CN106658703A - Cosine similarity based RSS (Received Signal Strength) detection difference compensation method - Google Patents

Cosine similarity based RSS (Received Signal Strength) detection difference compensation method Download PDF

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Publication number
CN106658703A
CN106658703A CN201610890601.3A CN201610890601A CN106658703A CN 106658703 A CN106658703 A CN 106658703A CN 201610890601 A CN201610890601 A CN 201610890601A CN 106658703 A CN106658703 A CN 106658703A
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rss
signal strength
reference point
cosine similarity
received signal
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CN106658703B (en
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王磊
周亮
李中雷
陈鸣楷
柳思然
周慧
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Nanjing Post and Telecommunication University
Nanjing University of Posts and Telecommunications
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Nanjing Post and Telecommunication University
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W64/00—Locating users or terminals or network equipment for network management purposes, e.g. mobility management
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S11/00—Systems for determining distance or velocity not using reflection or reradiation
    • G01S11/02—Systems for determining distance or velocity not using reflection or reradiation using radio waves
    • G01S11/06—Systems for determining distance or velocity not using reflection or reradiation using radio waves using intensity measurements
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S5/00—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations
    • G01S5/02—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations using radio waves
    • G01S5/0252—Radio frequency fingerprinting
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W24/00—Supervisory, monitoring or testing arrangements
    • H04W24/02—Arrangements for optimising operational condition

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  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Radar, Positioning & Navigation (AREA)
  • Remote Sensing (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
  • Position Fixing By Use Of Radio Waves (AREA)
  • Collating Specific Patterns (AREA)

Abstract

The invention discloses a cosine similarity based RSS (Received Signal Strength) detection difference compensation method. According to the method, the cosine similarity is introduced to act as a measurement criterion for judging whether RSS indoor positioning accuracy difference compensation can be performed or not between different equipment, and the difference of RSS detection positioning accuracy is compensated through a method of ratio correction for an equipment group meeting the measurement criterion, so that a problem of reduction in accuracy and stability brought about by the difference in RSS detection capabilities of different equipment is effectively solved. In addition, the effectiveness and the reliability of the method are verified by a system through Android platform software design. Experimental results show that the method disclosed by the invention effectively improves the positioning accuracy and the positioning stability.

