Bootstrap-based detection of targets with unknown parameters in unspecified correlated interference

A detection scheme is proposed for targets in a correlated interference environment. The scheme does not require training data but uses the bootstrap to estimate the distribution of the test statistic under the null hypothesis directly from the received data. It also provides estimates of the frequency and phase of the target return as by-products. Simulation results are provided that indicate its performance for correlated Gaussian and non-Gaussian interference. Good detection of 100% is achievable at -5 dB signal-to-noise ratio for sample sizes as small as 500 data points.