Active Radar Network Research Articles

无源雷达补盲效果推演评估研究

无源雷达为有源雷达网补盲是其作战运用的重要方面，补盲效果评估是作战决策的重要依据。针对复杂空情条件下无源雷达实施空情补盲的作战协同指挥决策问题，采用计算机仿真方法，构建对空情报雷达网对抗干扰机掩护下空中目标的作战推演模型和补盲效果评估模型。基于该仿真模型进行一定战术背景下的作战推演和评估解算，能够给出无源雷达补盲作战效能量化评估数据，并进行干扰前后、补盲前后两个不同维度的补盲效果对比分析和可视化表现，为复杂空情条件下无源雷达补盲作战决策提供依据。最后，给出了一个仿真实例，验证了模型的有效性。 Air supplement for active radar network is an important aspect of passive radar, and operational effectiveness evaluation is also the basis of operational decision. According to operational coordination decision for passive radar implementing air supplement in condition of complex air situation, operational deduction model and evaluation model are structured for intelligence radar network resisting air target in terms of jammer cover. By operational deduction and evaluation in condition of tactical background based on above models, operational effectiveness quantitative data of passive radar is outputted, and by carrying on comparative analysis and visual performance of effectiveness for before and after jam and compensation, it can provide the basis of command decision. Finally, a simulation example is given and the validity of the model is verified.

Cramer-Rao Lower Bound Evaluation for Linear Frequency Modulation Based Active Radar Networks Operating in a Rice Fading Environment.

This paper investigates the joint target parameter (delay and Doppler) estimation performance of linear frequency modulation (LFM)-based radar networks in a Rice fading environment. The active radar networks are composed of multiple radar transmitters and multichannel receivers placed on moving platforms. First, the log-likelihood function of the received signal for a Rician target is derived, where the received signal scattered off the target comprises of dominant scatterer (DS) component and weak isotropic scatterers (WIS) components. Then, the analytically closed-form expressions of the Cramer-Rao lower bounds (CRLBs) on the Cartesian coordinates of target position and velocity are calculated, which can be adopted as a performance metric to access the target parameter estimation accuracy for LFM-based radar network systems in a Rice fading environment. It is found that the cumulative Fisher information matrix (FIM) is a linear combination of both DS component and WIS components, and it also demonstrates that the joint CRLB is a function of signal-to-noise ratio (SNR), target’s radar cross section (RCS) and transmitted waveform parameters, as well as the relative geometry between the target and the radar network architectures. Finally, numerical results are provided to indicate that the joint target parameter estimation performance of active radar networks can be significantly improved with the exploitation of DS component.

Direct target localization with an active radar network

Target localization is commonly achieved by a decentralized two-step approach. Recently, studies proposed new promising centralized one-step algorithms. Using an active radar network, we study here the detection and estimation performances of a (one step, Direct Position Determination based) direct target position and velocity determination algorithm. Numerical results show the improvement of the proposed algorithm compared to a classical two-step method. The proposed method is also experimentally verified on real data.

Direct target localization and deghosting in active radar network

Recently proposed, the one step localization approach for active sensor network estimates the target location directly from the sensor raw signals, as opposed to the classical decentralized two steps approach where intermediate signal processing (e.g., measurement estimation, detection) is performed on each sensor node. A composite multiple hypothesis test based on the generalized maximum likelihood (GML) rule is proposed to cope with the sidelobes of the one step cost function that may cause detection and localization of ghost targets. The proposed method has been verified both on synthetic and real data.