CN113194427A - Identification method, system and device based on soft-decision visible and non-visible channels - Google Patents

Identification method, system and device based on soft-decision visible and non-visible channels Download PDF

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CN113194427A
CN113194427A CN202110485752.1A CN202110485752A CN113194427A CN 113194427 A CN113194427 A CN 113194427A CN 202110485752 A CN202110485752 A CN 202110485752A CN 113194427 A CN113194427 A CN 113194427A
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CN113194427B (en
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王威
谢景丽
刘鑫一
侯俊
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Changan University
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Abstract

The invention discloses a method for identifying visual and non-visual channels based on soft decision, which comprises the following specific processes: acquiring data packet information and calculating channel characteristics; calculating a probability distribution function of the obtained channel characteristics; fitting the probability distribution function of S2 with a plurality of gaussian functions; judging whether the data packet information belongs to a line-of-sight environment or is received in a non-line-of-sight environment by adopting soft decision; the invention combines two irrelevant characteristics to carry out soft decision to identify the sight distance/non-sight distance, and compared with a single characteristic, the accuracy of identification is improved, the single characteristic needs to train signals to find a proper threshold value, while the invention does not need to carry out excessive training on data, only needs to use the probability distribution of a training sample and uses a soft discrimination formula to calculate and identify the type of a data packet, thereby greatly shortening the running time, and only needs less prior knowledge to obtain higher identification accuracy without training and calculating the threshold value for multiple times.

Description

Identification method, system and device based on soft-decision visible and non-visible channels
Technical Field
The invention belongs to the technical field of wireless communication, relates to the field of location service application, and particularly relates to a method, a system and a device for identifying visual and non-visual channels based on soft decision.
Background
With the increasing popularity of wireless devices, mobile terminal devices with positioning functions play an important role in people's daily life. At present, a Global Positioning System (GPS) is used as a Positioning System for a mobile terminal in daily life, a satellite navigation System is used as a source of Positioning information, the satellite System can acquire a geographical position which can be observed by a satellite according to an instruction, and the satellite System has the characteristics of high accuracy, high efficiency and the like.
However, both GPS and the chinese beidou navigation positioning system have very weak power of satellite signals reaching the ground, and with the development of the human society, more and more buildings appear, which affects the observation of satellites, i.e. the so-called "city effect", most of people's daily life occurs in indoor environments, and under the influence of the "city effect", GPS system signals are affected by houses and trees and cannot play a role, so that the solution of the indoor positioning technology has a crucial meaning for the development of high-precision positioning.
The wireless signal may cause degradation of communication link quality in an NLOS (None-Line-of-signal, NLOS) propagation environment, and generally, the signal may become more unstable after being propagated through the NLOS, and its fluctuation and jump may be aggravated, for example, in time-based or energy-based angle estimation or distance measurement, NLOS propagation may cause significant deviation based on time and power, or generate false angle estimation, which has a great influence on measurement and estimation results. Therefore, a great deal of research is being conducted on this phenomenon, since UWB (Ultra Wide Band) signals have a large bandwidth, and exhibit a strong measurement accuracy in a multipath environment, non-line-of-sight identification is performed by extracting parameters such as kurtosis, average excess delay, root mean square delay spread, and the like in a multipath channel and analyzing probability density distribution of each parameter in the channel environment, but the cost of the UWB system is too high. In a multipath environment, the RSSI can also show a statistical rule, obey rice distribution under the LOS condition, and obey rayleigh distribution when the LOS path does not exist, but the RSSI is easily influenced by the multipath environment, particularly, in a dynamic environment, the non-line-of-sight identification precision is reduced due to severe change of a Channel, and due to the fine-grained characteristic of Channel State Information (CSI), the RSSI is not easily interfered by the multipath environment, and an Intel5300 wireless network card can more conveniently acquire an accurate CSI signal, and a threshold value is calculated by researching the statistical characteristic of the CSI, and then the threshold value is classified through binary hypothesis testing, so that the method is the most widely applied NLOS identification method at present; however, the hard decision method of the binary assumption requires a predefined recognition threshold. The threshold is easily affected by environment and equipment, so that it is difficult to obtain a high-precision and widely applicable threshold, and the threshold needs to be recalibrated when a scene is changed. However, the soft decision method is used, and the recognition accuracy can be increased by combining a plurality of characteristics, and the method is suitable for various scenes.