Description

A kind of RSS based on cosine similarity detects disparity compensation method
Technical field
The present invention relates to the indoor positioning technologies in wireless communication networks field, more particularly to a kind of connecing based on cosine similarity Receive the different compensation method of signal strength signal intensity RSS error of measurement.
Background technology
In recent years, with the development and the extensive application of general fit calculation technology of wireless network, many public and business takes It is engaged in, including searching rescue, fire-disaster emergency and location Based service progressively increase the location information requirements of mobile subscriber.Base It is highly developed in the outdoor positioning system of satellite-signal, but it is fixed also to lack interior that is more ripe, being widely adopted at present Position system.Therefore, the research of indoor positioning technologies has highly important practice significance.With Wi-Fi technology and mobile device Wireless network receiver is developed rapidly, and wireless network covers the natural navigation light become for alignment system.Therefore, pass through Depth is excavated and important ground realizing that indoor positioning has become one using the characteristics of wireless network generality and Wi-Fi signal Study carefully field.
In existing Wi-Fi location technologies, using the signal strength signal intensity for receiving (Received Signal Strength are indicated Indicator, RSSI) and there is outstanding advantage based on the indoor positioning algorithms of fingerprint database.And based on a determination that the fingerprint of property Algorithm, such as weighting K closes on method (Weight K-nearest Neighborhood, WKNN), relative to based on probabilistic algorithm Computation complexity is low, and the speed of service is fast, it is easy to accomplish.But for equipment difference problem, the algorithm is applied into different shiftings Dynamic equipment and while using identical location fingerprint database, test result indicate that the positioning precision of different mobile devices and stable Property have very big difference, it can thus be appreciated that impact of the equipment difference to positioning precision and stability is the problem that can not ignore.
Move during mobile device (training equipment) and tuning on-line that fingerprint database is generated in off-line training step The brand and model for employing the mobile device (i.e. location equipment) that family uses is uncontrollable, and many in certain positioning region The tuning on-line equipment of individual different brands and model but shares an off-line training equipment collection life by certain brand and model Into location fingerprint database.It is well known that due to the differences such as Wi-Fi chips, antenna model and packaging material, different brands The mobile device of model of signal receiving strength detectability with to(for) Wi-Fi is different, certainly will so cause finger print data Storehouse declines in different location equipments with the matching accuracy of RSS (received signal strength) value of real-time detection, so that fixed Position precise decreasing, position stability are reduced.For the problems referred to above, effective solution party is not also disclosed in existing technical literature Method.
The content of the invention
The technical problem to be solved is the mobile device of different brands and model because the signal for Wi-Fi The positioning precision that receiving intensity detectability is different and causes declines, the phenomenon that position stability is reduced.
In order to solve above-mentioned technical problem, the present invention proposes that a kind of RSS based on cosine similarity detects disparity compensation side Can method, the method introduce cosine similarity as the module that RSS disparity compensations are carried out between distinct device, to satisfaction The equipment group of amount standard compensates RSS and detects difference by the method for ratiometric correction, efficiently solves due to distinct device RSS detections The positioning precision and stability that capacity variance is brought declines problem.
Method flow：
Step one：Hypothesis has n reference point (Reference Point, RP), m WAP (Aceess Point, AP), off-line training step is carried out first, the location fingerprint database of target positioning region is built, calculate online respectively Location equipment has indicated that the real-time reception signal from all AP (WAP) collected on RP (reference point) is strong at certain The cosine similarity CS of the received signal strength vector set of each RP (reference point) in degree vector set and original fingerprint data storehousei；
Step 2：Select wherein cosine similarity CSiMaximum RP (reference point)；
Step 3：Calculate the received signal strength vector set for having indicated that tuning on-line equipment is collected on RP (reference point) The received signal strength vector set items component of maximum RP (reference point), i.e., (wirelessly connect from each AP with cosine similarity Access point) received signal strength value corresponding ratio ti；
Step 4：Calculate every component correspondence ratio tiMean valueAs original fingerprint data storehouse compensation correction because Son；
Step 5：The received signal strength vector set of all RP (reference point) in original fingerprint data storehouse is all multiplied by into compensation Correction factorGenerate new correction fingerprint database and participate in matching primitives.
Further, the location fingerprint database of the target positioning region in above-mentioned steps one, specifically includes following steps：
1) signal access point is set in the region for needing positioning and numbers, carry out RP (reference point) according to region and divide, often Individual RP (reference point) arranges unique numbering, and each RP (reference point) can receive the signal strength signal intensity from unlike signal access point Indicate；
2) signal strength signal intensity that receives is indicated to be measured several times, calculates its mean value, receive from difference Signal access point signals intensity indicates one signal strength signal intensity vector of composition, as the mark of the RP (reference point),；
3) fingerprint of each RP (reference point) is stored in into database, ultimately generates the fingerprint base in the region.
Cosine similarity CS in above-mentioned steps oneiValue between mobile device between 0.99 to 1, difference is mobile Equipment is consistent for the reflection trend of each AP (WAP) signal strength signal intensity size.
Further, above-mentioned mobile device is unrelated with brand.
Beneficial effect：
