Cross Validation Based Distributed Greedy Sparse Recovery for Multiview Through-the-Wall Radar Imaging

Algorithm 1

CSOMP.

Input: The measurement vectors , the dictionary matrices , for , the measurement noise level and the

censored level .

Initialization: Let the residual vectors , the support set , for , and the iteration times ;

Iteration: (1) Compute the observation vectors for ;

(2) (a) Censoring: , , where denotes the set of indices corresponding

to largest entries of ;

(b) Communication: sharing and with all units;

(c) Construct new sparse vector , whose nonzero entries are located at the indices indicated by with the

coefficients ;

(3) Sum up all observation vectors: ;

(4)Update support set: finding the largest entry in , , ;

(5) Update the residual: , where consists of columns of corresponding to the

indices in ;

(6) If , then , return to step (1); otherwise, stop the iteration and compute sparse solution ,

whose nonzero entries are located at the indices indicated by with the coefficients ;

Output: Obtain the additive fusion result of from various views .