CN111523667A - Neural network-based RFID (radio frequency identification) positioning method - Google Patents
Neural network-based RFID (radio frequency identification) positioning method Download PDFInfo
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Abstract
The invention discloses a neural network-based RFID (radio frequency identification) positioning method, which comprises a card reader and a processor, wherein the processor realizes the following steps through a program code: the positioning algorithm module reads the coordinate data of the card reader, the coordinate data of the reference label and the real coordinate data of the point to be measured, and obtains the measured coordinate and the positioning error data of the point to be measured by adopting a LANDMRC algorithm, a BVIRE algorithm and a VIRE algorithm; an error analysis module extracts the time stamp and the main error characteristics in the measured coordinates of the point to be measured and the positioning error data to construct a training set; the positioning correction module corrects the coordinate of the minimum error position point and the time of the minimum error position point to output the coordinate correction value of the point to be measured and the corresponding position coordinate with the maximum occurrence possibility.
Description
Technical Field
The invention mainly relates to the technical field of indoor positioning based on an RFID network, in particular to an RFID positioning method based on a neural network.
Background
Currently, in an indoor environment, a Radio Frequency Identification (RFID) technology has been widely regarded and applied due to its characteristics of information carrying function, reliable transmission Identification, and the like. The RFID positioning technology utilizes the unique identification characteristic of the tag to the object, and obtains the position information of the electronic tag according to the signal sent by the electronic tag and received by the card reader. The basis of the RFID indoor positioning is that parameters such as the received signal strength and the phase of an RFID signal are combined, the distance and the direction are calculated by using a positioning algorithm, namely, a plurality of card readers are placed in the room in advance, when a moving object with an electronic tag enters the identification range of the card readers, the received signal can be uploaded to an upper computer, and the upper computer can realize the positioning algorithm by calculating the signal attenuation degree of the tag and the position information of an adjacent known tag.
The indoor positioning algorithms based on RFID can be divided into two broad categories: non-ranging positioning algorithms and ranging positioning algorithms. The RSSI positioning method in the ranging positioning algorithm is easier to operate compared with other positioning methods, the RSSI positioning method is used for estimating the position of an object by measuring a signal strength value RSSI, and more advanced positioning algorithms comprise a LANDMAC algorithm, a BVIRE algorithm and a VIRE algorithm.
The LANDMARC (location Identification Based on Dynamic active rfid allocation) Algorithm is much more positive because of its simplicity and high positioning accuracy, and Based on the LANDMARC Algorithm, a boundary virtual tag Algorithm (boundary virtual Label Algorithm, BVIRE) is obtained by inserting grid virtual reference tags and boundary reference tags with similar squares. Two weights are adopted in the boundary virtual label algorithm, 1 more weight is adopted than the LANDMARC algorithm, and a threshold TH is adopted in the selection of adjacent labels to eliminate large error labels with small probability, so that the positioning accuracy of the BVIRE algorithm is greatly improved. The VIRE algorithm introduces the concepts of virtual reference labels and adjacent maps while utilizing the LANDMARC principle, provides the concept of the virtual reference labels, estimates the signal intensity value of the virtual reference labels by utilizing an interpolation method, and performs later-stage calculation positioning by taking the virtual reference labels as actual reference labels, thereby improving the accuracy and the positioning calculation feasibility.
Conventional solutions require that all test samples be stored in a database. This will seriously affect the positioning efficiency and accuracy, since in a complex indoor environment most of the test samples are noisy. And a large amount of data needs to be collected as a comparison to achieve high accuracy.
In recent years, machine learning has been increasingly applied to the processing of rfid. However, since the acquired RSSI signals are usually noisy, the existing features are selected based on raw data, and a large amount of calculation and tuning work is required to improve the model accuracy. Therefore, the organic combination of error correction and positioning calculation realizes the reduction of positioning error, the improvement of positioning accuracy and the increase of positioning calculation efficiency, and becomes the problem to be solved urgently.
Disclosure of Invention
The invention aims to design a neural network-based RFID positioning method, which analyzes errors by using a neural network method, reduces original noise and data quantity, and reduces algorithm errors, calculated quantity and calculation complexity.
In view of the above issues, the present invention provides a neural network-based RFID positioning method, which reads the minimum error value of the position data of a moving object at a certain position by calculating a random position-based card reader. Positioning calculation is carried out according to the optimization schemes of the three positioning algorithms, data analysis is carried out on the result data of the positioning calculation to obtain error data of the three algorithms, the error data is used as a modeling object, modeling is carried out by utilizing a neural network algorithm, and the position coordinate and the corresponding time which are possible to appear to the maximum extent are obtained. The method integrates the LANDMARC algorithm, the BVIRE algorithm and the VIRE algorithm, and combines the neural network algorithm to obtain a more efficient and accurate result, thereby realizing the improvement of the precision of the RFID positioning.
The achievement of the invention is realized by the following steps:
an RFID positioning method based on a neural network comprises a card reader and a processor, wherein the processor realizes the following steps through program codes:
the positioning algorithm module reads the coordinate data of the card reader, the coordinate data of the reference label and the real coordinate data of the point to be measured, and obtains the measured coordinate and the positioning error data of the point to be measured by adopting a LANDMRC algorithm, a BVIRE algorithm and a VIRE algorithm;
an error analysis module extracts the time stamp and the main error characteristics in the measured coordinates of the point to be measured and the positioning error data to construct a training set;
the training set processes the time stamp and the error main characteristics through a grid encryption algorithm and a neural network algorithm to output a minimum error position point coordinate and a minimum error position point time;
and the positioning correction module corrects the coordinate of the minimum error position point and the time of the minimum error position point and outputs a coordinate correction value of the point to be measured and the corresponding position coordinate with the maximum occurrence possibility.
