浅水环境中,多径干扰严重影响目标探测的性能,导致虚警概率增加。有效的多径干扰对消算法是浅水环境中目标检测的关键。自适应LMS算法能有效抑制多径干扰,但当干扰持续较长或空间分布范围较大时,需要使用高阶次滤波器,计算量很大,给实际应用带来困难。通过分析浅水环境下多径干扰的形成机理和分布特点,建立其理论模型,并提出阵列模型下的权系数部分更新的变步长自适应多径干扰对消算法。该算法采用权系数的周期性部分更新算法和基于改进Sigmoid函数的变步长算法,较好解决了自适应干扰对消算法中计算量与收敛速度之间的矛盾。理论分析与仿真结果表明,该算法能在有效地降低计算量的同时,仍保持较快的收敛速度,并具有良好的多径干扰对消效果。
Abstract
In the shallow water environment, multipath interference will affect the performance of underwater target detection and increase the false alarm probability. Thus, algorithm about multipath interference cancellation is a key to the target detection in shallow water environment. Adaptive LMS algorithm can suppress multipath interference effectively. But when the disturbance lasts too long or its space distribution ranges too wide, the adaptive LMS algorithm needs filter with higher order and huge amount of calculation, bringing difficulties to practical applications. Through analyzing the formation mechanism and distribution characteristics of multipath interference in shallow water, and establishing its theoretical model. Then, we propose a variable step size adaptive multipath interference cancellation algorithm based on partial-update of the weight coefficients. This algorithm resolves the contradiction between calculation amount and convergence speed by periodically updating weight coefficients and using variable step method based on improved Sigmoid function. Theoretical analysis and simulation results show that the proposed algorithm performs well on multipath interference cancellation. It can effectively reduce the calculation amount while keeping a fast convergence speed.
关键词
多径干扰 /
自适应干扰对消 /
部分更新 /
变步长
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Key words
multipath interference /
adaptive interference suppress /
partial-update /
variable step size
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