CN114372543A - RFID (radio frequency identification device) indoor multi-target 3D (three-dimensional) positioning system and method based on carrier phase - Google Patents

Abstract

The invention provides an indoor multi-target 3D positioning system and method based on Radio Frequency Identification (RFID) and carrier phase, and relates to the technical field of RFID and indoor positioning. Aiming at the technical problems of timeliness and precision of indoor multi-target positioning, the invention builds a positioning platform by virtue of the technical advantages of Radio Frequency Identification (RFID), realizes positioning target distinguishing by utilizing a passive label, converts the positioning problem into an Optimization problem, provides a Joint Particle Swarm Optimization (JPSO) algorithm by fusing a multi-Signal Classification (MUSIC) algorithm direction finding principle, a multi-carrier phase distance measurement principle and a Particle Swarm Optimization (PSO) algorithm, omits a direction finding and distance measurement retrieval process, reduces the dependence of PSO algorithm positioning precision on iteration times and Particle Swarm quantity, and directly realizes the synchronous multi-target positioning. The invention provides an effective indoor multi-target rapid high-precision positioning model, the system has simple structure and convenient deployment, and can provide positioning service in most typical indoor environments.

Description

RFID (radio frequency identification device) indoor multi-target 3D (three-dimensional) positioning system and method based on carrier phase

Technical Field

The invention relates to the technical field of Radio Frequency Identification (RFID) and indoor positioning, in particular to an RFID indoor multi-target 3D positioning system and method based on carrier phase.

Background

With the development of the times, large building groups such as shopping centers, logistics centers and the like are increasing, indoor space is gradually increased, and the demand for indoor positioning services is also increasing, so that indoor positioning systems and methods become research hotspots in recent years, but at present, indoor positioning faces many challenges. Firstly, due to the influence of building walls, the indoor satellite signal attenuation is severe, which results in poor effect of conventional satellite-based Positioning services such as Global Positioning System (GPS) in indoor environment; secondly, even if the indoor space is greatly increased compared with the prior art, a large-scale antenna array cannot be deployed like an outdoor environment, so that the indoor positioning system needs to consider the size of the occupied area and the difficulty of deployment; thirdly, according to different use environments, an indoor positioning system may need to position a target and a small object which are not easy to carry a power supply, such as a person or a book, and the like, which means that the positioning target may not carry information source equipment such as an antenna, so that the positioning system cannot use a common technology for positioning an information source, such as WiFi; finally, there may be multiple indoor positioning targets, and if the positioning is sequentially completed for multiple targets by using the conventional geometric positioning, the timeliness of the system will suddenly drop. Therefore, a new indoor positioning system and indoor positioning method are needed to overcome the above problems.

Disclosure of Invention

The invention provides an RFID (radio frequency identification) indoor multi-target positioning system and method based on carrier phases, which can utilize RFID passive tags to finish multi-target identification and realize 3D (three-dimensional) positioning of a plurality of passive tags, the positioning system is flexible to deploy, and the system can provide high-precision positioning information only by attaching one passive tag to a positioning target.

In order to achieve the purpose, the invention is realized by the following technical scheme:

an RFID indoor multi-target 3D positioning system based on carrier phase;

an RFID indoor multi-target 3D positioning system based on carrier phases comprises an RFID reader-writer, a one-transmitting multi-receiving frequency hopping transceiver, a plurality of RFID passive tags, a plurality of antennas, a kilomega switch, a data processing terminal, a plurality of kilomega network cables and a plurality of radio frequency cables.

The indoor positioning system is based on an RFID technology, the indoor positioning system based on the RFID adopts a frequency hopping technology, an RFID reader-writer of the indoor positioning system based on the RFID is responsible for establishing communication with an RFID passive tag and realizing multi-tag anti-collision, a frequency hopping transceiver of a multi-transceiver of the indoor positioning system based on the RFID is composed of a Radio frequency signal transmitting end and a plurality of Radio frequency signal receiving ends, one transmitting end of the frequency hopping transceiver of the multi-transceiver is composed of a Software Defined Radio (SDR) device connected to an antenna through a Radio frequency cable, a plurality of receiving ends of the frequency hopping transceiver of the multi-transceiver are composed of a plurality of Software Defined Radios (SDR) devices connected to a plurality of antennas through Radio frequency cables, and the transmitting end and the receiving end of the frequency hopping transceiver of the multi-transceiver are connected to a data processing terminal through a gigabit switch and a gigabit network cable, and the data processing terminal of the RFID-based indoor positioning system is responsible for controlling hardware control and subsequent data processing and positioning.

