CN111741454B - RFID tag number estimation system and method based on virtual vector Aloha protocol - Google Patents
RFID tag number estimation system and method based on virtual vector Aloha protocol Download PDFInfo
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Abstract
The invention relates to an RFID tag number estimation system and an RFID tag number estimation method based on a virtual vector Aloha protocol. The invention aims to solve the problems of low accuracy in estimating the number of target tags in an RFID system caused by high resource requirement, low query speed, high computing resource cost and high time complexity of the conventional RFID base estimation protocol. The RFID tag number estimation system based on the virtual vector Aloha protocol comprises: the device comprises a virtual vector generation module, a vector analysis and probability estimation execution module, a single reader polling module and a result processing module which are based on an Aloha protocol; the virtual vector generation module based on the Aloha protocol is used for generating expected vectors of the target tag set and collecting response vectors of obtained responses. The method and the device are used in the field of RFID tag quantity estimation.
Description
Technical Field
The invention relates to an RFID tag quantity estimation system and an RFID tag quantity estimation method.
Background
In warehouse management, intelligent logistics, and supply chain management systems based on RFID (Radio Frequency Identification) technology, it is often necessary to quantitatively evaluate a particular type of tag or set of tags. The basic search process is as shown in fig. 1: the Reader (Reader) interrogates all tags with the target tag ID, each tag compares its own ID with the query ID, and the matching tag sends a response to the Reader indicating that the query was successful. Because the RFID tag carried by the target object and the reader can perform wireless identification and automatic interaction, the interaction efficiency is more efficient than that of the traditional passive optical identification technology such as bar codes or two-dimensional codes.
The problem with radix estimation is that in a given RFID system, the number of target tags currently on-line is found for a set of known target tags. There are multiple types of tags within the effective communication range of the reader, each of which is classified into an on-line and an off-line state according to whether it is in the current system or not. The problem that the radix estimation needs to solve is to estimate the number of certain tags (online) in the current RFID system.
Furthermore, in inventory management, inventory categorization, and the like applications, there are a large number of low cost tags (i.e., passive UHF tags whose resources contain 2K-5K gate level circuits according to the EPC-C1-G2 standard), and only lightweight search protocols can be deployed on tags whose resources are so limited. The radix estimation protocol based on the Bloom filter can estimate the number of target tags in the system by setting the hash function number and the mapping vector length of the Bloom filter. However, the method has the problems of selection of a large number of hash functions, high computing resource cost and high time complexity, and is not suitable for the application scene. The RFID MAC layer communication adopts a similar transmission mode of data packets (frames) in a packet switching network, and the EPC-G1 standard prescribes the information transmission rate and time slot frame format of a tag and a reader in an RFID system, wherein the data exchange between the tag and the reader is carried out in units of frames, a certain time interval exists between the frames, and each frame consists of a plurality of frame gaps (including a start mark and an end mark). By establishing the mapping relation between the tag and the frame gap, the quick large-scale RFID tag searching can be realized.
Disclosure of Invention
The invention aims to solve the problems of low accuracy in estimating the number of target tags in an RFID system caused by high resource requirement, low query speed, high calculation resource overhead and high time complexity of the conventional RFID base number estimation protocol, and provides an RFID tag number estimation system and an RFID tag number estimation method based on a virtual vector Aloha protocol.
The RFID tag number estimation system based on the virtual vector Aloha protocol comprises:
the device comprises a virtual vector generation module, a vector analysis and probability estimation execution module, a single reader polling module and a result processing module which are based on an Aloha protocol;
the virtual vector generation module based on the Aloha protocol is used for generating an expected vector V of the target tag set k [·]And collect the response vector V of the obtained response c [·]；
The vector analysis and probability estimation execution module is used for carrying out the vector analysis and probability estimation on the expected vector V k [·]And response vector V c [·]Comparing and analyzing to obtain the number estimation of the target labels;
the single reader polling module is used for moving the reader in an effective range and executing a base number estimation algorithm to realize preliminary estimation of the number of target tags;
the result processing module is used for carrying out de-duplication and summarization on the preliminary estimation results of the number of the target labels obtained by the single-reader polling module, and obtaining accurate estimation of the number of the target label set.
