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Multitarget Resolution and Performance for Radar Sensor Network (RSN)

机译:雷达传感器网络(RSN)的多目标分辨率和性能

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Multitarget detection and separation are important for radar sensor network (RSN). Since the interference among the targets existing in adjacent resolution cells may mask the desired one, in this paper we develop a multitarget resolution model for RSN systems. Three waveforms, i.e., simple pulse, linear frequency modulated (LFM) pulse and Barker codes (phase modulated pulse) are applied to appraise the resolution performance. The multitarget detection performance of a distributed RSN over pass-loss fading channels with additive noise has also been analyzed. For fusion center we use Likelihood ratio (LR) decision rule, which is reduced to the test statistics according to the channel signal-to-noise ratio (SNR), probability of false alarm and the number of radar sensors (RSs). Simulation results demonstrate that detection and resolution performances are better with higher SNR. Adding the number of RSs in RSN can improve the performance, combination of amplifying SNR and increasing the number of RSs can get a much better RSN system performance, while increasing targets can exert worse influence on the detection results.
机译:多目标检测和分离对于雷达传感器网络(RSN)至关重要。由于相邻分辨率单元中存在的目标之间的干扰可能掩盖了所需的目标,因此在本文中,我们为RSN系统开发了一个多目标分辨率模型。三种波形,即简单脉冲,线性调频(LFM)脉冲和巴克码(调相脉冲)被用于评估分辨率性能。还分析了具有附加噪声的通损耗衰落信道上的分布式RSN的多目标检测性能。对于融合中心,我们使用似然比(LR)决策规则,该规则会根据信道信噪比(SNR),虚警概率和雷达传感器(RSs)数量简化为测试统计数据。仿真结果表明,较高的SNR时,检测和分辨率性能更好。在RSN中增加RS的数量可以改善性能,将SNR放大和增加RS的数量相结合可以得到更好的RSN系统性能,而增加目标数量可以对检测结果产生更坏的影响。

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