Maximising Detection Performance Using High Performance Processing of Multi-Sensor Data

该项目将开发用于潜艇战的最先进的信号处理算法。未来的潜艇传感将涉及机器人潜艇和水面舰艇以及部署在海底的自主传感器。为了避免泄露传感器位置的可能性，并使潜艇能够避免被探测到，使用水听器阵列被动侦听信号比主动传感更可取。不幸的是，声能的水下传播是复杂的。为了从单个传感器检测隐身目标，已经开发了称为检测前跟踪（Track-Before-Detect）的信号处理算法。这些算法使用顺序数值贝叶斯推理算法来处理长时间尺度上的原始传感器数据，在长时间尺度上，目标的轨迹导致不太可能是噪声的结果的传感器伪影。这些算法的计算要求高，但服从并行处理，并已被证明提供显着的优势，在检测性能。这种融合算法的高级变体仔细考虑了物理模型和统计模型的组合：物理模型可以准确地预测数据，但通常在计算上要求很高或难以鲁棒地校准;统计模型可以捕获物理上没有建模的现象的影响，但根据定义，不能像物理模型那样准确地预测将被感知的东西。该项目的重点将是开发具有多个传感器模型的Track-Before-Detect算法，这些传感器考虑物理和统计模型的组合，同时配置信号处理以最大限度地提高检测性能。

This project will develop state-of-the-art in signal processing algorithms to be used in submarine warfare. Future sensing of submarines will involve robotic submarines and surface-ships as well as autonomous sensors deployed on the seabed. To avoid the potential to give away the position of the sensors and for the ability of a submarine to avoid detection, passive listening for signals using an array of hydrophones is preferable to active sensing. Unfortunately, the underwater propagation of acoustic energy is complicated. Signal processing algorithms called Track-Before-Detect have been developed to detect stealthy targets from single sensors. These algorithms use sequential numerical Bayesian inference algorithms to process the raw sensor data over long timescales over which the trajectories of the targets cause sensor artefacts that are unlikely to be the result of noise. These algorithms are computationally demanding but amenable to parallel processing and have been demonstrated to provide significant advantages in terms of detection performance. Advanced variants of such fusion algorithms carefully consider a combination of physical and statistical models: physical models can accurately predict the data but are typically very computationally demanding or difficult to calibrate robustly; statistical models can capture the effect of phenomena that are not modelled physically, but cannot, by definition, predict what will be sensed as accurately as physical models. The focus of the project will be developing Track-Before-Detect algorithms with models for multiple sensors that consider a combination of physical and statistical models while configuring the signal processing to maximise detection performance.