Detection and Localization of Multiple Unresolved Extended Targets via Monopulse Radar Signal Processing

Abstract

When the size of targets is comparable to the range resolution of monopulse radars, these targets should be considered as extended rather than point targets. If several closely-spaced targets fall within the same radar beam and between adjacent matched filter samples in range, the full monopulse information from all of these samples can and should be used to resolve these targets, i.e., estimate the number of targets and their respective angles-of-arrival and ranges. To detect and localize multiple unresolved extended targets, we establish a model for monopulse radar returns from extended objects, and present a maximum likelihood estimator to localize the targets. Rissanen's minimum description length (MDL) criterion will be used to decide the number of existing extended objects. We also derive the upper limit on the number of targets and their scattering centers that can be resolved, and we provide necessary conditions for these targets to be uniquely identified. We compare the new extended target monopulse processing scheme with previously developed point-target monopulse techniques in the simulations.

Radar detection and range estimation using oversampled data

“…In [6], it is assumed that several closely spaced targets fall within the same beam of a monopulse radar and among three or more adjacent matched filter samples in range; for the considered scenario, a maximum likelihood (ML) extractor is developed that uses monopulse information from the preceding samples to estimate the angles and ranges of the targets. This idea is further investigated to estimate the angles and ranges of multiple unresolved extended targets in [7]. In [8], the authors focus on space-time adaptive processing [9][10][11][12] and devise decision schemes for pointlike targets, which suitably exploit the spillover of target energy to provide accurate estimates of the target position within the CUT (subbin accuracy).…”

Radar detection and range estimation of a point-like target in non-Gaussian, possibly correlated, noise

“…Moreover, most detectors considered so far assume that the target is located exactly "where the matched filter is sampled" and, hence, that there is no spillover of target energy to adjacent matched filter returns. Actually, there are reports of algorithms that consider the spillover between two adjacent sampling points and use practical centroiding to associate pairs of targets from one matched filter sample to those in another [6], [7]. The case of coherent targets embedded in homogeneous Gaussian environment has been attacked in [8].…”

A ML localizer of multiple radar targets

“…In [4] it is assumed that several closely spaced targets fall within the same beam of a monopulse radar and among three or more adjacent matched filter samples in range; therein, target returns are ruled by the Swerling II fluctuation model, and a maximum likelihood (ML) extractor is developed that makes use of monopulse information from the above samples to estimate the angles and ranges of the targets. This idea is further investigated in order to estimate the angles and ranges of multiple unresolved extended targets in [5]. This paper is in part an extension of [6] where space-time data are processed in order to detect a possible point-like target with known Doppler frequency shift and direction of arrival (DOA).…”