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ADS-B (Automatic Dependent Surveillance-Broadcast) and (Traffic Collision Avoidance System) data fusion method based on variational Bayes estimation

A variational Bayesian, data fusion technology, applied in computing, computer parts, instruments, etc.

Inactive Publication Date: 2017-08-18
SHANGHAI JIAO TONG UNIV
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  • Abstract
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  • Application Information

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Problems solved by technology

[0003] The present invention aims at the above-mentioned deficiencies that exist in the prior art, proposes a kind of ADS-B and TCAS data fusion method based on variational Bayesian estimation, utilizes variational Bayesian (VB) method to estimate sensor time-varying noise online, as The parameter input of observation noise in the process of IMM interactive multi-model filtering changes the situation that observation noise is used as a known fixed constant in the past

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  • ADS-B (Automatic Dependent Surveillance-Broadcast) and (Traffic Collision Avoidance System) data fusion method based on variational Bayes estimation
  • ADS-B (Automatic Dependent Surveillance-Broadcast) and (Traffic Collision Avoidance System) data fusion method based on variational Bayes estimation
  • ADS-B (Automatic Dependent Surveillance-Broadcast) and (Traffic Collision Avoidance System) data fusion method based on variational Bayes estimation

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Embodiment Construction

[0060] Such as figure 1 As shown, it is a schematic diagram of the implementation of the system, which specifically includes: target generation module, TCAS monitoring module, ADS-B monitoring module, data preprocessing module, noise estimation module, information fusion module and two groups of TCAS and ADS-B data As the input CS current statistical tracking model module, IMM fixed sampling module and IMM variable sampling module, among them: target generation module is connected with TCAS monitoring and ADS-B monitoring and transmits track information, TCAS monitoring module and ADS-B monitoring module After the data is converted and unified by the coordinate system of the data preprocessing module, the TCAS noise estimate and the ADS-B noise estimate are obtained through the variational Bayesian online noise estimation module, which are respectively input as noise parameters and transmitted to two groups of CS current statistical tracking model modules, The IMM fixed sampl...

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Abstract

The invention relates to an ADS-B (Automatic Dependent Surveillance-Broadcast) and (Traffic Collision Avoidance System) data fusion method based on variational Bayes estimation. Time-varying noise of a sensor is on-line estimated by use of a variational Bayes algorithm and is used as a parameter input for observation noise in an IMM (Interactive Multiple Model Algorithm) filter process, and the previous situation that the observation noise is used as a known fixed constant is changed. Because the sensor noise may change due to own characteristics of the sensor and environmental factor in practical application, if the observation noise is set by use of prior knowledge, deterioration of a tracking effect is easily caused. The noise estimated online by use of the variational Bayes can also provide guarantee for the safety of a fusion system besides the situation of the prior knowledge lack of noise. The method plays a good sensing role for the representation situation of harmful elements in the noise.

Description

technical field [0001] The present invention relates to a technology in the field of aircraft monitoring, in particular to a method for data fusion of Automatic Dependent Surveillance-Broadcast (ADS-B) and Air Collision Avoidance System (TCAS) based on variational Bayesian estimation, which is particularly suitable for Handle scenarios where sensor noise is time-varying and unknown, and where safety hazards can be represented in the noise during fusion. Background technique [0002] The current research on the maneuvering target tracking algorithm mainly focuses on the interactive multi-model tracking algorithm (IMM), which has been proved to be the best cost-effective algorithm in the current hybrid system estimation algorithm, and it is easy to implement on the computer. The basic idea of ​​the IMM algorithm is to use different models to match the different motion states of the target, and the filters representing each model work in parallel, and assume that the switching ...

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Application Information

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IPC IPC(8): G06K9/62
CPCG06F18/251
Inventor 肖刚戴周云张强赵俊豪刘独玉
Owner SHANGHAI JIAO TONG UNIV