Disclosure of Invention
The invention aims to provide a line-of-sight/non-line-of-sight path identification method based on soft decision, which solves the problem of identifying the line-of-sight/non-line-of-sight path in a complex indoor environment, improves positioning accuracy and achieves the aim of high-accuracy position service quality.
In order to achieve the purpose, the invention adopts the technical scheme that: a soft decision visual and non-visual channel based identification method comprises the following steps:
s1, acquiring data packet information and calculating channel characteristics;
s2, calculating the probability distribution function of the channel characteristics obtained in S1;
s3, fitting the probability distribution function of S2 by a plurality of Gaussian functions;
and S4, judging whether the data packet information belongs to the received data packet in the line-of-sight environment or the non-line-of-sight environment by adopting soft decision.
The data packet information acquisition and channel feature calculation method specifically comprises the following steps:
acquiring data packet channel information and processing the synthesized channel state information;
performing inverse Fourier transform on each channel state information data to obtain a corresponding channel impact response;
and calculating the kurtosis and skewness of the channel impulse response amplitude to obtain the channel characteristics.
Kurtosis was calculated using the following formula:
Figure BDA0003050167230000031
skewness was calculated using the following formula:
Figure BDA0003050167230000032
where E {. cndot } represents the sample expected delay, μhAnd σhMean and standard deviation of | h (τ) | representing CIR amplitude, respectively.
In S2, the features without correlation among the features in S1 are selected, and the probability distribution function is calculated.
The gaussian function described in S3 is as follows:
Figure BDA0003050167230000033
wherein x is the measured sample data, f (x) is the density probability of the sample point, ai，bi，ciIs a set of coefficients fitting a gaussian function, a is the height of the curve peak, b is the center coordinate of the peak, c is the standard deviation;
probability distributions of features in line-of-sight/non-line-of-sight scenes are fitted with a plurality of gaussian functions, respectively.
In S4, the probability of the corresponding feature of the data packet in the line-of-sight/non-line-of-sight scene is calculated, specifically as follows:
Figure BDA0003050167230000034
Figure BDA0003050167230000035
wherein y is1，y2，ynRespectively representing the channel characteristics without correlation, P (y)1|x＝LOS)，P(y1| x ═ NLOS), each representing a signal at y1Probability under characteristic line-of-sight and non-line-of-sight conditions, P (y)2|x＝LOS),P(y2| x ═ NLOS) represent the signals y, respectively2Probability of a feature under line-of-sight and non-line-of-sight conditions, P (y)n|x＝LOS),P(yn| x ═ NLOS) represent the signals y, respectivelynThe probability of a feature under line-of-sight and non-line-of-sight conditions, and P (x ═ LOS) and P (x ═ NLOS) represent prior probabilities under line-of-sight and non-line-of-sight conditions.
In S4, the specific determination formula is:
line-of-sight signals: p (x ═ LOS | y)1,y2,...yn)≥1-P(x＝LOS|y1,y2,...yn)
Non-line-of-sight signal: p (x ═ LOS | y)1,y2,...yn)＜1-P(x＝LOS|y1,y2,...yn)。
The invention relates to a recognition device based on soft-decision visual and non-visual channels, which comprises an information receiving device, a processor and a memory, wherein the information receiving device and the memory are connected with the processor through an I/O interface, the memory is used for storing computer executable programs, the processor reads part or all of the computer executable programs from the memory and executes the computer executable programs, the processor can realize the visual range and non-visual range recognition method based on a channel parameter extraction method when executing part or all of the computer executable programs, and the memory is also used for storing information data acquired by the information receiving device.
A computer-readable storage medium, in which a computer program is stored, which, when being executed by a processor, implements the line-of-sight and non-line-of-sight identification method based on the channel parameter extraction method according to the present invention.
A recognition system based on soft decision visual and non-visual channels comprises a channel characteristic calculation module, a probability distribution calculation module and a judgment module;
the channel characteristic calculation module is used for acquiring data packet information and calculating channel characteristics;
the probability distribution calculation module is used for calculating a probability distribution function of the channel characteristics and fitting the probability distribution function by adopting a plurality of Gaussian functions;
the decision module is used for deciding whether the data packet information belongs to the line-of-sight environment or is received under the non-line-of-sight environment based on the soft decision.
