Abstract
In this paper, the application of the emerging compressed sensing (CS) theory and the geometric characteristics of the targets in radar images are investigated. Currently, the signal detection algorithms based on the CS theory require knowing the prior knowledge of the sparsity of target signals. However, in practice, it is often impossible to know the sparsity in advance. To solve this problem, a novel sparsity adaptive matching pursuit (SAMP) detection algorithm is proposed. This algorithm executes the detection task by updating the support set and gradually increasing the sparsity to approximate the original signal. To verify the effectiveness of the proposed algorithm, the data collected in 2010 at Pingtan, which located on the coast of the East China Sea, were applied. Experiment results illustrate that the proposed method adaptively completes the detection task without knowing the signal sparsity, and the similar detection performance is close to the matching pursuit (MP) and orthogonal matching pursuit (OMP) detection algorithms.
Highlights
In recent years, in order to improve the image resolution, radar systems generally use a larger signal bandwidth
Our primary goal in this paper is to propose a novel sparsity adaptive matching pursuit (SAMP) detection algorithm to efficiently and effectively execute the detection task by updating the support set and gradually increasing the sparsity to approximate the signal without knowing the prior knowledge of the signal sparsity
The performance of the detection algorithms is shown in Figure 3, where the signal-to-noise ratio (SNR) is SNR = 15, the number of iterations of MP and orthogonal matching pursuit (OMP) methods is T = 10, the step size of SAMP method is S = 3 and the threshold γ = 0.3 is obtained by the optimal detection based on Monte Carlo simulations
Summary
In order to improve the image resolution, radar systems generally use a larger signal bandwidth. Under the limitation of Nyquist sampling theorem, the system needs to sample the received data at a high rate, which results in a large amount of data [1,2] This problem imposes a higher requirement on A/D converters and brings difficulties to data storage and transmission. Based on the emerging CS theory, the original signal could be restored almost perfectly under the low sampling law by using the prior knowledge of the sparsity of the original signal. Both the amount of data to a certain extent and the requirements for sampling equipment are reduced when dealing with the sparse problem based on the CS theory
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