Indoor moving target positioning method and device and computer equipment

A technology of moving targets and positioning methods, applied in the field of indoor positioning, which can solve problems such as filter divergence, wireless signal reflection, diffraction and refraction, and indoor positioning systems that cannot be used normally, and achieve strong robustness and good estimation effects

Pending Publication Date: 2021-12-07
HOHAI UNIV
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  • Claims
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Problems solved by technology

However, in the actual indoor environment, due to the complex environment, wireless signals are prone to reflection, diffraction, and refraction. Due to these factors, the indoor positioning system is often affected by non-Gaussian noise with multiple peaks and heavy tails. At this time, the accuracy of the Kalman filter based on the MMSE criterion will be greatly reduced, and even cause the filter to diverge, making the indoor positioning system unable to work normally. use

Method used

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  • Indoor moving target positioning method and device and computer equipment
  • Indoor moving target positioning method and device and computer equipment
  • Indoor moving target positioning method and device and computer equipment

Examples

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Embodiment 1

[0178] In order to fully verify the rationality of the proposed MCUKF method, a Zigbee indoor positioning system based on CC2530 is used for experiments. In the Zigbee indoor positioning system based on CC2530, there is an unknown node and four AP points, and the four AP points are randomly distributed. Determine the coordinates of the AP point. Then, the PC is used to collect signal strength data when the unknown node is stationary, and each point is collected for 3 minutes each time, and 50 sets of data are selected from it, and the distribution characteristics of the signal strength errors of the four APs received by the unknown node are tested and analyzed. According to Kolmogorov-Smirnov According to the analysis of test results, if the test rejects the null hypothesis at the 15% significant level, the signal intensities from the unknown node to the 4 APs are all non-normally distributed. The signal strength received by the unknown node from the AP presents a non-Gaussian...

Embodiment 2

[0181] In the actual complex indoor environment, the positioning accuracy is often affected by non-Gaussian noise with multiple peaks and heavy tails, which will cause the accuracy of traditional nonlinear filtering algorithms to decrease or even diverge. The linear robust filtering method, the method proposed by the present invention introduces the maximum correlation entropy on the basis of the traditional nonlinear filtering UKF, so that the filtering has the robustness against non-Gaussian. The following is a specific simulation experiment to verify, and compare and analyze to select the appropriate core width. Deploy four APs in the indoor positioning area, and their corresponding coordinates are A1(0,0), A2(0,10), A3(10,0), A4(10,10), assuming the state of the unknown node in the experiment vector The initial real state is provided by LS (position unit m, speed unit m / s) The initial state estimation error covariance matrix is ​​set as P(0∣0)=diag([1,0.5,1,0.5]), the ...

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Abstract

The invention discloses an indoor moving target positioning method. The method comprises the following steps: establishing a state equation and an observation model of unknown moving target node motion; according to the state equation, obtaining a one-step predicted value and a covariance matrix of a system state quantity through a group of Sigma point sets calculated by UT and corresponding weights thereof, and observing the Sigma point sets; according to the observation model, calculating an observation prediction value of the observation Sigma point sets, and obtaining an observation prediction value of the system; and according to a cost function based on the maximum correlation entropy, fusing the observation predicted value of the system, obtaining an observation covariance matrix and an observation cross covariance matrix of the reconstructed observation model, determining a Kalman gain matrix, estimating the state quantity of the system, and determining the position coordinates of an unknown moving target node. According to the indoor positioning method based on the MCUKF, indoor positioning under the condition that non-Gaussian noise occurs in measurement noise can be better completed, the positioning precision is higher than that of LS, EKF and UKF indoor positioning methods, and the robustness is higher than that of the LS, EKF and UKF indoor positioning methods.

Description

technical field [0001] The present invention relates to the field of indoor positioning, in particular to an indoor moving target positioning method, device and computer equipment based on maximum correlation entropy unscented Kalman filter (MCUKF). Background technique [0002] In recent years, the demand for indoor positioning has been increasing. It is precisely because of the large-scale application scenarios and high commercial value of indoor positioning that they are widely used in medical centers, smart homes, shopping centers, personnel positioning and tracking in underground mines, cargo tracking and other fields. The indoor positioning system based on RSS has received extensive attention since its appearance without specific hardware. Since the Kalman filter based on the MMSE criterion is an optimal state estimation method, it has also been introduced into indoor positioning and achieved good results. In addition, some improved algorithms of Kalman filtering suc...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01S5/02H03H17/02
CPCG01S5/0294H03H17/0257
Inventor 曹宁马利毛明禾
Owner HOHAI UNIV
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