Compared with the prior art, the invention has at least the following beneficial effects: the invention combines two irrelevant characteristics to carry out soft decision to identify the sight distance/non-sight distance, and compared with a single characteristic, the accuracy of identification is improved, the single characteristic needs to train a signal to find a proper threshold value, while the invention does not need to carry out excessive training on data, only needs to use the probability distribution of a training sample and uses a soft decision formula to calculate and identify the type of a data packet, thereby greatly shortening the operation time and improving the identification efficiency. The method does not need to calculate the threshold value through multiple times of training, can obtain higher identification accuracy rate only by less prior knowledge, and has higher accuracy rate than the accuracy rate of identifying the sight distance/non-sight distance by two irrelevant single characteristics.
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FIG. 1 is a probability distribution function for calculating kurtosis for line-of-sight and non-line-of-sight scenes using the present invention on a signal and a function fitted with multiple gaussians.
FIG. 2 is a probability distribution function for calculating skewness in line-of-sight and non-line-of-sight scenes using the present invention for signals and the function after fitting with multiple gaussians.
FIG. 3 is a flow chart of a method that may be implemented in accordance with the present invention.
Detailed Description
The invention is described in detail below with reference to the drawings.
Referring to fig. 3, a method for identifying visual and non-visual channels based on soft decision includes the following steps:
s1, acquiring data packet channel information and processing the synthesized channel state information;
s2, carrying out Fourier inversion on each channel state information data obtained in the S1 to obtain corresponding channel impact response;
s3, calculating the kurtosis and skewness of the channel impact response amplitude;
s4, calculating S3 to obtain probability distribution functions of kurtosis and skewness;
s5, fitting the probability distribution function of S4 by a plurality of Gaussian functions;
and S6, judging whether the data packet belongs to the line-of-sight environment or is received in the non-line-of-sight environment according to a soft decision formula.
The specific implementation process of S1 includes:
the amplitude and phase of each subcarrier in the S11 channel transfer function may be expressed as;
Figure BDA0003050167230000051
wherein, H (f)k) Representing carrier centre frequency fkCSI information of, | H (f)k) I and H (f)k) Each represents H (f)k) Amplitude and phase information of.
Fourier inversion is carried out on the CSI information obtained in the S1, and channel impact response is obtained;
Figure BDA0003050167230000061
wherein, ai,θi,τiRespectively representing the amplitude, phase and channel delay of the ith path, N refers to the number of all paths, and delta (tau) is a Dirac delta function.
Kurtosis was calculated using the following formula:
Figure BDA0003050167230000062
skewness was calculated using the following formula:
Figure BDA0003050167230000063
where E {. cndot } represents the sample expected delay, μ|h|And σ|h|Mean and standard deviation of | h (τ) | representing CIR amplitude, respectively.
The probability distribution function of S4 is fitted with a plurality of gaussian functions in S5:
the gaussian function is:
Figure BDA0003050167230000064
x is the sample data obtained by measurement, and f (x) is the density probability of the sample point. a isi，bi，ciIs a set of coefficients fitting a gaussian function, a is the height of the curve peak, b is the center coordinate of the peak, and c is the standard deviation.
Respectively fitting kurtosis probability distribution and skewness probability distribution under LOS/NLOS scene by using a plurality of Gaussian functions
In S6, the soft decision formula is:
Figure BDA0003050167230000065
Figure BDA0003050167230000071
wherein y is1，y2，ynRespectively representing the channel characteristics without correlation, P (y)1|x＝LOS)，P(y1| x ═ NLOS), each representing a signal at y1Probability under characteristic line-of-sight and non-line-of-sight conditions, P (y)2|x＝LOS),P(y2| x ═ NLOS) represent the signals y, respectively2Probability of a feature under line-of-sight and non-line-of-sight conditions, P (y)n|x＝LOS),P(yn| x ═ NLOS) represent the signals y, respectivelynThe probability of a feature under line-of-sight and non-line-of-sight conditions, and P (x ═ LOS) and P (x ═ NLOS) represent prior probabilities under line-of-sight and non-line-of-sight conditions.
The invention provides a soft-decision-based visual and non-visual channel identification system, which comprises a channel characteristic calculation module, a probability distribution calculation module and a judgment module, wherein the channel characteristic calculation module is used for calculating the probability distribution of a channel characteristic;
the channel characteristic calculation module is used for acquiring data packet information and calculating channel characteristics;
the probability distribution calculation module is used for calculating a probability distribution function of the channel characteristics and fitting the probability distribution function by adopting a plurality of Gaussian functions;
the decision module is used for deciding whether the data packet information belongs to the line-of-sight environment or is received under the non-line-of-sight environment based on the soft decision.