Optionally, the model of the RFID reader-writer is Impinj R420;

optionally, the RFID passive tag model is a commercial tag Impinj M4H 47;

optionally, the antenna model is a VERT 900 omnidirectional antenna;

optionally, the model of the SDR device is Universal Software Radio Peripheral (USRP) N210;

optionally, the gigabit switch has a model number TL-SG 1005D;

optionally, the data processing terminal is configured as an i7-9700 processor, a 16G operating memory, and a storage device containing codes required for controlling the SDR device to perform 3D frequency hopping, pre-process the received signal, and perform a subsequent positioning algorithm;

the invention provides a carrier phase-based RFID indoor multi-target 3D positioning method;

an RFID indoor multi-target 3D positioning method based on carrier phase comprises the following steps:

(1) the RFID reader-writer is used for establishing communication with all RFID passive tags in a positioning range and realizing anti-collision, so that only one passive tag can communicate at the same time;

(2) and acquiring reflected signals of the RFID passive tag under a plurality of different frequency point carriers by using a frequency hopping technology. Because the communication equipment uses IQ modulation, and only one tag is in a communication state at the same time, an Electronic Product Code (EPC) of the current communication tag can be analyzed from a tag reflection signal, and because the frequency hopping frequency and the time interval of the SDR equipment are controllable, the reflection signal corresponding to each frequency point of the current communication tag can be stored in a frequency point manner;

(3) and (3) completing multi-target 3D positioning by utilizing a Joint Particle Swarm Optimization (JPSO).

Further, in the step (3), the JPSO algorithm includes the specific steps of:

firstly, grouping particle swarms according to the number of EPCs solved by a system after one round of frequency hopping is finished, wherein each group is responsible for positioning one target;

secondly, initializing the position and the speed of each particle, then performing iterative computation, wherein in each iteration, each particle respectively computes a distance weight and a direction weight according to multi-frequency point reflection Signal information of a positioning target in charge of a group, a multi-carrier phase ranging principle and a multi-Signal Classification (MUSIC) algorithm principle, and moves according to a computation result;

thirdly, after each iteration is finished, the JPSO algorithm updates the historical optimal coordinates of each particle and the global optimal coordinates of each group according to the weight calculation result, and performs 'double-shock' counting update on each group according to the global optimal coordinates update condition of each group, if the counter reaches a certain value, the group of particle swarm triggers 'double-shock', the position and the speed are randomly distributed again, other groups are not influenced, and the iteration continues;

and finally, when the iteration is finished, each group of historical global optimal coordinates recorded by the JPSO is the multi-target positioning result.

The invention has the beneficial effects that:

(1) the positioning system has simple integral framework and flexible deployment, does not need complicated pre-calibration of equipment, and can be applied to most typical indoor environments;

(2) by means of the technical advantages of RFID, the method can realize target distinguishing and positioning only by attaching RFID tags with extremely low cost to a plurality of targets to be positioned, and the targets to be positioned do not need to carry other equipment;

(3) the JPSO algorithm can realize 3D status for a plurality of targets, omits the geometric retrieval process in the traditional positioning, and has higher positioning speed and higher timeliness;

(4) in the positioning method, the JPSO algorithm considers the situation that when the number of positioning targets is increased and the number of Particle swarms in each group is too small, the situation is easy to fall into local optimization, and provides a 'double-shaking' mechanism aiming at the situation, so that the dependence of the positioning precision of the traditional Particle Swarm Optimization (PSO) on the number of iterations and the number of Particle swarms is reduced, and the timeliness of the system is further improved.

Drawings

FIG. 1 is a system block diagram of the present invention.

Fig. 2 is a JPSO algorithm work flow diagram.

Figure 3 is a diagram of the MUSIC algorithm.

Detailed Description

The invention will be described in further detail with reference to the accompanying drawings. The embodiment described is only one embodiment of the invention, and not all embodiments.

The invention provides a system and a method for RFID (radio frequency identification) indoor multi-target 3D positioning based on carrier phase, and the system structure is shown in figure 1. In this embodiment, the reader in the figure uses Impinj R420 and one VERT 900 omnidirectional antenna, Tag uses a plurality of Impinj M4H47 RFID passive tags, the radio frequency signal transmitting end uses one USRP N210 and one VERT 900 omnidirectional antenna, the radio frequency signal receiving end uses four USRP N210 and four VERT 900 omnidirectional antennas to respectively build 4 receiving ends, the signal of the switch is TL-SG1005D, the positioning server is a data processing terminal, and is configured as a 16G running memory and an i7-9700 processor, and in addition, an external clock source with a model number of OCTOCLOCK-G CDA-2990 is used for USRP device synchronization in this embodiment.

