A novel approach to robust radar detection of range-spread targets

Abstract

This paper proposes a novel approach to robust radar detection of rangespread targets embedded in Gaussian noise with unknown covariance matrix.The idea is to model the useful target echo in each range cell as the sum of a coherent signal plus a random component that makes the signal-plus-noise hypothesis more plausible in presence of mismatches. Moreover, an unknown power of the random components, to be estimated from the observables, is inserted to optimize the performance when the mismatch is absent. The generalized likelihood ratio test (GLRT) for the problem at hand is considered. In addition, a new parametric detector that encompasses the GLRT as a special case is also introduced and assessed. The performance assessment shows the effectiveness of the idea also in comparison to natural competitors.

Design of Robust Radar Detectors Through Random Perturbation of the Target Signature

“…Outgrowths of this work are the investigation of further possible structures for the design matrix Σ, and possibly the derivation of the one-step GLRT for the general case (which in fact is missing in Table I). A different avenue for possible generalizations is robust detection of range-spread targets; to date, the GLRT for the proposed random perturbation approach has been derived only in the case Σ = C, which though has the same remarkable properties of the point-like detector derived here [30]. Finally, it would be interesting to ascertain whether receivers with the same detection power of Kelly's detector (under matched conditions) but controllable robustness or selectivity under mismatched conditions can be obtained through design approaches different from GLRT, namely inspired to machine learning tools.…”

Detection and Classification of Multirotor Drones in Radar Sensor Networks: A Review

“…In many scenarios, it is not realistic to expect only one target in the scenario; this might cause possible target returns in the reference window. Similarly, there might be a large target that exceeds the size of the CUT and thus contaminates the reference window [ 47 ]. These situations cause the erroneous introduction of a bias in the threshold and give rise to the so-called target masking : specifically, the first type is referred to as mutual masking and the second one as self-masking.…”

Toward Integrated Large-Scale Environmental Monitoring Using WSN/UAV/Crowdsensing: A Review of Applications, Signal Processing, and Future Perspectives

“…Practical examples include the meteorological monitoring stations [4], the diffused seismographs systems for earthquake detection [5], or the oceanic report systems [6], just to name a few. To complement such terrestrial systems, observations from satellite and airborne platforms have been largely considered, not only for strict monitoring purposes [7][8][9][10], but also for building accurate 3D models of the earth's surface [11]. Despite the quite high precision provided through their dedicated equipment, such systems are tailored for single or limited types of environmental analyses and can provide observations of the physical phenomena only at a very small number of locations.…”