TAILIEUCHUNG - handbook of multisensor data fusion phần 4

thấp quan sát được TMA Sử dụng phương pháp tiếp cận ML-PDA với tính năng Phần này xem xét các vấn đề phân tích chuyển động mục tiêu (TMA) - dự toán của các tham số quỹ đạo của một mục tiêu vận tốc không đổi | Low Observable TMA Using the ML-PDA Approach with Features This section considers the problem of target motion analysis TMA estimation of the trajectory parameters of a constant velocity target with a passive sonar which does not provide full target position measurements. The methodology presented here applies equally to any target motion characterized by a deterministic equation in which case the initial conditions a finite dimensional parameter vector characterize in full the entire motion. In this case the batch maximum likelihood ML parameter estimation can be used this method is more powerful than state estimation when the target motion is deterministic it does not have to be linear . Furthermore the ML-PDA approach makes no approximation unlike the PDAF in Equation . Amplitude Information Feature The standard TMA consists of estimating the target s position and its constant velocity from bearings-only wideband sonar measurements corrupted by Narrowband passive sonar tracking where frequency measurements are also available has been The advantages of narrowband sonar are that it does not require a maneuver of the platform for observability and it greatly enhances the accuracy of the estimates. However not all passive sonars have frequency information available. In both cases the intensity of the signal at the output of the signal processor which is referred to as measurement amplitude or amplitude information AI is used implicitly to determine whether there is a valid measurement. This is usually done by comparing it with the detection threshold which is a design parameter. This section shows that the measurement amplitude carries valuable information and that its use in the estimation process increases the observability even though the amplitude information cannot be correlated to the target state directly. Also superior global convergence properties are obtained. The pdf of the envelope detector output . the AI a when the .

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