CN108960051A - A kind of adaptive CSI signal auxiliary filter method based on frequency analysis - Google Patents
A kind of adaptive CSI signal auxiliary filter method based on frequency analysis Download PDFInfo
- Publication number
- CN108960051A CN108960051A CN201810519228.XA CN201810519228A CN108960051A CN 108960051 A CN108960051 A CN 108960051A CN 201810519228 A CN201810519228 A CN 201810519228A CN 108960051 A CN108960051 A CN 108960051A
- Authority
- CN
- China
- Prior art keywords
- frequency
- fcn
- max
- denoted
- csi
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2218/00—Aspects of pattern recognition specially adapted for signal processing
- G06F2218/02—Preprocessing
- G06F2218/04—Denoising
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Artificial Intelligence (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Signal Processing (AREA)
- Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Measuring And Recording Apparatus For Diagnosis (AREA)
Abstract
The adaptive CSI signal auxiliary filter method based on frequency analysis that the invention discloses a kind of, it is characterized in that carrying out as follows: 1 acquires one section of environmental noise data before acquiring the required CSI data comprising action message；2 acquisitions include the CSI data of action message, carry out frequency-domain analysis comparison to two segment datas；3 determine the cut-off frequecy of passband of low-pass filter according to frequency differences and stopband cutoff frequency and are filtered to signal.Present invention reduces the processing times filtered to CSI signal, provide support to build the real-time system of detection human body behavioural information.
Description
Technical field
The invention belongs to field of signal processing, and specifically a kind of adaptive CSI signal based on frequency analysis assists
Filtering method.
Background technique
In recent years, with the development of computer technology, calculating mode centered on machine is just towards being human-centred
Calculating Mode change.It allows people to become a part for calculating link, promotes the fusion of physical world and information world, realize high-rise
Secondary human-computer interaction is following developing direction.Accurate perception and understanding to human body behavior are then essential technical supports.
Human body behavioural information is obtained using WiFi signal to be had non line of sight, passively perceives (without carry sensors), is at low cost, easy portion
Administration is not illuminated by the light a series of advantages such as condition limits, scalability is strong.The major part that human body behavioural information is obtained using WiFi is ground
Study carefully and used CSI (channel state information) to obtain fine-grained human body behavioural information, and CSI is handled must can not
A few step is exactly to filter (human body behavioural information concentrates on low-frequency range substantially), guarantees to obtain a standard to eliminate noise jamming
True final result.Human body information is obtained using WiFi can apply and monitoring breath state, detection sleep state and detection
The medical domains such as tumble, largely require building short processing time, the high real-time system of accuracy rate in these work.
Summary of the invention
The adaptive CSI signal auxiliary filter method based on frequency analysis that the present invention provides a kind of passes through one section of acquisition
Ambient noise signal accurately determines the passband and stopband of low-pass filter come the signal progress frequency analysis filtered with needs,
It filters out the noise of the overwhelming majority to realize with lower time complexity to reach and retains the function of useful information, save letter
Number the processing time, for build detection human action information real-time system such as respiratory monitoring system etc. support is provided.
Purpose according to the present invention provides a kind of adaptive CSI signal auxiliary filter method based on frequency analysis, special
Sign is that described method includes following steps:
Step 1: one section of static environment ambient noise CSI data of acquisition；
Step 2: obtaining the CSI data containing useful action message；
Step 3: carrying out frequency-domain analysis comparison to two segment datas；
Step 4: determining the cut-off frequecy of passband and stopband cutoff frequency and to signal of low-pass filter according to frequency difference
It is filtered.
Preferably, one section of static environment ambient noise CSI data of the acquisition, specifically, including action message obtaining
Data before acquire one section of static environment ambient noise CSI data, be denoted as CSIN, with resolution ratio f analysis CSIN in f_min and
Frequency content between f_max, f are set according to required filtering fineness and time complexity, are usually arranged as 1hz, f_
Min and f_max are empirically determined, if approximate range locating for the signal frequency of known demand, f_min can be arranged with reference to it
And f_max, otherwise the range of f_min and f_max is set greatly as far as possible, and frequency-domain analysis result is denoted as FN1, FN2,
FN3 ..., FNn-1, FNn } (n=(f_max-f_min)/f).