Example, results referring to figures 1 and 2,
(1) obtaining a data packet
A2.4 GHz-5.4 GHz channel measurement platform is built in an 8 x 4m conference room for channel measurement, firstly, 5 anchor nodes are arranged in the conference room to serve as transmitting antenna positions, and target nodes are arranged in the conference room at intervals of 0.05m to serve as receiving antenna positions. Selecting 3 anchor nodes as a line of sight (LOS) scene, using the remaining 2 anchor nodes as a non line of sight (NLOS) scene, collecting CSI information by using a notebook and a wireless router supporting the IEEE 802.11n standard, storing received data packets, wherein each CSI information represents the frequency, the amplitude and the phase of each subcarrier and represents signal state information of a transmission signal from a transmitting end to a receiving end,
Figure BDA0003050167230000072
wherein, H (f)k) Representing carrier centre frequency fkCSI information of, | H (f)k) I and H (f)k) Each represents H (f)k) Amplitude and phase information of;
(2) and (2) carrying out IFT change on the data of each CSI information acquired in the step (1) to obtain a Channel Impulse Response (CIR) corresponding to the IFT change:
Figure BDA0003050167230000081
wherein, ai,θi,τiRespectively representing the amplitude, phase and time delay of the ith path, N refers to the number of all paths, and delta (tau) is a Dirac delta function.
(3) Calculating the kurtosis of the channel impulse response amplitude:
Figure BDA0003050167230000082
calculating skewness:
Figure BDA0003050167230000083
where E {. cndot } represents the sample expected delay, μ|h|And σ|h|Mean and standard deviation of | h (τ) | representing CIR amplitude, respectively.
(4) Calculating S4 to obtain probability distribution functions of kurtosis and skewness;
(5) fitting the probability distribution function of S5 with a plurality of gaussian functions;
the gaussian function is as follows:
Figure BDA0003050167230000084
x is the sample data obtained by measurement, f (x) is the density probability of the sample point, ai，bi，ciIs a set of coefficients fitting a gaussian function, a is the height of the curve peak, b is the center coordinate of the peak, and c is the standard deviation.
(6) Finding out the probability distribution corresponding to the step (5) according to the kurtosis and skewness obtained in the step (4), and calculating
Figure BDA0003050167230000085
Figure BDA0003050167230000086
The specific judgment formula is as follows:
line-of-sight signals: p (x ═ LOS | y)1,y2)≥1-P(x＝LOS|y1,y2)
Non-line-of-sight signal: p (x ═ LOS | y)1,y2)＜1-P(x＝LOS|y1,y2)
The following results were obtained by experimental simulation training.
Feature(s) Rate of identification accuracy
Deflection degree 89％
Kurtosis 88％
Soft decision 91％
Compared with a single-feature soft decision method, the method improves the accuracy and greatly shortens the running time.
The invention also provides a recognition device based on soft-decision visual and non-visual channels, which comprises an information receiving device, a processor and a memory, wherein the information receiving device and the memory are both connected with the processor through an I/O interface, the memory is used for storing computer executable programs, the processor reads part or all of the computer executable programs from the memory and executes the computer executable programs, the recognition method based on the soft-decision visual and non-visual channels can be realized when the processor executes part or all of the computer executable programs, and the memory is also used for storing information data acquired by the information receiving device.
Wireless router supporting IEEE 802.11n standard
Further, when executing S1, the apparatus may acquire the packet channel information through the information receiving apparatus, and process the packet channel information in real time by the processor to obtain the channel transfer function; and may store the channel transfer function to memory.
The identification device based on the soft-decision visible and non-visible channels can adopt a notebook computer, a tablet computer, a desktop computer, a mobile phone or a workstation.
Alternatively, the processor according to the present invention may be a Central Processing Unit (CPU), a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf programmable gate array (FPGA), or a Programmable Logic Device (PLD).
The memory of the invention can be an internal storage unit of a notebook computer, a tablet computer, a desktop computer, a mobile phone or a workstation, such as a memory and a hard disk; an external memory unit, such as a removable hard disk or a flash memory card, may also be used.
Computer-readable storage media may include computer storage media and communication media. Computer storage media includes volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data. The computer-readable storage medium may include: a Read Only Memory (ROM), a Random Access Memory (RAM), a Solid State Drive (SSD), or an optical disc. The Random Access Memory may include a resistive Random Access Memory (ReRAM) and a Dynamic Random Access Memory (DRAM).