The RFID tag number estimation method based on the virtual vector Aloha protocol comprises the following specific processes:
step one, a reader generates a random number r and divides a frame into N pieces of length f s Combining the subframes of the current frame number f c Frame length f of subframe s Form message { f c ,f s R and broadcasting;
wherein f c ∈[0,N-1]，f s =512 is the frame length, f c Stored by the current frame counter;
step two, the reader is used for collecting T according to the known target labels k Using a uniformly distributed hash function H (TID, f c ,f s ) Mapping, filling and generating the desired vector V k [·]；
Step three, the tag in the effective range of the reader receives the information sent by the reader, and calculates the frame number of the tag according to the TID of the tagIf the message { f) sent by the reader c ,f s F in r c If the data match, the { f } is sent to the reader c ′,f s ' otherwise the tag remains silent;
wherein f s ' 512 is the frame length; f (f) c ' is the frame number to which the tag belongs;
step four, the reader uses the same hash function of the step two for all tags in the effective rangeMapping and filling response vector V c [·]；
Step five, the reader receives { f } c ′,f s ' update response vector V c [·]；
Step six, the reader is according to the expected vector V k [·]And response vector V c [·]Calculating the number K of frame gaps 1,0 、K 1,1 Obtaining a preliminary estimated value of the number of the online target labels;
step seven, judging whether an area uncovered by the reader exists or not, if so, moving the reader in the space, and executing the steps one to seven; if not, executing the step eight;
step eight, the response vector V generated by the step four c [·]Querying the expected vector V recorded in the second step k [·]And D, obtaining the identifier TID of the tag, and acquiring intersections with the result set obtained in the step seven to obtain all online target tag sets, wherein the number of set elements is an estimated value of the number of online target tags.
The beneficial effects of the invention are as follows:
aiming at the problem of estimating the number of target tags in an RFID system, a single reader is utilized to continuously move in space, a tag set in an effective communication range is searched, and the number of the target tag set is rapidly estimated based on a certain confidence coefficient. The method solves the problems of high resource requirement and low query speed of the conventional RFID base estimation protocol.
The invention uses a hash function with uniform distribution to map based on a virtual vector Aloha protocol to generate an expected vector V k [·]All tags are subjected to frame filling by using the same hash algorithm to generate a response vector V c [·]By observing the desired vector V k [·]And response vector V c [·]The number of tags is estimated with a certain confidence interval.Better performance than a deterministic estimation method is provided, the computational overhead is low, the time complexity is low, and the method is suitable for a low-cost RFID tag system. The accuracy rate of estimating the number of target tags in the RFID system is improved.
From the results in tables 2 and 3, it can be seen that although the increase in the label size causes a loss in accuracy, the accuracy value can be improved by increasing N. And the time cost of the base estimation protocol is smaller than that of the deterministic estimation protocol, and the average search time of 1000 orders of magnitude of the target label reaches the level below 1s under the condition that the total label size is 10000 orders of magnitude, so that the performance is improved by about 5 times compared with the deterministic estimation protocol.
Drawings
FIG. 1 is a schematic diagram of a basic RFID tag search process;
FIG. 2 is a block diagram of the components of the present invention;
FIG. 3 is a schematic diagram of Aloha frame gap mapping in accordance with the present invention;
FIG. 4 is a communication process diagram of a radix estimation protocol according to an embodiment of the present invention;
FIG. 5 is a graph of simulation results of an estimation protocol in the case of simulating a RFID system scan according to the present invention.