Preferably, the CSI data of the acquisition containing useful action message, specifically,
The CSI data containing useful action message are obtained, CSIU is denoted as, are existed with resolution ratio f identical with step 1 analysis CSIN
Frequency content between f_min and f_max, analyzes its frequency content, and frequency-domain analysis result is denoted as FU1, FU2,
FU3 ..., FUn-1, FUn } (n=(f_max-f_min)/f).
Preferably, described that frequency-domain analysis comparison is carried out to two segment datas, specifically, by { FN1, FN2, FN3 ... ..., FNn-
1, FNn } subtract each other with { FU1, FU2, FU3 ... ..., FUn-1, FUn } step-by-step, be as a result denoted as FC1, FC2, FC3 ... ..., FCn-1,
FCn } (n=(f_max-f_min)/f)；Calculate { FC1, FC2, FC3 ..., FCn-1, FCn } absolute value | FC1 |, | FC2
|, | FC3 | ... ..., | FCn-1 |, | FCn | }, find amplitude bust point k backward since its array section start, i.e., | FCk | < * |
FCk-1 |, and p* | FCk-1 | > | FCh | > p* | FCk | (p value 0-0.6, h=k+1, k+2 ... ..., k+m), by f_min+f*k
It is denoted as fs；To { FC1, FC2, FC3 ... ..., FCn-1, FCn }, the peak-to-peak value of m Frequency point after k point is calculated, is denoted as
Va searches out a sign-inverted point z from k point backward, the peak-to-peak value of judgement m Frequency point thereafter whether be less than n times of va with
On, otherwise n value 0-1 continually looks for next sign-inverted point and continues above-mentioned steps if f_min+f*z is denoted as fp.
Beneficial effects of the present invention:
1, the present invention can adaptively be that need to handle signal accurately to determine that filter passband cutoff frequency and stopband are cut
Only frequency is filtered, empirically logical to estimate filter without assessing a large amount of signal datas
Band and stopband.
2, according to correlative study, as long as filter passband and stopband has accurately been determined, so that it may filter out the overwhelming majority
Noise.The present invention can accurately determine filter passband and stopband to be filtered to coherent signal, without for another example
Outlier, DWT, weighting are such as gone in the same processing for carrying out a series of complex again after by low-pass filtering of filters solutions before
It is average etc. to filter out noise, signal processing time is reduced, to create short processing time, the high real-time system of accuracy rate is provided
It supports.
Detailed description of the invention
Fig. 1 is overhaul flow chart of the invention
Specific embodiment
The application is described in further detail with reference to the accompanying drawing, it is necessary to it is indicated herein to be, implement in detail below
Mode is served only for that the application is further detailed, and should not be understood as the limitation to the application protection scope, the field
Technical staff can make some nonessential modifications and adaptations to the application according to above-mentioned application content.
Purpose according to the present invention, as shown in Figure 1, providing a kind of adaptive CSI signal auxiliary filter based on frequency analysis
Wave method includes the following steps:
Step 1: one section of static environment ambient noise CSI data of acquisition；
One section of static environment ambient noise CSI data, the present embodiment are acquired before obtaining the data comprising action message
In, one section of duration, 1.5 seconds ambient noise signals are first obtained before carrying out keystroke movement, are denoted as CSIN, with resolution ratio f points
CSIN frequency content between f_min and f_max is analysed, f is set according to required filtering fineness and time complexity, is led to
It is standing to be set to 1hz, f_min and f_max and empirically determine, it, can be with if approximate range locating for the signal frequency of known demand
F_min and f_max is set with reference to it, is otherwise set as far as possible to guarantee not lose useful information for the range of f_min and f_max
Greatly, and by frequency-domain analysis result it is denoted as { FN1, FN2, FN3 ... ..., FNn-1, FNn } (n=(f_max-f_min)/f).
