Abstract
This paper focuses on the issue of adaptive waveform selection to optimize the radar resolvability of closely located targets, which is significant in radar detection and estimation. The well-known ambiguity function-based radar resolution only takes transmitted waveform into consideration, which cannot reflect the achievable resolution of a real radar system. In this paper, we consider radar practical resolution based on a geometric metric, Euclidean Distance between probability density functions (PDF-ED), which is defined as square difference between the probability density functions of radar measurements. The PDF-ED takes the PDF's envelope curve into account and specifies the essential difference between the two PDFs in terms of information geometry. Thus the radar practical resolution based on this conveniently characterizes the effect of waveform parameter, target state and measurement model, etc. and offers a statistical way to assess radar sensing capability for a given application. Accordingly, an adaptive waveform selection criterion aiming to maximize the practical resolution is proposed. The experimental simulations verify its effectiveness in decreasing the probability of error when distinguishing two targets.
Highlights
The ability to distinguish multiple adjacent targets, usually represented by resolution, is of great importance for evaluating the performance of radar and other sensing systems
We focus on two types of waveforms, the Gaussian pulse of continuous wave (CW) and the linear frequency modulation (LFM) pulse with Gaussian envelope
The error probability of the proposed adaptive waveform is much lower than that of the ambiguity function-based one, which suggests the superiority of our method in radar practical resolution
Summary
The ability to distinguish multiple adjacent targets, usually represented by resolution, is of great importance for evaluating the performance of radar and other sensing systems. This paper adopts Euclidean Distance between the probability density functions (PDF-ED) to specify the radar practical resolution This metric considers the geometric shape and structure of the PDF and is a more accurate and reliable way to measure the difficulty for distinguishing different PDFs than the methods that considers the mean only. A. MINIMUM PROBABILITY OF ERROR AND PDF-ED In practice, radar measurements of targets, which are random variables contaminated by noise, follows a parameter-based likelihood function. H1 where p (x) is the empirical PDF obtained from the radar measurements This PDF-ED-based resolution calculates the resolvability of targets in measurement domain, and corresponds to the waveform parameter, target state, and measurement model. It is a more practical criterion for measuring the radar sensing performance. As the PDF-ED reflects the differences between the likelihood functions quantitatively, it promotes the radar resolvability of targets
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