Detailed Description
The first embodiment is as follows: the RFID tag number estimation system based on the virtual vector Aloha protocol of the present embodiment includes:
the RFID tag number estimation system based on the virtual vector Aloha protocol is shown in fig. 2, and each module has the functions of:
the device comprises a virtual vector generation module, a vector analysis and probability estimation execution module, a single reader polling module, a result processing module and a test module which are based on an Aloha protocol;
the virtual vector generation module based on the Aloha protocol is used for generating an expected vector V of the target tag set k [·]And collect the response vector V of the obtained response c [·]；
The vector analysis and probability estimation execution module is used for carrying out the vector analysis and probability estimation on the expected vector V k [·]And response vector V c [·]Performing alignmentComparing and analyzing to obtain the number estimation of the target labels;
the single reader polling module is used for moving the reader in an effective range and executing a base number estimation algorithm (a vector analysis and probability estimation execution module function) to realize preliminary estimation of the number of target tags;
the result processing module is used for carrying out de-duplication and summarization on the preliminary estimation results of the number of the target labels obtained by the single-reader polling module, and obtaining accurate estimation of the number of the target label set.
The test module is used for comparing and analyzing the accurate estimated value of the number of the target label sets obtained by the result processing module with the similar estimated protocol so as to verify the feasibility and the execution effect of the protocol.
The same kind estimation protocol is a time slot Aloha protocol or a binary search algorithm, etc.
The second embodiment is as follows: the RFID tag number estimation method based on the virtual vector Aloha protocol in the embodiment comprises the following specific processes of
The RFID tag number estimate is that in a given RFID system, for a known set of target tags a= { t 1 ,t 2 ,...,t n In the current RFID system c= {.. i ,...,t j ,...,t k ,. the number of target sets t=a n C i T i i.e. the number of online target tags in the system is found. There are multiple types of tags within the reader's effective range, each of which is classified into an on-line state and an off-line state according to whether it is in the current system.
Aiming at the problem of estimating the number of target label sets of a low-cost RFID system and the problem of simultaneously meeting higher estimation efficiency and estimation precision, the invention creatively adopts an Aloha label searching method based on virtual vectors, and utilizes an Aloha protocol to define the target label sets and the expected vector V k [·]Mapping relation between the labels, and calculating response vectors V of all labels in the system c [·]The method comprises the steps of carrying out a first treatment on the surface of the Then, by the desired vector V k [·]And response vector V c [·]Corresponding relation between the reader and the current reader based on a certain confidence levelThe number of target tag sets within; and finally, continuously moving in the whole space by utilizing a single reader, summarizing the number of all the target tags in the system, and integrating the result set with the target tag set to obtain the number estimation of all the online target tags in the system in order to prevent the repeated counting problem caused by overlapping coverage areas of the readers.
Step one, a reader generates a random number r and divides a frame into N pieces of length f s Combining the subframes of the current frame number f c Frame length f of subframe s Form message { f c ,f s R and broadcasting;
wherein f c ∈[0,N-1]，f s =512 is the frame length, f c Stored by the current frame counter;
dividing a frame into N pieces of length f s Is to avoid collision problems caused by a large number of tags participating in the mapping.
Step two, a virtual vector generation module based on Aloha protocol is used for a reader to generate a target label set T according to known targets k Using a uniformly distributed hash function H (TID, f c ,f s ) Mapping, filling and generating the desired vector V k [·]；
Step three, the tag (stored TID) in the effective range of the reader receives the information sent by the reader, and calculates the frame number of the tag according to the TID of the tagIf the message { f) sent by the reader c ,f s F in r c If the data match, the { f } is sent to the reader c ′,f s ' otherwise the tag remains silent;
wherein f s ' 512 is the frame length; f (f) c ' is the frame number to which the tag belongs;
step four, the virtual vector generation module based on Aloha protocol is used for the reader to generate a virtual vector for the tags (Tag E T) c ) Using the same hash function of step twoMapping and filling response vector V c [·]；
Step five, the reader receives { f } c ′,f s ' update response vector V c [·]；
Step six, a vector analysis and probability estimation execution module is used for the reader to execute the vector V according to the expected vector k [·]And response vector V c [·]Calculating the number K of frame gaps 1,0 、K 1,1 Obtaining a preliminary estimate of the number of online target tags (by comparing the two vector state changes);
step seven, the single reader polling module is used for judging whether an area which is not covered by the reader exists (judged by an operator), if so, the reader moves in the space, and the steps one to seven are executed; if not, executing the step eight;
step eight, a result processing module is configured to generate a response vector V according to the step four in order to avoid a repetition count problem possibly caused by overlapping coverage areas of the readers c [·]Querying the expected vector V recorded in the second step k [·]And D, obtaining the identifier TID of the tag, and acquiring intersections with the result set obtained in the step seven to obtain all online target tag sets, wherein the number of set elements is an estimated value of the number of online target tags.