Step 2: obtaining the CSI data containing useful action message；
The CSI data containing useful action message are obtained, in embodiment, for the keystroke for several times that is done comprising a bit test person
The CSI signal of action message, is denoted as CSIU, with the identical resolution ratio f analysis CSIN frequency between f_min and f_max of step 1
Rate ingredient analyzes its frequency content, and frequency-domain analysis result is denoted as { FU1, FU2, FU3 ... ..., FUn-1, FUn } (n=(f_
max-f_min)/f)。
Step 3: carrying out frequency-domain analysis comparison to two segment datas；
{ FN1, FN2, FN3 ... ..., FNn-1, FNn } and { FU1, FU2, FU3 ... ..., FUn-1, FUn } step-by-step are subtracted each other,
As a result be denoted as FC1, FC2, FC3 ..., FCn-1, FCn } (n=(f_max-f_min)/f)；Calculate FC1, FC2,
FC3 ... ..., FCn-1, FCn } absolute value | FC1 |, | FC2 |, | FC3 | ... ..., | FCn-1 |, | FCn | }, from its array
Start to find amplitude bust point k backward at beginning, i.e., | FCk |<* | FCk-1 |, and p* | FCk-1 |>| FCh |>p* | FCk | (p value
0-0.6, h=k+1, k+2 ... ..., k+m), f_min+f*k is denoted as fs；To { FC1, FC2, FC3 ... ..., FCn-1, FCn },
Calculate k point after m Frequency point peak-to-peak value, be denoted as va, search out a sign-inverted point z backward from k point, this be because
For after first signal rollback point, the difference of FN and FU will tend to be tranquil, judge that the peak-to-peak value of m Frequency point thereafter is
F_min+f*z is denoted as by no n times or more less than va, n value 0-1 if then illustrating that the difference of FN and FU are smaller
Otherwise fp continually looks for next sign-inverted point and continues above-mentioned steps.
Step 4: determining the cut-off frequecy of passband and stopband cutoff frequency and to signal of low-pass filter according to frequency difference
It is filtered.
In the present embodiment, by fs, cut-off frequecy of passband and stopband cutoff frequency pair of the fp respectively as low-pass filter
CSIU is filtered.We carry out the frequency content of ambient noise signal and the frequency content of the signal containing action message
Comparison, their certain frequency contents can differ greatly, this is because caused by the movement of human body.That is these frequencies
Partial signal contains the human action information of the overwhelming majority, this be we under study for action required for, so we are by fs
As the cut-off frequecy of passband of filter.And high frequency section later belongs to noise section, is the part that we need to filter out,
So we filter out noise signal and frequency similar portion containing useful information signal, i.e., fp is set as filter stop bend and cut
Only frequency.
The embodiments described above only express several embodiments of the present invention, and the description thereof is more specific and detailed, but simultaneously
Limitations on the scope of the patent of the present invention therefore cannot be interpreted as.It should be pointed out that for those of ordinary skill in the art
For, without departing from the inventive concept of the premise, various modifications and improvements can be made, these belong to guarantor of the invention
Protect range.Therefore, the scope of protection of the patent of the invention shall be subject to the appended claims.
Claims (4)
1. a kind of adaptive CSI signal auxiliary filter method based on frequency analysis, which is characterized in that the method includes as follows
Step:
Step 1: one section of static environment ambient noise CSI data of acquisition；
Step 2: obtaining the CSI data containing useful action message；
Step 3: carrying out frequency-domain analysis comparison to two segment datas；
Step 4: according to frequency difference determine low-pass filter cut-off frequecy of passband and stopband cutoff frequency and to signal carry out
Filtering.
