防恶意干扰的VANET双群联合定位算法

传统的无线定位方法直接应用于车辆自组织网络(VANET)时，会被窃听方利用定位信息，从而导致严重的安全隐患。为此，提出基于Bancroft方法的双群组合定位算法,用于保障车辆自组织网络中车辆定位的安全性和准确性。算法利用两个群组的智能车辆节点分别进行定位，利用两组解分别反求伪距值，如果所获得的伪距值与经过节点钟差修正后的原始伪距值基本一致，则认为这一组解为最终的定位结果，从而保证了定位的正确性。同时，双群定位的信息分散性保证了系统整体的安全性。理论分析及算法仿真表明,双群联合定位算法可使窃听方的误码率稳定在较高水平。

A dual group joint localization algorithm for VANET with anti-malicious disturbance ability

The traditional wireless localization method will be tapped by using location information when it is directly applied to the vehicle ad hoc network(VANET),which leads to serious security risks. To solve this problem,a dual group localization algorithm based on the Bancroft method is presented,which is used to protect the VANET in vehicle safety and accuracy of location. The algorithm uses intelligent vehicle nodes of two groups for localization,which means that the solutions of the two groups are used to inversely get the pseudo range value .If the obtained pseudo range values are basically the same as the original ones corrected by the node clock,the solutions of the group will be regarded as the final results,thus ensuring the localization accuracy. Meanwhile,the security of the whole system is ensured by the information dispersion of the double group localization.Theoretical analysis and simulation results show that the dual group joint localization algorithm can stabilize the bit error rate(BER) of the eaves dropper in a high level.