In the second step, the expected vectors of all the tags are recorded, and in the fourth step, the response vector of the current online tag is obtained, so that the TID of the current online tag can be obtained by inquiring the record of the second step by using the result of the fourth step. The tag TID obtained in step seven may be duplicated, so that step eight may remove duplicate TIDs, avoiding duplicate statistics.
Step seven may produce a result of repeated coverage, so step eight requires performing a deduplication operation.
And a third specific embodiment: the second difference between this embodiment and the second embodiment is that: the virtual vector generation module based on Aloha protocol in the second step is used for the reader to generate a set T according to the known target labels k Information of (a) using allEvenly distributed hash function H (TID, f c ,f s ) Mapping, filling and generating the desired vector V k [·]The method comprises the steps of carrying out a first treatment on the surface of the The specific process is as follows:
defining a hash function H (TID, f c ,f s ) The method comprises the following steps:
the rest V k [·]＝0；
Wherein TID represents tag ID, r represents random number generated by reader; v (V) k [·]Is a desired vector; % is the operation of taking the remainder,is an exclusive or operation;
wherein N x f c Determines the size of the expected vector, f s =512 is the frame length;
according to H (TID, f c ,f s ) Function filling expectation vector V k [·]；
Filled isNot V k [·]＝0；
Other steps and parameters are the same as in the second embodiment.
The specific embodiment IV is as follows: this embodiment differs from the second or third embodiment in that: the virtual vector generating module based on Aloha protocol in the fourth step is used for the reader to generate a virtual vector for the tags (Tag E T c ) The same hash function H (TID is used in step two Tc ,f c ,f s ) Mapping and filling response vector V c [·]The method comprises the steps of carrying out a first treatment on the surface of the The specific process is as follows:
hash function H (TID TC ,f c ,f s ) The method comprises the following steps:
the rest V c [·]＝0
Wherein the TID Tc Indicating that the reader is currently within the effective range T c The tag ID, r in the tag represents a random number generated by a reader;
wherein N x f c Determines the size of the expected vector, f s =512 is the frame length;
according to H (TID TC ,f c ,f s ) Function fill response vector V c [·]；
Filled isNot V c [·]＝0。
Other steps and parameters are the same as in the second or third embodiment.
Fifth embodiment: the second to fourth embodiments of the present embodiment are different from the first embodiment in that the reader in the fifth step receives { f } c ′,f s ' update response vector V c [·]；
The update rule is as follows:
if V c [f c ′×f c +f s ′]＝＝0
Then V c [f c ′×f c +f s ′]＝1
Otherwise
V c [f c ′×f c +f s ′]＝2。
Other steps and parameters are the same as those of the second to fourth embodiments.
Specific embodiment six: the difference between the present embodiment and the second to fifth embodiments is that the vector analysis and probability estimation execution module in the sixth step is used for the reader to execute the vector analysis and probability estimation according to the expected vector V k [·]And response vector V c [·]Calculating the number K of frame gaps 1,0 、K 1,1 Obtaining a preliminary estimate of the number of online target tags (by comparing the two vector state changes); the method comprises the following specific steps:
(1) calculating the number K of frame gaps 1,0 、K 1,1 ；
Wherein K is 1,0 Representation pairSatisfy (V) k [i]＝＝1)and(V c [i]Number of frame gaps of= 0);
K 1,1 representation pairSatisfy (V) k [i]＝＝1)or(V c [i]Number of frame gaps of= 0);
thus K is 1,0 Approximately equal to T k Number of labels in line, K 1,1 Can be regarded as T k The number of tags on line;
(2) according to the number K of frame gaps 1,0 、K 1,1 Calculating a preliminary estimate of the number of online target tags
Where k is the total number (T) of the target tag set (including offline tags and online tags) k )。
Other steps and parameters are the same as in one of the second to fifth embodiments.
