The accuracy of cooperative localization for multiple autonomous underwater vehicles (AUVs) equipped with low precise proprioceptive localization sensors can be improved by using relative location information between individuals and Bayesian filtering. However, when the relative location measurement errors are high, its accuracy will be reduced. Two measurement for rough estimation algorithms under the constraint environment of the cooperative structure are developed in this paper: the first algorithm is based on the underwater acoustic isotropic transmission. And the second algorithm is based on the common observation environment. In the first algorithm, it builds under the assumption that the distance errors calculated from the simultaneous omnidirectional response signals from the same transmitting source have correlated. Similarly, in the second algorithm, the assumption that “common observation environment” is correlated is made. First, the correlation between the errors is used roughly to estimate the measurement of information. Then, a suitable filter is applied to fuse the rough estimation measurement with dead-reckoning estimation that improves the location estimation accuracy. The final simulation, by changing the AUV formation navigation paths and the sensor observation noises, shows the proposed processing methods have effectiveness and consistency compared to the traditional algorithm.

通过使用个体之间的相对位置信息和贝叶斯过滤，可以提高配备有低精确本体感受定位传感器的多个自主水下航行器（AUV）的协作定位精度。然而，当相对位置测量误差高时，其精度将降低。本文针对协同结构约束环境下的粗略估计算法，提出了两种测量方法：第一种基于水下声各向同性传输算法。第二种算法基于共同的观测环境。在第一种算法中，它是基于以下假设建立的：从来自同一发射源的同时全向响应信号计算出的距离误差是相关的。同样，在第二种算法中，假设“公共观察环境”是相关的。首先，误差之间的相关性大致用于估计信息的度量。然后，应用合适的滤波器将粗略估计测量值与推死推算估计值融合在一起，从而提高位置估计精度。最终仿真通过改变AUV编队的导航路径和传感器的观测噪声，表明所提出的处理方法与传统算法相比具有有效性和一致性。应用合适的滤波器将粗略估计测量与死区推算估计融合在一起，从而提高位置估计的准确性。最终仿真通过改变AUV编队导航路径和传感器观测噪声，表明所提出的处理方法与传统算法相比具有有效性和一致性。应用合适的滤波器将粗略估计测量结果与死区推算估计结果融合在一起，从而提高位置估计的准确性。最终仿真通过改变AUV编队导航路径和传感器观测噪声，表明所提出的处理方法与传统算法相比具有有效性和一致性。