Further, the neural network algorithm is to selectively input the RNN, CNN and LSTM neural network algorithms to obtain the coordinates of the minimum error position point and the time of the minimum error position point after the positioning errors of the LANDMRC algorithm, the BVIRE algorithm and the VIRE algorithm are subjected to efficiency calculation and data stability evaluation.
Furthermore, the positioning correction module corrects the minimum error position point coordinate and the minimum error position point time, searches the corresponding measured coordinate of the point to be measured through data screening of the minimum error value, performs data analysis on the measured coordinate of the point to be measured and the real coordinate of the point to be measured through path fitting of a moving object, and realizes positioning correction of the measured coordinate of the point to be measured
Advantageous effects
1. The invention provides a neural network analysis algorithm based on an RFID indoor positioning algorithm, and the calculation accuracy and the positioning calculation complexity are improved.
2. The invention provides a neural network analysis method based on a time stamp and an error value as a feature set, and a feedback neural network is utilized to convert a positioning problem into an error problem for analysis. Compared with the prior art, the method combines a plurality of algorithms, effectively saves data resources, does not directly analyze the original data, and improves the data operation analysis speed.
Drawings
FIG. 1 is a schematic diagram of the positioning algorithm results.
FIG. 2 is a logical structure diagram of an analysis method based on three RFID positioning algorithms and a neural network;
fig. 3 is a logic structure diagram of a feedback neural network.
Detailed Description
The data analysis, network training and data calculation of the present invention are described in detail below with reference to the accompanying drawings. The whole positioning principle block diagram is shown in fig. 1, fig. 2 and fig. 3.
Step 1: three positioning algorithm modules are utilized to carry out data analysis, input data are card reader coordinate data, reference label coordinate data and real coordinate data of the point to be measured, output data are measured coordinates and positioning errors of the point to be measured, and a schematic diagram of a positioning result is shown in figure 1.
The method comprises the steps of performing parallel calculation through three positioning algorithm modules of a LANDMARC algorithm, a BVIRE algorithm and a VIRE algorithm, performing analog simulation by using Matlab, inputting coordinate data of a card reader, coordinate data of a reference label and real coordinate data of a point to be measured, realizing positioning calculation by calculating the received signal intensity of the reference label and the label to be measured, starting calculation of the three algorithms simultaneously according to a distributed principle, and outputting data as the measured coordinate of the point to be measured and a positioning error.
Step 2: the error analysis module performs network training by using a neural network algorithm, inputs data as positioning errors of the three algorithms, simultaneously inputs RNN, CNN and LSTM neural network algorithms, and outputs data as a minimum error value and a corresponding timestamp.
The method comprises the steps of preprocessing positioning error data, inputting positioning errors of three algorithms by utilizing a grid computing algorithm and a neural network algorithm, simultaneously inputting RNN, CNN and LSTM neural network algorithms, selecting the neural network algorithm according to the computing efficiency and the data stability, training by using a timestamp and an error value as main characteristic sets, and outputting the minimum value of the positioning errors and the timestamp corresponding to the occurrence of the positioning errors by training the selected neural network algorithm.
And step 3: and performing data calculation by using a positioning correction algorithm, wherein input data is the minimum value of errors, and output data is a coordinate correction value of the point to be measured, the corresponding maximum possible position coordinate and the corresponding time.
Namely, a positioning correction algorithm is realized by selecting the minimum positioning error value, the corresponding measured coordinate of the point to be measured is searched for by screening the data with the minimum error value, the measured coordinate of the point to be measured and the real coordinate of the point to be measured are subjected to data analysis by path fitting of a moving object, the positioning correction of the measured coordinate of the point to be measured is realized, and the corrected value of the coordinate of the point to be measured and the corresponding position coordinate with the maximum occurrence possibility are output.
Claims (3)
1. An RFID positioning method based on a neural network comprises a card reader and a processor, and is characterized in that: the processor realizes the following steps through program codes:
the positioning algorithm module reads the coordinate data of the card reader, the coordinate data of the reference label and the real coordinate data of the point to be measured, and obtains the measured coordinate and the positioning error data of the point to be measured by adopting a LANDMRC algorithm, a BVIRE algorithm and a VIRE algorithm;
an error analysis module extracts the time stamp and the main error characteristics in the measured coordinates of the point to be measured and the positioning error data to construct a training set;
the training set processes the time stamp and the error main characteristics through a grid encryption algorithm and a neural network algorithm to output a minimum error position point coordinate and a minimum error position point time;
and the positioning correction module corrects the coordinate of the minimum error position point and the time of the minimum error position point and outputs a coordinate correction value of the point to be measured and the corresponding position coordinate with the maximum occurrence possibility.
2. The neural network-based RFID location method of claim 1, wherein:
the neural network algorithm is characterized in that after the positioning errors of the LANDMARC algorithm, the BVIRE algorithm and the VIRE algorithm are subjected to efficiency calculation and data stability evaluation, the positioning errors are selectively input into the RNN, CNN and LSTM neural network algorithms to obtain the coordinates of the minimum error position point and the time of the minimum error position point.
3. The neural network-based RFID location method of claim 1, wherein:
the positioning correction module corrects the coordinate of the minimum error position point and the time of the minimum error position point, searches the corresponding measured coordinate of the point to be measured through data screening of the minimum error value, performs data analysis on the measured coordinate of the point to be measured and the real coordinate of the point to be measured through path fitting of a moving object, and realizes positioning correction of the measured coordinate of the point to be measured.
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