Seventh embodiment: this embodiment differs from one of the second to sixth embodiments in that: the value of N in the fourth step is related to the precision parameter (α, β), and the specific calculation process is as follows:
(1) firstly, obtaining a function meeting normal distribution according to a central limit theorem
In the method, in the process of the invention,preliminary estimate for number of online target tags +.>Is>Preliminary estimate for number of online target tags +.>F is an objective function;
the percentage Z of beta is obtained according to the above β Make F satisfy
P{-Z β ≤F≤Z β }≥β
Wherein P {.cndot. } is a probability distribution function;
for example, if β=0.95, Z can be calculated from the normal distribution β ＝1.96。
Thus, according to the precision requirements (α, β), we get:
wherein s is the number of labels in the on-line state of the target label set;
(2) from normal distribution, the method satisfies P { -Z β ≤F≤Z β Substitution in case } is greater than or equal to beta
Wherein g (K) 1,0 ,K 1,1 ) For the ratio of the number of offline labels to the number of online labels in the target set, u is the number of all labels, E (g (K) 1,0 ,K 1,1 ) G (K) 1,0 ,K 1,1 ) M is the number of labels in the offline state in the target label result set;
obtaining the relation between N and (alpha, beta):
N≥u/[f c ln((kα 2 /gZ β 2 )+1)]
wherein the parameter u represents the number of all tags; g represents the ratio m/s of the number of the off-line labels to the number of the on-line labels; m is the number of labels in the offline state in the target label result set, s is the number of labels in the online state in the target label result set;
in practice, however, these two quantities are not directly measurable;
u/[f c ln((kα 2 /gZ β 2 )+1)]is a monotonically increasing function of u and g, and N values which enable the expression to be established are obtained by finding the maximum values of u and g;
(3) definition u max 、g max 、g min The extremum of u and g is represented; the extremum is set by the user, for example, in a warehouse management system, the user has a general knowledge of the warehoused goods, sets the upper limit of the ex-warehouse (possibly completely ex-warehouse or possibly with stock left), i.e. the maximum offline article number m, and then derives
g max ＝m max /(k-m max )
Handle-on type (g) max ＝m max /(k-m max ) Substituted N is greater than or equal to u/[ f ] c ln((kα 2 /gZ β 2 )+1)]Namely, the minimum value of N satisfying the precision (alpha, beta) is obtained (the upper limit of N is not important, and the minimum value satisfying the requirement is obtained as far as possible when the method is realized);
wherein m is max Is the maximum offline item count.
Other steps and parameters are the same as in one of the second to sixth embodiments.