When the method is used, all tags participating in positioning are ensured to be allocated with independent EPCs (the tags can be actively allocated with EPCs by a reader-writer), and then the tags are attached to the surfaces of a plurality of targets to be positioned, and a positioning system is started. In this embodiment, the frequency hopping band of the radio frequency signal receiving end and the radio frequency signal transmitting end of the positioning system is 830MHz to 960MHz, the frequency hopping interval is 10M, the frequency hopping interval is 50ms, the reader/writer can establish communication with each Tag, and ensure that at most one Tag is in a communication state at each moment, the Tag in the communication state can modulate and reflect a pure frequency hopping carrier signal sent by the radio frequency signal transmitting end, the radio frequency signal receiving end can receive a Tag reflection signal and upload data to the positioning server through the gigabit switch, the positioning server can analyze the EPC of the current reflection signal, then store the reflection signal information according to the EPC frequency dividing point, and start to execute the JPSO positioning algorithm.

In the JPSO algorithm flow chart of FIG. 2, the detailed steps for realizing the algorithm are as follows:

the method comprises the following steps: the JPSO algorithm is initialized. After one round of frequency hopping is finished, M EPCs are analyzed, the maximum iteration number of a JPSO algorithm is W, the number of particle swarm particles is G, the G particles are divided into M groups, each group of G/M particles has two attributes of position and speed, and during initialization, the positions and the speeds of all the particlesThe degrees will be randomly assigned. Note that the position of the g-th particle is pos_g,m＝(x_g,y_g) Velocity v_g＝(v_xg,v_yg) And m is the group number of the particle g, and after the initialization is completed, the iteration of the JPSO algorithm is started.

Step two: and (4) multi-target 3D positioning. In the JPSO algorithm iteration process, the position pos of the particle g_g,mPos 'in a Next State'_g,mThe update formula is:

pos'_g,m＝pos_g,m+v_g＝(x_g+v_xg,y_g+v_yg,z_g+v_zg) (1)

accordingly, v_gA lower state v'_gThe update formula is:

v'_g＝α*v_g+c_pb*(pb_g-pos'_g)+c_gb*(gb_m-pos'_g) (2)

wherein alpha represents the velocity v of the particle g in the previous state_gInfluence factor of c_pbThe influence factor, coordinate pb, representing the historical optimum position of the particle_gRepresenting the historically optimal position of the particle g, equivalent to the local optimum in FIG. 2, c_gbRepresenting a globally optimal influencing factor, coordinate gb_mThe global historical optimal position representing the mth group is equivalent to the global optimal position in fig. 2.

In the w-th iterative calculation, pb_gThe update formula of (2) is:

where F (x, y, z) is a weight calculation function, and its parameters are coordinate points. On the first iteration, pb_g＝pos_g,m(ii) a In subsequent iterations, if the current position pos of the particle g_g,mWeight by F (x, y, z) greater than pb_gThe weight of the current recording position obtained by F (x, y, z), namely F (pos)_g,m)>F(pb_g) Then pb will_gThe recorded position is updated to the current position of the particle g, otherwise pb_gRemain unchanged. Accordingly, gb_mThe update formula of (2) is:

on first iteration, gb_m(0,0, 0); in subsequent iterations, gb_mUpdate rule of pb_gSame, but pb_gOnly the change in position of the corresponding particle g is monitored, and gb_mThe position change of all particles in the m-th group is monitored.

Further, the specific formula of F (x, y, z) is:

let the antenna connected to the ith receiving terminal in the RF signal receiving terminal in FIG. 1 be antenna i, then D_ij(x, y, z) represents the coordinate (x, y, z) versus the directional weight of the antenna i and antenna j pair, T_i(x, y, z) represents the distance weight of the coordinate (x, y, z) to the antenna i. D_ij(x, y, z) and T_i(x, y, z) does not really realize direction finding and distance measuring, but respectively calculates respective weights by using the MUSIC direction finding principle and the multi-carrier phase distance measuring principle, wherein D_ijThe larger (x, y, z) represents the closer the coordinates (x, y, z) are to the actual direction of the positioning target, T_iThe smaller (x, y, z) the link distance representing the coordinates (x, y, z) is closer to the target actual link distance.

