In this paper, we tackle the problem of off-grid radar target detection based on the Normalized Matched Filter. State-ofthe-art solutions to this problem include the use of the Generalized Likelihood Ratio test (GLRT), whose implementation can be costly, subspace detectors, or oversampling. In this paper, we introduce a new solution for improving detection of an off-grid target. It is inspired by monopulse angle estimation methods. Using simulations, we show that our solution is comparable to the GLRT under Gaussian noise hypothesis with a known covariance matrix in terms of detection probability. As such, its detection performance appears to be often better than those of detectors in the same computational cost range, and significantly better in some cases.