The estimation value in the step sixIs an unbiased estimate of s, proving that the process is as follows:
for convenience of description, the following definitions are made:
definition T k ＝{t 1 ,t 2 ,...,t k -represents a known set of target tags (a particular type of tag set that needs to be evaluated, and assuming that the user knows this information);
definition of the definitionThe method is characterized in that all online label sets in the current system are represented, the scale of the system can be known by observing the sets, but the system is dynamically changed, so that all information of all sets in the system cannot be known;
definition T M ＝T k -T c Representing a set of tags in the target tag result set (assumed, used in analysis) that are offline in the current system;
definition T s ＝T k ∩T c A set of tags representing the current system in the target tag result set (assumed, used in analysis);
definition T o ＝T c -T k Representing a set of non-target tags in the system;
definition T U ＝T k ∪T c Representing a set of all tags of the system;
the modulus of all the above sets represents the cardinality of the set, denoted by letters k, c, m, s, o and u, respectively; all six parameters are natural numbers, and k=m+s, u=m+s+o; in addition, g=m/s represents the ratio of the number of offline tags to the number of online tags in the target tag set;
under the condition of meeting specific precision requirements (alpha, beta), estimating the number of the target labels, namely obtaining an estimated value of the solution s; the parameters (alpha, beta) represent the accuracy of the estimated values, satisfyingWherein alpha is E (0, 1)]Is the confidence interval and β e 0, 1) is the desired accuracy;
defining the number K of frame gaps 1,0 Representing the satisfaction of the pair in the vector of the two observationsSatisfy (V) k [i]＝＝1)and(V c [i]Number of frame gaps of= 0); definition K 1,1 Indicates that the satisfaction (V k [i]＝＝1)or(V c [i]Number of frame gaps for case of= 0);
K 1,0 approximately equal to T k Number of labels in line, K 1,1 Approximately equal to T k The number of tags on line; for any tag, the probability that one frame gap is selected and the other tags do not select that frame gap is noted as p 1,0 ：
Due to N x f c The value of (2) is large, so the above expression may be about equal toSatisfy Bernoulli B (m, p) 1,0 ) Distribution to obtain K 1,0 Desired value E (K) 1,0 ) Variance D (K) 1,0 ) The expression is as follows:
also for variable K 1,1 There are two cases:
(1) if the tag belongs to the online tag set T s Then when the tag occupies the frame gap, K 1,1 Will increase by 1;
define this type as K' 1,1 . For a pair ofUnique accountThe probability according to a frame gap is denoted as p' 1,1 ：
Likewise, K' 1,1 Following Bernoulli distribution B (s, p' 1,1 )，K′ 1,1 The expectation and variance are:
(2) if an off-line label and a non-target label just select the same frame gap, the label information mapping results of the off-line label and the non-target label (namely the off-line label and the non-target label which select the same frame gap) are consistent, so that K is achieved 1,1 Increase by 1. Define the number of such cases as K 1,1 . For any offline label, the probability of selecting the same frame gap with other non-target labels is marked as p 1,1 . The probability of the mapping value (32 bits) coincidence condition is 1/2 32 Then, according to the characteristic of the uniform distribution of the function, p' -can be obtained 1,1 Is represented as follows:
can also be based on K 1,1 The distribution of (1) gives K 1,1 Expected and variance:
due to K 1,1 From K' 1,1 And K' 1,1 Is composed of K' 1,1 And K' 1,1 Independent of each other, so K 1,1 ＝K′ 1,1 +K″ 1,1 ；
Therefore, E (K) 1,1 )＝E(K′ 1,1 )+E(K″ 1,1 ) The same thing can get the variance D (K 1,1 )＝D(K′ 1,1 )+D(K″ 1,1 )；
By comparing equation (5) with equation (8), it can be found that equation (8) contains a negligible minimum value of 1/2 32 This term can also be ignored in the variance, resulting in:
since m+s=k, combining equations (2) and (10) can result in the relationship of r to the desired:
if the observed value K 1,1 、K 1,0 Instead of substituting the corresponding expectations into the formula (12), the estimated quantity can be obtainedThe method comprises the following steps:
equation (13) is used as the observed value K 1,1 、K 1,0 And is defined as g (K) 1,0 ,K 1,1 ) The method comprises the steps of carrying out a first treatment on the surface of the Then, obtaining the expected and variance of the actual value s through a Taylor expansion;
define the expansion point as (α, β), where α=e (K 1,0 )、β＝E(K 1,1 ) The resulting secondary expansion is as follows:
among others
Due to K 1,1 、K 1,0 Are independent of each other, and thus when Nxf c When sufficiently large, covariance Cov (K 1,0 ,K 1,1 ) =0, function g (K 1,0 ,K 1,1 ) The second partial derivative of (2) is:
substituting the second derivative into the E (g (α, β)) equation and replacing (α, β) with the corresponding expectation yields:
substituting variance formulas (2), (3) and (10) into the above formulas to obtain:
thus estimate valueIs an unbiased estimate, and its variance is:
substituting the value of the second derivative to obtain an estimated valueThe variance of (2) is:
(after proving)
The following examples are used to verify the benefits of the present invention:
the simulation environment is shown in table 1. The reader coverage radius is 1m, and the RFID system range is 10×10m 2 . Description figure 5 shows simulation results.
