Optimized Reconstruction of GPR Target Signature Using Evolving Sparse Representation
Optimized Reconstruction of GPR Target Signature Using Evolving Sparse Representation
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DOI：
10.23919/piers.2018.8597877
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发表时间：
2018-08
期刊：
影响因子：
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通讯作者：
B. Rohman;M. Nishimoto
中科院分区：
文献类型：
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作者：
B. Rohman;M. Nishimoto
本文提出了一种基于稀疏表示技术的超宽带脉冲探地雷达目标特征信号重建方法。该方法采用了一个著名的匹配追求，这是顺序优化的进化算法与一定的技术。虽然在一些先前的研究中已经提出了进化算法的分解优化，本研究将通过采取不同的路径进一步探索和优化的方法进行扩展。模拟的情况下，已采取在自由空间中，以集中在所提出的方法研究。在确定目标响应的时间选通后，对信号进行匹配追踪分解。在每个分解步骤中，分解的原子信号进化自己，以改善其波形参数约束的先验匹配追踪结果的下限和上限的一半偏移。优化的目标函数是误差平方和值。仿真结果表明了该方法在信号重构方面的优越性，在地下目标分类识别等领域具有广泛的应用前景。然后，与现有的方法进行了比较，以确认该方法的性能。
In this paper, a new approach to signal processing based on sparse representation technique is presented to reconstruct the target signature of ultra-wideband pulse ground penetrating radar. The proposed method employs a well-known matching pursuit which is sequentially optimized by the evolutionary algorithm with a certain technique. Though the decomposition optimization by evolutionary algorithms has been proposed in some prior research, this study will extend the approach by taking a different path to explore further and optimize it. The simulated case has been taken in the free space in order to focus on the proposed method investigation. After determination of the time gating of target object response, the signal is decomposed by matching pursuit. In each decomposition step, the decomposed atom signals evolve themselves for refining their waveform parameters constrained by prior matching pursuit results in both lower and upper limit with a half offset. The objective function of the optimization is the sum squared error value. The simulation results show the superiority of proposed method in the signal reconstruction which is promising enough to be applied for wide applications such as classification and identification of the buried object. Then, the comparison with the existing methods was also conducted to confirm the performance of this method.