1. the present invention replaces Euclidean distance using cosine similarity as direction during module, more prominent positioning Difference in (i.e. variation tendency), while have modified the skimble-scamble problem of module that may be present between user.
2. the present invention is positioned using the fingerprint database after compensation correction, is efficiently solved due to distinct device RSS The positioning precision that detectability difference is brought declines problem, significantly improves positioning precision.
3. present invention introduces can cosine similarity used as the module that RSS disparity compensations are carried out between distinct device, Its positioning mean error is reduced, and position stability is also greatly improved.
4. present invention introduces can cosine similarity used as carrying out RSS indoor position accuracy disparity compensations between distinct device Module, the difference that RSS detects positioning precision is compensated by the method for ratiometric correction to meeting the equipment group of module, Efficiently solve the precision and stability brought due to distinct device RSS detectability differences and decline problem.
Description of the drawings
Fig. 1 is that the RSS of two kinds of different mobile devices changes over contrast difference's figure.
Fig. 2 is that the RSS of three kinds of different mobile devices changes over contrast difference's figure.
Fig. 3 is alignment system correction map.
Fig. 4 is based on the compensation correction strategy basic principle schematic of cosine similarity.
Fig. 5 is that the machine positioning, original fingerprint data storehouse positioning and the correction fingerprint database of three kinds of different mobile devices are determined The contrast histogram of the position error mean value, variance and standard deviation of position.
Fig. 6 is that the machine positioning, original fingerprint data storehouse positioning and the correction fingerprint database of three kinds of different mobile devices are determined The positioning precision cumulative probability profiles versus figure of position.
Specific embodiment
With reference to Figure of description, the present invention is described in further detail.
The alignment system of the compensation correction method based on cosine similarity of the present invention is divided into three phases：Off-line training Stage, on-line correction stage and tuning on-line stage.In off-line training step, using off-line training equipment in area to be targeted Suitable reference point is selected to build original fingerprint data storehouse；In the on-line correction stage, gather in reference point finger print data and finish Positioning region in by wherein one or more reference point locations, with appropriate flag tag, out (particular location and quantity can roots According to the rationally distributed selection of indoor environment), then obtained using tuning on-line equipment and indicated that distance users are nearest in reference point From the received signal strength vector set of each AP (WAP) in reference point, finally using the benefit based on cosine similarity Repay Correction Strategies to compensate and correct original fingerprint data storehouse, generate final correction fingerprint database；The tuning on-line stage In, obtain certain real-time locating point data using identical tuning on-line equipment and carry out matching primitives with correction fingerprint database, obtain Take locating point position information.The characteristic for introducing cosine similarity simultaneously is compared.
The characteristics of cosine similarity of the present invention, includes：
1. for absolute figure is insensitive；
Even if 2. two only little same components values of vector are likely to that very high similarity can be obtained；
3., if each entry value of two vectors is more intended to that correspondence is proportional, regardless of numerical value difference, similarity gets over To in 1.
What cosine similarity was focused on is the variation tendency problem of two vector values, is not specific numerical value, and difference is set Standby to regard different users as, the received signal strength value from multiple identical AP (WAP) that equipment is detected regards use as Each item rating at family, RSS (received signal strength) the detectabilities difference of distinct device can module between analogy different user Skimble-scamble problem.
Experiment and notional result according to more than, provide RSS (received signal strength) the detection differences based on cosine similarity Compensation policy.The present invention is divided into following step：
Step one：Off-line training step is carried out, the location fingerprint database of target positioning region is built.Calculate respectively online Location equipment has indicated that the real-time RSS from all AP (WAP) collected in reference point (receives signal strong at certain Degree) the cosine similarity CS of the received signal strength vector set of each reference point in vector set and original fingerprint data storehousei；
Step 2：Select wherein cosine similarity CSiMaximum reference point；
CSi=max { CS1,CS2,…,CSn}
Step 3：Calculate received signal strength vector set that this has indicated that tuning on-line equipment is collected in reference point and remaining The received signal strength vector set items component of the maximum reference point of string similarity is (from the reception of each AP (WAP) Signal strength values) corresponding ratio ti；
Step 4：Calculate every component correspondence ratio tiMean valueAs original fingerprint data storehouse compensation correction because Son；
Step 5：The RSS vector sets of all reference points in original fingerprint data storehouse are all multiplied by into the compensation correction factorIt is raw The correction fingerprint database of Cheng Xin participates in matching primitives.
The principle that different brands receive Wi-Fi signal with the mobile device of model is consistent, and for RSS (receives letter Number intensity) difference of receiving ability led by device parameter differences such as Wi-Fi chip parameters, the antenna model parameters of distinct device Cause.
Test result indicate that, RSS is changed over and had differences, Android device brand A and certain product of different brands Board B under identical precondition (i.e.：Same time, same place) to same wireless aps (WAP) signal strength signal intensity Testing result is significantly different, but both curves all keep identical variation tendency substantially.
And RSS there is also certain difference with distance change between different brands equipment, it is with certain brand B and certain brand A Example, although the size of the RSS values of the same AP (WAP) that both detect has differences under same distance, but its It is basically identical with distance change trend, and the linear relationship of kept stable, the data difference between certain brand A and certain brand B Different fluctuated near a fixed ratio.