TABLE 1 simulation test environmental parameter Table
In order to avoid accidental errors caused by single search, a path scanning mode is adopted. The solid black line in fig. 5 represents the scan path of the reader. Table 2 lists the average values of simulation results at subframe size n=500. Table 3 shows the variation of the protocol estimation accuracy at different subframe scales. From the results in tables 2 and 3, it can be seen that although the increase in the label size causes a loss in accuracy, the accuracy value can be improved by increasing N. And the time cost of the base estimation protocol is smaller than that of the deterministic estimation protocol, and the average search time of 1000 orders of magnitude of the target label reaches the level below 1s under the condition that the total label size is 10000 orders of magnitude, so that the performance is improved by about 5 times compared with the deterministic estimation protocol.
Table 2 average performance of an RFID tag number estimation system based on virtual vector Aloha protocol (n=500)
TABLE 3 influence of different subframe sizes on estimation accuracy
The present invention is capable of other and further embodiments and its several details are capable of modification and variation in light of the present invention, as will be apparent to those skilled in the art, without departing from the spirit and scope of the invention as defined in the appended claims.
Claims (5)
1. The RFID label number estimation method based on the virtual vector Aloha protocol is characterized by comprising the following steps of: the method comprises the following specific processes:
step one, a reader generates a random number r and divides a frame into N pieces of length f s Combining the subframes of the current frame number f c Frame length f of subframe s Form message { f c ,f s R and broadcasting;
wherein f c ∈[0,N-1]，f s =512 is the frame length, f c Stored by the current frame counter;
step two, the reader is used for collecting T according to the known target labels k Using a uniformly distributed hash function H (TID, f c ,f s ) Mapping, filling and generating the desired vector V k [·]；
TID represents tag ID;
step three, the tag in the effective range of the reader receives the information sent by the reader, and calculates the frame number of the tag according to the TID of the tagIf the message { f) sent by the reader c ,f s F in r c If the data match, the data is sent to the readerSend { f c ′,f s ' otherwise the tag remains silent;
wherein f s ' 512 is the frame length; f (f) c ' is the frame number to which the tag belongs;
step four, the reader uses the same hash function of the step two for all tags in the effective rangeMapping and filling response vector V c [·]；
Indicating that the reader is currently within the effective range T c A tag ID within;
step five, the reader receives { f } c ′,f s ' update response vector V c [·]；
Step six, the reader is according to the expected vector V k [·]And response vector V c [·]Calculating the number K of frame gaps 1,0 、K 1,1 Obtaining a preliminary estimated value of the number of the online target labels; the method comprises the following specific steps:
(1) calculating the number K of frame gaps 1,0 、K 1,1 ；
Wherein K is 1,0 Representation pairSatisfy V k [i]＝＝1andV c [i]Number of frame gaps of= 0;
K 1,1 representation pairSatisfy V k [i]＝＝1orV c [i]Number of frame gaps of= 0;
thus K is 1,0 Approximately equal to T k Number of labels in line, K 1,1 Regarded as T k The number of tags on line;
(2) according to the number K of frame gaps 1,0 、K 1,1 Calculating a preliminary estimate of the number of online target tags
Where k is the target tag set T k Is the sum of (3);
step seven, judging whether an area uncovered by the reader exists or not, if so, moving the reader in the space, and executing the steps one to seven; if not, executing the step eight;
step eight, the response vector V generated by the step four c [·]Querying the expected vector V recorded in the second step k [·]And D, obtaining the identifier TID of the tag, and acquiring intersections with the result set obtained in the step seven to obtain all online target tag sets, wherein the number of set elements is an estimated value of the number of online target tags.
