquired, practical know-how. These categories are described with illustrations drawn from the development of the FUSE instrument. In addition to these practical skills and the concomitant knowledge, the system engineer needs personal integrity, which is the link that connects knowledge with know-how and makes them work together to motivate a team of subsystem engineers. This, too, will be discussed