Theoretical based on more than, we can provide the RSS based on cosine similarity and detect disparity compensation strategy.The present invention point For following step：
Step one：Off-line training step is carried out, the location fingerprint database of target positioning region is built.Calculate respectively online Location equipment certain indicated the real-time reception signal strength signal intensity from all AP (WAP) that collects in reference point to The cosine similarity of the received signal strength vector set of each reference point in quantity set and original fingerprint data storehouse；
Step 2：Select the wherein maximum reference point of cosine similarity；
Step 3：Calculate the RSS vector sets and cosine similarity that have indicated that tuning on-line equipment is collected in reference point The correspondence ratio of received signal strength vector set items component (from the received signal strength value of each AP) of maximum reference point Value；
Step 4：The mean value of every component correspondence ratio is calculated, as the compensation correction factor in original fingerprint data storehouse；
Step 5：The received signal strength vector set of all reference points in original fingerprint data storehouse is all multiplied by into compensation correction The factor, generates new correction fingerprint database and participates in matching primitives.
It is below simulation result：
Fig. 1 illustrates that the reception of wireless signals ability of certain brand A is slightly above certain brand B and stability is also of a relatively high, but Both curves all keep identical variation tendency substantially.According to the general principle of location fingerprint positioning, if tuning on-line rank The mobile device of section different brands and model is directly using the location fingerprint database set up by certain training equipment in off-line phase Very big position error is inherently produced to carry out positioning in real time.
Fig. 2 illustrates that the signal reception difference between certain brand B of same brand and certain brand C is little, acceptable Error range in, so it is not recommended that amendment.And the signal reception between different brands equipment is implicitly present in certain difference It is different, by taking certain brand A and certain brand B as an example, although the same AP's (WAP) that both detect under same distance The size of RSS (received signal strength) value has differences, but it is basically identical with distance change trend, and kept stable Linear relationship.
Fig. 3 illustrates that the alignment system for applying the compensation correction strategy based on cosine similarity for being carried herein is divided into Three phases：Off-line training step, on-line correction stage and tuning on-line stage.In off-line training step, using off-line training Equipment selects suitable reference point to build original fingerprint data storehouse in area to be targeted；In the on-line correction stage, in reference point Finger print data gathered in the positioning region that finishes by wherein one or more reference point locations with appropriate flag tag out (particular location and quantity can be according to the rationally distributed selections of indoor environment), is then obtained using tuning on-line equipment and has indicated reference Finally use from the received signal strength vector set of each AP (WAP) in the nearest reference point of distance users in point Original fingerprint data storehouse is compensated and corrected based on the compensation correction strategy of cosine similarity, generates final correction fingerprint number According to storehouse；In the tuning on-line stage, using identical tuning on-line equipment obtain certain real-time locating point data and with correction finger print data Storehouse carries out matching primitives, obtains locating point position information.
Fig. 4 illustrates that hypothesis has n RP (reference point), m AP (WAP), the compensation school based on cosine similarity Positive strategy general principle.First, respectively calculate tuning on-line equipment certain indicated collect in reference point from all AP The reception signal of each reference point is strong in the real-time reception signal strength signal intensity vector set of (WAP) and original fingerprint data storehouse The cosine similarity CS of degree vector seti；Secondly, wherein cosine similarity CS is selectediMaximum reference point；Then, calculate this to mark Tuning on-line equipment is collected in will reference point RSS (received signal strength) vector sets and the reference point of cosine similarity maximum Received signal strength vector set items component (i.e.：From the received signal strength value of each AP) corresponding ratio ti；Then, Calculate every component correspondence ratio tiMean valueAs the compensation correction factor in original fingerprint data storehouse；Finally, will be original The received signal strength vector set of all reference points is all multiplied by the compensation correction factor in fingerprint databaseGenerate new correction to refer to Line database participates in matching primitives.
It is certain brand B the machine positioning, certain brand A original fingerprint data storehouse positioning and certain brand A correction finger print data in Fig. 5 The contrast histogram of the position error mean value, variance and standard deviation of storehouse positioning.As seen from the figure, for the original that certain brand B builds Beginning fingerprint database, compares certain brand B the machine positioning, and the mean error of certain brand A positioning has risen to 2.12m by 1.51m, and Position stability also decreases.But when certain brand A is positioned using the fingerprint database after compensation correction, its positioning is flat Error is reduced to 1.64m again, and position stability is also greatly improved.
Fig. 6 is certain brand B the machine positioning, certain brand A original fingerprint data storehouse positioning and certain brand A correction fingerprint database The positioning precision cumulative probability profiles versus figure of positioning.Knowable to the variation tendency of each bar curve in figure, for certain brand B builds Original fingerprint data storehouse, compare certain brand B the machine positioning, certain brand A positioning precision is decreased obviously, and works as certain brand A and use When fingerprint database after compensation correction is positioned, positioning precision improves significantly to original level again.

