时变多径信道中目标跟踪的计算高效似然近似方法
A Computationally Efficient Likelihood Approximation for Target Tracking in Time-Varying Multipath Channels
AI总结：
针对时变多径信道下主动声呐低信噪比目标跟踪的高计算开销问题，提出一种伯努利先跟踪后检测滤波的计算高效似然近似方法，验证了其目标确认与定位性能优于传统基于恒定虚警率的跟踪方法。
AI中文摘要：
浅水环境中的主动声呐目标跟踪颇具挑战性，因为弱目标回波嵌入在包含结构化多径分量的时变背景中。传统的先检测后跟踪方法依赖阈值化检测，可能会丢弃弱目标证据或因多径诱导的检测生成虚假航迹。在低信噪比下，先跟踪后检测滤波可通过直接利用原始传感器测量中的弱目标信息提升跟踪性能，但直接考虑时变背景会导致目标-背景联合推断问题，计算开销极大。为解决该问题，本文针对伯努利先跟踪后检测滤波，提出一种基于物理动机且计算高效的原始传感器测量似然近似方法。背景中的多径分量在原始传感器测量域中建模，并使用扩展卡尔曼滤波进行递归跟踪。通过忽略目标与背景状态间的后验依赖关系，利用预测的背景统计量构建伯努利滤波器的近似目标存在与目标不存在似然。使用统计模型和BELLHOP生成的测量进行评估，结果表明，与基于恒定虚警率的跟踪相比，本文方法的目标确认和定位性能均有所提升。这些结果说明，通过计算可处理的近似似然显式考虑背景，可在无需完整目标-背景联合推断的情况下利用弱目标信息。
英文摘要：
Active sonar target tracking in shallow-water environments is challenging when weak target echoes are embedded in a time-varying background containing structured multipath components. Conventional detect-before-track methods rely on thresholded detections, which may discard weak target evidence or generate false tracks from multipath-induced detections. At low signal-to-noise ratios, track-before-detect filtering can improve tracking performance by exploiting weak target information directly from raw sensor measurements, but directly accounting for the time-varying background leads to a joint target--background inference problem, which is computationally demanding. To address this, this paper develops a physics-motivated and computationally efficient approximation of the raw sensor measurement likelihood for Bernoulli track-before-detect filtering. The multipath components in the background are modeled in the raw sensor measurement domain and recursively tracked using an extended Kalman filter. By neglecting the posterior dependence between the target and background state, the predicted background statistics are used to construct approximate target-present and target-absent likelihoods for the Bernoulli filter. Evaluations using measurements generated from the statistical model and BELLHOP show improved target-confirmation and localization performance over constant-false-alarm-rate-based tracking. These results indicate that explicitly accounting for the background through a computationally tractable approximate likelihood can exploit weak target information without requiring full joint target--background inference.