2. The method for estimating the number of RFID tags based on the virtual vector Aloha protocol according to claim 1, wherein: the reader in the second step is based on the known target label set T k Using a uniformly distributed hash function H (TID, f c ,f s ) Mapping, filling and generating the desired vector V k [·]The method comprises the steps of carrying out a first treatment on the surface of the The specific process is as follows:
defining a hash function H (TID, f c ,f s ) The method comprises the following steps:
the rest V k [·]＝0；
Wherein r represents a random number generated by a reader; v (V) k [·]Is a desired vector; % is the operation of taking the remainder,is an exclusive or operation;
wherein N x f c Determines the size of the expected vector, f s =512 is the frame length;
according to H (TID, f c ,f s ) Function filling expectation vector V k [·]。
3. The method for estimating the number of RFID tags based on the virtual vector Aloha protocol according to claim 2, wherein: the reader in the fourth step uses the same hash function in the second step for all tags within the effective rangeMapping and filling response vector V c [·]The method comprises the steps of carrying out a first treatment on the surface of the The specific process is as follows:
hash functionThe method comprises the following steps:
the rest V c [·]＝0
Wherein r represents a random number generated by a reader;
wherein N x f c Determines the size of the expected vector, f s =512 is the frame length;
according to H (TID TC ,f c ,f s ) Function fill response vector V c [·]。
4. The method for estimating the number of RFID tags based on the virtual vector Aloha protocol according to claim 3, wherein: in the fifth step, the reader receives { f } c ′,f s ' update response vector V c [·]；
The update rule is as follows:
if V c [f c ′×f c +f s ′]＝＝0
Then V c [f c ′×f c +f s ′]＝1
Otherwise
V c [f c ′×f c +f s ′]＝2。
5. The method for estimating the number of RFID tags based on the virtual vector Aloha protocol according to claim 4, wherein: the value of N in the fourth step is related to the precision parameter (α, β), and the specific calculation process is as follows:
(1) firstly, obtaining a function meeting normal distribution according to a central limit theorem
In the method, in the process of the invention,preliminary estimate for number of online target tags +.>Is>Preliminary estimate for number of online target tags +.>F is an objective function;
the percentage Z of beta is obtained according to the above β Make F satisfy
P{-Z β ≤F≤Z β }≥β
Wherein P {.cndot. } is a probability distribution function;
thus, according to the precision requirements (α, β), we get:
wherein s is the number of labels in the on-line state of the target label set;
(2) from normal distribution, the method satisfies P { -Z β ≤F≤Z β Substitution in case } is greater than or equal to beta
Wherein g (K) 1,0 ,K 1,1 ) For the ratio of the number of offline labels to the number of online labels in the target set, u is the number of all labels, E (g (K) 1,0 ,K 1,1 ) G (K) 1,0 ,K 1,1 ) M is the number of labels in the offline state in the target label result set;
obtaining the relation between N and (alpha, beta):
N≥u/[f c ln((kα 2 /gZ β 2 )+1)]
wherein the parameter u represents the number of all tags; g represents the ratio m/s of the number of the off-line labels to the number of the on-line labels; m is the number of labels in the offline state in the target label result set, s is the number of labels in the online state in the target label result set;
(3) definition u max 、g max 、g min The extremum of u and g is represented; the extremum is set by the user g max ＝m max /(k-m max )
Substituting the above formula into N is greater than or equal to u/[ f ] c ln((kα 2 /gZ β 2 )+1)]Namely, the minimum value of N satisfying the precision (alpha, beta) is obtained;
wherein m is max Is the maximum offline item count.
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| CN107145807A (en) * | 2017-05-05 | 2017-09-08 | 中国石油大学(华东) | The loss label identification method of radio-frequency recognition system containing Unknown Label |
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