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.     

The White Sands Range and Range-Rate System

This paper discusses the concept, design, and design verification of the White Sands Range and Range-Rate System. Development of the system has been completed only through the design phase. The system is designed to meet requirements for high-accuracy midcourse tracking under severe target dynamics at the White Sands Missile Range. It is a multistatic Doppler and range tracker which operates at X band and incorporates transmitter, transponder, receiver, and baseline subsystems. The transmitter includes specially designed digital circuitry to synthesize test signals for target simulation during checkout of the system. The transponder signal is processed by a receiver which has been established theoretically to be the optimum realizable processor of continuous tracking data. The receiver incorporates specially designed carrier acquisition circuitry and digital VCO, and directly provides digital Doppler and tone phase data to facilitate real-time processing. The system utilizes data from other tracking systems at the Range for spatial acquisition, for aiding carrier acquisition in the receiver, and for resolving range ambiguities.     

Position Finding with Prior Knowledge of Covariance Parameters

Butterly [1] presents a Bayesian approach as an alternative to the classical methods for solving the position-finding problem. Butterly assumes that bearing errors are independent and normally distributed with known variances. In the paper, the assumption of known variances is relaxed and it is shown that uncertainty about these variances can be incorporated into the model while also retaining the computational advantages of the Butterly formulation. It is also shown that the Bayes estimate and the classical maximum likelihood estimate will agree in certain cases.