2. the adaptive CSI signal auxiliary filter method according to claim 1 based on frequency analysis, which is characterized in that
One section of static environment ambient noise CSI data of the acquisition, specifically, acquiring one before obtaining the data comprising action message
Section static environment ambient noise CSI data, be denoted as CSIN, with resolution ratio f analysis CSIN between f_min and f_max frequency at
Point, f is set according to required filtering fineness and time complexity, is usually arranged as 1hz, f_min and f_max according to warp
Determination is tested, if approximate range locating for the signal frequency of known demand, f_min and f_max can be set with reference to it, otherwise by f_
The range of min and f_max is set greatly as far as possible, and frequency-domain analysis result is denoted as { FN1, FN2, FN3 ... ..., FNn-1, FNn } (n=
(f_max-f_min)/f)。
3. the adaptive CSI signal auxiliary filter method according to claim 1 based on frequency analysis, which is characterized in that
The CSI data of the acquisition containing useful action message, specifically,
The CSI data containing useful action message are obtained, CSIU is denoted as, with resolution ratio f identical with step 1 analysis CSIN in f_
Frequency content between min and f_max, analyzes its frequency content, and frequency-domain analysis result is denoted as FU1, FU2, FU3 ... ...,
FUn-1, FUn } (n=(f_max-f_min)/f).
4. the adaptive CSI signal auxiliary filter method according to claim 2 or 3 based on frequency analysis, feature exist
In, it is described to two segment datas carry out frequency-domain analysis comparison, specifically, will { FN1, FN2, FN3 ... ..., FNn-1, FNn } and
{ FU1, FU2, FU3 ... ..., FUn-1, FUn } step-by-step is subtracted each other, and { FC1, FC2, FC3 ... ..., FCn-1, FCn } (n=is as a result denoted as
(f_max-f_min)/f)；Calculate { FC1, FC2, FC3 ..., FCn-1, FCn } absolute value | FC1 |, | FC2 |, | FC3
| ... ..., | FCn-1 |, | FCn |, find amplitude bust point k backward since its array section start, i.e., | FCk | < * | FCk-1 |,
And p* | FCk-1 | > | FCh | > p* | FCk | f_min+f*k is denoted as fs by (p value 0-0.6, h=k+1, k+2 ... ..., k+m)；
To { FC1, FC2, FC3 ... ..., FCn-1, FCn }, the peak-to-peak value of m Frequency point after k point is calculated, va is denoted as, from k point
A sign-inverted point z is searched out backward, judges whether the peak-to-peak value of m Frequency point thereafter is less than n times of va or more, n value
Otherwise 0-1 continually looks for next sign-inverted point and continues above-mentioned steps if f_min+f*z is denoted as fp.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810519228.XA CN108960051A (en) | 2018-05-28 | 2018-05-28 | A kind of adaptive CSI signal auxiliary filter method based on frequency analysis |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810519228.XA CN108960051A (en) | 2018-05-28 | 2018-05-28 | A kind of adaptive CSI signal auxiliary filter method based on frequency analysis |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN108960051A true CN108960051A (en) | 2018-12-07 |
Family
ID=64492171
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201810519228.XA Pending CN108960051A (en) | 2018-05-28 | 2018-05-28 | A kind of adaptive CSI signal auxiliary filter method based on frequency analysis |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN108960051A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110737201A (en) * | 2019-10-11 | 2020-01-31 | 珠海格力电器股份有限公司 | monitoring method, device, storage medium and air conditioner |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2005227248A (en) * | 2004-02-10 | 2005-08-25 | High Speed Signal Processing Laboratory Inc | External type signal analyzer |
| CN101800845A (en) * | 2010-01-06 | 2010-08-11 | 北京中科大洋科技发展股份有限公司 | Method for designing adjustable frequency domain filter based on smooth curve |
| CN102158197A (en) * | 2010-02-11 | 2011-08-17 | 北京国能子金电气技术有限公司 | Self-adaptive low-pass digital filter of variable frequency signals and method thereof |
| CN105897636A (en) * | 2016-05-19 | 2016-08-24 | 电子科技大学 | CPFSK (Continuous-Phase Frequency Shift Keying) signal modulation index estimation method based on Lyapunov exponent |