Claims (10)

1. A soft decision based visual and non-visual channel identification method is characterized by comprising the following steps:
s1, acquiring data packet information and calculating channel characteristics;
s2, calculating the probability distribution function of the channel characteristics obtained in S1;
s3, fitting the probability distribution function of S2 by a plurality of Gaussian functions;
and S4, judging whether the data packet information belongs to the received data packet in the line-of-sight environment or the non-line-of-sight environment by adopting soft decision.
2. The method for identifying visual and non-visual channels based on soft decision as claimed in claim 1, wherein the obtaining of the packet information to calculate the channel characteristics is as follows:
acquiring data packet channel information and processing the synthesized channel state information;
performing inverse Fourier transform on each channel state information data to obtain a corresponding channel impact response;
and calculating the kurtosis and skewness of the channel impulse response amplitude to obtain the channel characteristics.
3. The soft decision visual and non-visual channel based identification method of claim 2,
kurtosis was calculated using the following formula:
Figure FDA0003050167220000011
skewness was calculated using the following formula:
Figure FDA0003050167220000012
where E {. cndot } represents the sample expected delay, μhAnd σhMean and standard deviation of | h (τ) | representing CIR amplitude, respectively.
4. The method as claimed in claim 1, wherein the non-correlated ones of the features of S1 are selected in S2, and the probability distribution function is calculated.
5. The soft-decision based visual and non-visual channel identification method of claim 1, wherein the gaussian function in S3 is as follows:
Figure FDA0003050167220000013
wherein x is the measured sample data, f (x) is the density probability of the sample point, ai，bi，ciIs a set of coefficients fitting a gaussian function, a is the height of the curve peak, b is the center coordinate of the peak, c is the standard deviation;
probability distributions of features in line-of-sight/non-line-of-sight scenes are fitted with a plurality of gaussian functions, respectively.
6. The method for identifying visual and non-visual channels based on soft decision as claimed in claim 1, wherein in S4, the probability of the corresponding characteristic of the data packet in the line-of-sight/non-line-of-sight scenario is calculated as follows:
Figure FDA0003050167220000021
Figure FDA0003050167220000022
wherein y is1，y2，ynRespectively representing the channel characteristics without correlation, P (y)1|x＝LOS)，P(y1| x ═ NLOS), each representing a signal at y1Probability under characteristic line-of-sight and non-line-of-sight conditions, P (y)2|x＝LOS),P(y2| x ═ NLOS) represent the signals y, respectively2Probability of a feature under line-of-sight and non-line-of-sight conditions, P (y)n|x＝LOS),P(yn|x＝NLOS)Respectively represent a signal ynThe probability of a feature under line-of-sight and non-line-of-sight conditions, and P (x ═ LOS) and P (x ═ NLOS) represent prior probabilities under line-of-sight and non-line-of-sight conditions.
7. The method for identifying visual and non-visual channels based on soft decision as claimed in claim 1, wherein in S4, the specific determination formula is:
line-of-sight signals: p (x ═ LOS | y)1,y2,...yn)≥1-P(x＝LOS|y1,...y2,...yn)
Non-line-of-sight signal: p (x ═ LOS | y)1,y2,...yn)＜1-P(x＝LOS|y1,y2,...yn)。
8. An identification device based on soft-decision visual and non-visual channels, characterized by comprising an information receiving device, a processor and a memory, wherein the information receiving device and the memory are connected with the processor through an I/O interface, the memory is used for storing computer executable programs, the processor reads part or all of the computer executable programs from the memory and executes the computer executable programs, when the processor executes part or all of the computer executable programs, the identification device can realize the visual range and non-visual range identification method based on the channel parameter extraction method according to any one of claims 1 to 7, and the memory is also used for storing information data acquired by the information receiving device.
9. A computer-readable storage medium, storing a computer program, which when executed by a processor implements the line-of-sight and non-line-of-sight identification method based on the channel parameter extraction method according to any one of claims 1 to 7.
10. A recognition system based on soft decision visual and non-visual channels is characterized by comprising a channel characteristic calculation module, a probability distribution calculation module and a judgment module;
the channel characteristic calculation module is used for acquiring data packet information and calculating channel characteristics;
the probability distribution calculation module is used for calculating a probability distribution function of the channel characteristics and fitting the probability distribution function by adopting a plurality of Gaussian functions;
the decision module is used for deciding whether the data packet information belongs to the line-of-sight environment or is received under the non-line-of-sight environment based on the soft decision.
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