Claims (4)

1. a kind of RSS based on cosine similarity detects disparity compensation method, it is characterised in that methods described includes following step Suddenly：
Step one：Off-line training step is carried out first, the location fingerprint database of target positioning region is built, and then, is counted respectively Calculate tuning on-line equipment and indicate the real-time reception signal from all wireless access point APs that collects in reference point RP at certain Intensity vector collection, and it is similar to calculate the cosine of the received signal strength vector set of each reference point RP in original fingerprint data storehouse Degree CSi, wherein,
CS i = s i m ( R T , RP i ) = c o s θ = r t → · r p → | | r t | | · | | r p | | ;
Step 2：Select cosine similarity CS in received signal strength vector setiMaximum reference point RP；
Step 3：Calculate the received signal strength vector set and cosine that have indicated that tuning on-line equipment is collected in reference point RP The received signal strength vector set items component of maximum reference point RP of similarity, i.e., from the reception of each wireless access point AP The corresponding ratio t of signal strength valuesi, wherein,
t 1 = RSS 1 RSS i 1 , t 2 = RSS 2 RSS i 2 , ... , t m = RSS m RSS i m ;
Step 4：Calculate every component correspondence ratio tiMean valueAs the compensation correction factor in original fingerprint data storehouse, its In,
t ‾ = 1 m Σ i = 1 m t i ;
Step 5：By the received signal strength vector set of all reference points RP in original fingerprint data storehouse be all multiplied by compensation correction because SonGenerate new correction fingerprint database and participate in further matching primitives.
2. a kind of RSS based on cosine similarity according to claim 1 detects disparity compensation method, it is characterised in that： The location fingerprint database of the target positioning region in the step one, specifically includes following steps：
Step 2.1：Signal access point is set in the region for needing positioning and is numbered, carry out the division of reference point RP according to region, often Individual reference point RP arranges unique numbering, and each reference point RP can receive the signal strength signal intensity from unlike signal access point and refer to Show；
Step 2.2：The signal strength signal intensity that receives is indicated to be measured several times, its mean value is calculated, receive from not One signal strength signal intensity vector of composition is indicated with signal access point signals intensity, as the mark of reference point RP；
Step 2.3：The fingerprint of each reference point RP is stored in into database, the fingerprint base in the region is ultimately generated.
3. a kind of RSS based on cosine similarity according to claim 1 detects disparity compensation method, it is characterised in that： Cosine similarity CS in the step oneiValue between mobile device between 0.99 to 1, different mobile devices for The reflection trend of each AP signal strength signal intensity size is consistent.
4. a kind of RSS based on cosine similarity according to claim 3 detects disparity compensation method, it is characterised in that： The mobile device is unrelated with brand.
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Cited By (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107333243A (en) * 2017-08-14 2017-11-07 柳景斌 A kind of mobile device fingerprint matching localization method for exempting from hardware demarcation
CN107677989A (en) * 2017-10-26 2018-02-09 武汉大学 A kind of indoor location localization method that RSSI removal noises are carried out based on RSSI maximums
CN108307498A (en) * 2018-02-05 2018-07-20 通鼎互联信息股份有限公司 A kind of localization method and device of WSN nodes
CN109495951A (en) * 2019-01-09 2019-03-19 广东小天才科技有限公司 Method and device for testing receiving performance of wireless network
CN109766951A (en) * 2019-01-18 2019-05-17 重庆邮电大学 A kind of WiFi gesture identification based on time-frequency statistical property
CN110035384A (en) * 2019-05-09 2019-07-19 桂林电子科技大学 A kind of indoor orientation method merging multiple sensor signals filtering optimization
CN110189367A (en) * 2019-05-29 2019-08-30 Oppo广东移动通信有限公司 Calibration method and relevant device
CN111783073A (en) * 2020-07-23 2020-10-16 北京斗米优聘科技发展有限公司 Black product identification method and device and readable storage medium
CN112291705A (en) * 2020-10-26 2021-01-29 腾讯科技（深圳）有限公司 Positioning method, device, storage medium and equipment based on signal difference information
CN113242633A (en) * 2021-05-14 2021-08-10 重庆大学 Classroom lighting control system based on CSI and RSSI combined positioning

Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103533650A (en) * 2013-10-28 2014-01-22 哈尔滨工业大学 Cosine-similarity-based indoor positioning method capable of improving positioning precision
CN105898713A (en) * 2016-06-17 2016-08-24 东华大学 WiFi fingerprint indoor positioning method based on weighted cosine similarity

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103533650A (en) * 2013-10-28 2014-01-22 哈尔滨工业大学 Cosine-similarity-based indoor positioning method capable of improving positioning precision
CN105898713A (en) * 2016-06-17 2016-08-24 东华大学 WiFi fingerprint indoor positioning method based on weighted cosine similarity

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
SHUAI HAN等: "Cosine Similarity Based Fingerprinting Algorithm in WLAN Indoor Positioning Against Device Diversity", 《IEEE ICC 2015-WIRELESS COMMUNICATIONS SYMPOSIUM》 *

Cited By (13)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107333243A (en) * 2017-08-14 2017-11-07 柳景斌 A kind of mobile device fingerprint matching localization method for exempting from hardware demarcation
CN107677989A (en) * 2017-10-26 2018-02-09 武汉大学 A kind of indoor location localization method that RSSI removal noises are carried out based on RSSI maximums
CN107677989B (en) * 2017-10-26 2019-09-10 武汉大学 A kind of indoor location localization method carrying out RSSI removal noise based on RSSI maximum value
CN108307498A (en) * 2018-02-05 2018-07-20 通鼎互联信息股份有限公司 A kind of localization method and device of WSN nodes
CN108307498B (en) * 2018-02-05 2020-05-26 通鼎互联信息股份有限公司 WSN node positioning method and device
CN109495951B (en) * 2019-01-09 2021-04-30 广东小天才科技有限公司 Method and device for testing receiving performance of wireless network
CN109495951A (en) * 2019-01-09 2019-03-19 广东小天才科技有限公司 Method and device for testing receiving performance of wireless network
CN109766951A (en) * 2019-01-18 2019-05-17 重庆邮电大学 A kind of WiFi gesture identification based on time-frequency statistical property
CN110035384A (en) * 2019-05-09 2019-07-19 桂林电子科技大学 A kind of indoor orientation method merging multiple sensor signals filtering optimization
CN110189367A (en) * 2019-05-29 2019-08-30 Oppo广东移动通信有限公司 Calibration method and relevant device
CN111783073A (en) * 2020-07-23 2020-10-16 北京斗米优聘科技发展有限公司 Black product identification method and device and readable storage medium
CN112291705A (en) * 2020-10-26 2021-01-29 腾讯科技（深圳）有限公司 Positioning method, device, storage medium and equipment based on signal difference information
CN113242633A (en) * 2021-05-14 2021-08-10 重庆大学 Classroom lighting control system based on CSI and RSSI combined positioning

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