| CN107749143A (en) * | 2017-10-30 | 2018-03-02 | 安徽工业大学 | A kind of indoor occupant fall detection system and method through walls based on WiFi signal |
-
2018
- 2018-05-28 CN CN201810519228.XA patent/CN108960051A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2005227248A (en) * | 2004-02-10 | 2005-08-25 | High Speed Signal Processing Laboratory Inc | External type signal analyzer |
| CN101800845A (en) * | 2010-01-06 | 2010-08-11 | 北京中科大洋科技发展股份有限公司 | Method for designing adjustable frequency domain filter based on smooth curve |
| CN102158197A (en) * | 2010-02-11 | 2011-08-17 | 北京国能子金电气技术有限公司 | Self-adaptive low-pass digital filter of variable frequency signals and method thereof |
| CN105897636A (en) * | 2016-05-19 | 2016-08-24 | 电子科技大学 | CPFSK (Continuous-Phase Frequency Shift Keying) signal modulation index estimation method based on Lyapunov exponent |
| CN107749143A (en) * | 2017-10-30 | 2018-03-02 | 安徽工业大学 | A kind of indoor occupant fall detection system and method through walls based on WiFi signal |
Non-Patent Citations (2)
| Title |
|---|
| PALASH PAL: "Filter design for harmonics minimization of CSI based contact less induction heater", 《2015 INTERNATIONAL CONFERENCE ON ENERGY, POWER AND ENVIRONMENT: TOWARDS SUSTAINABLE GROWTH (ICEPE)》 * |
| 王召利: "基于自适应滤波的瞬时频率测量新方法", 《制导与引信》 * |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110737201A (en) * | 2019-10-11 | 2020-01-31 | 珠海格力电器股份有限公司 | monitoring method, device, storage medium and air conditioner |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108926333B (en) | System and method for heart rate estimation | |
| CN103584854B (en) | Extraction method of electrocardiosignal R waves | |
| CN107228282B (en) | A kind of gas pipeline leakage localization method and device | |
| CN107272651B (en) | A kind of Robust Detection Method of Braking System for Multiple Units interval multiplying property sensor fault | |
| WO2006058135A3 (en) | Systems and methods relating to enhanced peripheral fields motion detection | |
| CN105737909A (en) | Anti-instantaneous-impact-interference signal processing method and system for vortex shedding flowmeter based on Kalman filtering | |
| CN109009052A (en) | The embedded heart rate measurement system and its measurement method of view-based access control model | |
| CN103932693A (en) | Method for measuring human body heart rate on basis of mobile phone image | |
| CN105588577A (en) | Detection method and detection apparatus for abnormal step counting in exercise monitoring device | |
| CN104095627B (en) | A kind of electrocardiogram digitized signal quality soft decision method and device | |
| CN109101902A (en) | A kind of satellite NLOS signal detecting method based on unsupervised learning | |
| CN110084987A (en) | A kind of foreign matter inspecting system and method towards rail traffic | |
| Wang et al. | Weak fault diagnosis of rolling bearing under variable speed condition using IEWT-based enhanced envelope order spectrum | |
| CN108960051A (en) | A kind of adaptive CSI signal auxiliary filter method based on frequency analysis | |
| CN107784640B (en) | Ear-drum state real-time analysis method | |
| JP2009103672A (en) | Analysis method for discriminating between earthquake and vibration caused by noise | |
| CN203191567U (en) | Human body security check equipment | |
| CN103549947A (en) | Real-time and accurate electrocardiographic wave drawing method of smartphone platforms | |
| CN110786863B (en) | Pedestrian gait detection method based on mobile device | |
| CN106214143B (en) | Transient motion interference identification method and heart rate confidence calculations method | |
| CN108507782A (en) | The detection method of periodic signal implicit cycle under a kind of strong background noise | |
| CN112345247B (en) | Fault diagnosis method and device for rolling bearing | |
| CN107271184B (en) | A kind of the kernel regression decomposition method and system of rolling bearing fault diagnosis | |
| Fedotov | A robust method for detecting the QRS complex of the ECG signal | |
| CN108074228A (en) | Ear-drum state real-time analyzer |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| WD01 | Invention patent application deemed withdrawn after publication |
Application publication date: 20181207 |
|
| WD01 | Invention patent application deemed withdrawn after publication |