Optimum Signals for Radar

The research reported herein deals with the general problem of the selection of radar waveforms. The investigation is specifically concerned with the synthesis of radar signals which are optimum in the sense that they are characterized by ambiguity surfaces minimized over certain predetermined regions of the ambiguity plane. The weighted ambiguity surface is utilized as the weighted error criterion. This error criterion is mathematically tractable and pertinent to radar system performance but is not unduly restrictive as some orientation parameters are left unspecified for subsequent cost or penalty function analysis. The signal optimization is approached by variational techniques augmented by equality and inequality constraints, for example, limiting the amount of bandwidth or frequency modulation to be less than some system requirement. Several examples are presented demonstrating the optimization techniques and providing a minimum error for the stated problem. It is shown that for any given type of amplitude modulation of the radar signal, the variance or dispersion of the ambiguity surface is not decreased for any type of phase modulation added. The optimum signal for an elliptical weighting function is derived for several cases. The minimum error is shown to depend upon the constraints and the unspecified orientation parameters and, for one case, on the second moment of the signal.     

Optimal Tracking of Maneuvering Targets

Maneuvering target motion is modeled by introducing a binary random variable in the target state equation. The optimal estimate is shown to be a weighted combination of two Kalman filter estimates with weights depending on the likelihood ratio for the detection of a maneuver. A tracking scheme is proposed for maneuvering target tracking and illustrated in an example.     

Optimum Signals for High-Resolution Radio Transmission Systems

This paper reports on progress in signal design that has led to improvedresolution capability in radar and communication systems without theuse of complicated signal-processing techniques.Two approaches to the problem of improving resolution capabilityare made. The first approach emphasizes the need to produce sharplypeaked autocorrelation functions. The optimum signal amplitude infrequency is specified to accomplish this, and the spectral density ofthe deterministic signal is shown to satisfy a homogeneous Wiener-Hopfequation. The second approach emphasizes the need to producelow and flattened cross-correlation functions, in order to distinguishthem (since they correspond to error outputs) from the sharply peakedautocorrelation functions. With the use of stationary phase integration,a detailed method for producing any desired cross-correlationamplitude is presented. In particular, the techniques necessary to producesinusoidally modulated cross-correlation functions are discussed.These tools are applied to a realistic N-signal processing system,and the resulting optimum signals are shown to be amplitude-modulatedchirped sinusoids. Detailed examples for physically justifiablesystem parameters are included.