Velocity ambiguity resolving for moving target indication by compressed sensing

Adaptive Clutter Suppression for Airborne Random Pulse Repetition Interval Radar Based on Compressed Sensing

“…AsΘ (ξ ) has already been obtained in (9), the only unknown part of (10) is the pre-filter matrix F. Therefore, we will focus on the problem of how to form F as follows.…”

“…In our first experiment, we will analyze the realization of weighting algorithm for whitening proposed in [9], the basic idea of which is to solve the following minimization problem…”

“…Traditional processing approach for the random PRI radar signal such as correlation is constrained by the uncertainty principle and suffers from high sidelobe pedestal, which reduces the performance of target detection and velocity measurement. Considering of the random modulation scheme and the inherent sparsity of the target echo signal, the compressed sensing (CS) theory [4][5][6][7][8] has been applied to successfully resolve the velocity ambiguity and suppress the sidelobe pedestal [9].…”

“…By now, several literatures have directly addressed to alleviate strong clutter interference [9,[11][12][13], where the approaches can be classified into two groups. One is prefiltering before CS recovery that is mathematically represented as a projection to the clutter free subspace [11,12], and the other is weighting the clutter support combined with CS optimization [9,13], which is actually a sparsity enhancing technology [14]. Both of the methods have a common point that the clutter spectrum needs to be estimated as a prior knowledge for clutter cancellation.…”

“…Traditionally, statistical spectrum estimation methods such as autoregressive model [9] need sufficient statistically independent and identically distributed (i.i.d.) training data to obtain an effective estimation of the clutter.…”