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

## TL;DR

This paper introduces a new robust radar detection method for range-spread targets in Gaussian noise with unknown covariance, modeling the target echo as a sum of coherent and random components, and proposes a generalized likelihood ratio test and a new parametric detector.

## Contribution

It presents a novel detection approach that models target echoes with random components and introduces a new parametric detector, improving robustness against mismatches.

## Key findings

- The proposed detectors outperform natural competitors in performance assessments.
- The new parametric detector encompasses the GLRT as a special case.
- The approach effectively handles unknown covariance matrices and mismatches.

## Abstract

This paper proposes a novel approach to robust radar detection of range-spread 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.