1) obtaining baseband signal by a receiver, and performing pulse compression and coherent integration on the baseband signal to obtain a range-Doppler map, performing pre-detection based on the range-Doppler map to obtain interested pre-detect targets (PDTs), wherein the corresponding ranges and Doppler frequencies of cells, wherein the PDTs are pre-detected, are represented by r_ζ and f_ζ, respectively;

2) estimating ranges and the Doppler frequencies of the PDTs, wherein the estimates are represented by and ;

3) establishing a dimension-reduction observation model of a received signal based on and ;

4) reconstructing a target vector in the dimension-reduction observation model based on a sparse recovery algorithm; and

5) adopting a generalized likelihood ratio detector for target detection based on a reconstruction result and outputting target detection results and their parameters, and determining whether the PDTs are true targets based on the target vector,

wherein the step 2) comprises:

adopting and to represent true ranges and Doppler frequencies of the PDTs, respectively, and letting =[; ] and θ_ζ=[r_ζ;f_ζ], wherein the symbol; in the square brackets represents connecting two vectors, the received signal is represented as:

y in the equation (7) represents a received signal of one coherent processing interval, wherein β is a dimension-reduction target vector, an i^th element β_i represents a true complex amplitude of an i^th PDT, is an observation matrix, and n is an additive white Gaussian noise vector;

based on a maximum likelihood criterion, estimates of and β are given as:

β=(A(θ)^HA(θ))⁻¹A(θ)^Hy  (9)

g(θ)=∥y−A(θ)(A(θ)^HA(θ))⁻¹A(θ)^Hy∥₂²,  (10)

∇_θg(θ)≈∇_θg()+∇_θ²g()(θ−),  (11)

≈θ−(∇_θ²g())⁻¹(∇_θg(θ))  (12)

≈θ_ζ−(∇_θ²g(θ_ζ))⁻¹(∇_θg(θ_ζ))  (13).