低信噪比环境下, 原始数据未知门限的机动目标跟踪是一个比较棘手的问题。提出了一种交互式多模型伯努利(IMM-Bernoulli）检测前跟踪(TBD）算法, 该算法结合交互式多模型算法对滤波器中每个目标状态的采样粒子进行预测, 利用伯努利滤波对目标粒子进行递归, 粒子更新阶段结合TBD算法进行,最终实现目标存在概率及分布密度的更新估计。算法对粒子预测时采用多个模型参与转移预测, 使得预测粒子更加接近目标真实运动状态, 兼备了伯努利TBD算法和交互式多模算法的特点, 可用于处理低信噪比环境下机动弱目标检测跟踪问题, 且对目标状态的估计更加精准。仿真实验表明, 该滤波器能够实时地估计出目标位置, 比传统的伯努利TBD算法具有更好的滤波性能。

Maneuvering target tracking using the raw data with unknown threshold under low Signal-To-Noise Ratio (SNR) circumstance is a difficult problem.In this paper, an Interactive Multiple Model (IMM) Bernoulli Track-Before-Detect (TBD) algorithm is proposed.The algorithm uses the IMM method to predict the sampling particles in each target state of the filter, the Bernoulli filter for the recursion of the target particles, and the TBD algorithm in the particle updating phase, thus to realize the updating estimation of the existence probability and distribution density of the targets.In the particle prediction, several models are used to transfer the prediction, which makes the motion state of the predicted particles similar to that of the real targets.The method combines the features of the Bernoulli TBD algorithm with those of the IMM method, which can be used for the high-accuracy detection and tracking of maneuvering targets under the condition of low SNR.Simulation results show that the proposed filter can estimate the target position in real time, and has better filtering performance than the traditional Bernoulli TBD algorithm.