First consider D_ij(x, y, z). As one of the classical direction finding algorithms, the MUSIC algorithm strictly demonstrates the orthogonal relationship between a signal subspace and a noise subspace, and realizes the super-resolution retrieval of the signal incidence angle on the basis of the orthogonal relationship. The signal incidence scenario of the MUSIC algorithm is shown in fig. 3, and the antenna array at the radio frequency signal receiving end is a uniform linear array, wherein N receiving antennas are provided, and the spacing between the receiving antennas is d, then the angle of incidence search equation of the MUSIC algorithm is as follows:

where θ is the angle search result, i.e. the signal incident angle, U is the noise subspace extracted by the MUSIC algorithm, α (ψ) is the directional response vector established by the current search angle ψ and the antenna array, c is the speed of light, f is the current signal center frequency, (.)^TRepresenting a matrix or vector transpose.

The anti-collision mechanism of the EPC G2 protocol ensures that only one Tag is in communication at the same time, so that the MUSIC algorithm can independently extract the noise subspace of each Tag reflected signal according to IQ two-way amplitudes stored by different EPCs under each receiving antenna, thereby completing the incident direction retrieval of all tags in sequence, namely the number of the information sources is always 1 in the retrieval process, N only needs to be more than or equal to 2 to meet the requirement that the number of the antennas is more than that of the information sources when the MUSIC is retrieved, but the traditional MUSIC algorithm only uses single frequency points, which can reduce the utilization rate of multi-frequency point signals of a frequency hopping system.

Now, the B frequency points are used in common, and the antenna i and the antenna j obtained by the MUSIC algorithm are recorded at f_bThe lower Tag reflection signal noise subspace is U_ij,b，U_ij,all＝[U_ij,1,U_ij,2,...,U_ij,B]Represents the set of all frequency point noise subspaces of antenna i and antenna j, D_ijThe specific formula of (x, y, z) is:

wherein diag (. cndot.) represents taking the diagonal elements of the matrix and arranging them into a row vector, β_ijA directional response matrix is represented, which is specified as follows:

β_ij＝[β₁,β₂,...,β_b,...,β_B]

where dis (i, x, y, z) represents the distance of antenna i from the coordinates (x, y, z), which can be obtained using the Pythagorean theorem. In order to make the best use of the frequency hopping signal,expanding the direction response vector used in the traditional MUSIC algorithm retrieval in the formula (6) into a direction response matrix to obtain beta_ij，β_ijOf each column vector beta_bRepresents the directional response vector of the antenna i and the antenna j under the b frequency point, and because the positions of all the antennas and each particle are known, the beta value_bThe distance difference can be directly calculated, and the angle search in the formula (6) is omitted.

According to the MUSIC algorithm, since the number of antennas in the antenna sub-array is 2, the number of search information sources is 1, and the number of frequency points is B, U is adopted_ij,allIs a 2 × B matrix, and beta_ijAlso 2 × B matrix, and further beta in formula (7)_ij ^T*U_ij,allThe results are:

in a similar manner, in formula (7)

The results are:

the denominator of equation (7) results in:

in formula (11), res_bThe norm is just obtained for the direction response vector and the noise subspace of the same frequency point, and the method is the same as the direction retrieval mode of the MUSIC algorithm in the formula (6), but the comprehensive direction-finding result of coordinates (x, y, z) of all frequency points can be obtained by the formula (11), the frequency hopping information is fully utilized, and the condition that the noise subspaces of different frequency points are possibly different is avoided.

Equation (11) is inverted to obtain equation (7), and the larger the value of equation (7), the closer the coordinate (x, y, z) is to the positioning target direction, while equation (11) does not realize true direction finding, but calculates the direction weight of coordinate (x, y, z) in F (x, y, z).

Now consider T_i(x,_yZ) is calculated. Because the receiving end of the radio frequency signal can only obtain [ -pi, pi [ -]When the signal flight distance exceeds the signal wavelength, the resulting signal phase will contain the integer ambiguity. To estimate the signal link distance, the influence of integer ambiguity needs to be eliminated by using multi-frequency point carrier phase.

Let a complete frequency hopping period contain B frequency points, then the frequency point vector F is:

F＝[f₁,f₂,…,f_b,…f_B] (12)

make the antenna i in the receiving end of the radio frequency signal at the frequency point f_bTa obtained below_gThe IQ amplitude of the reflected signal is Q_Tag,fbAnd I_Tag,fbThen its corresponding phase

Can be expressed as:

the frequency hopping time is very short, and the flight time t of the Tag reflection signal under each frequency point is considered to be not moved in one frequency hopping period_rAre all the same, i.e.

Can be expressed as:

where mod (A, B) represents the remainder of A on B. Now using the multi-carrier phase pair t_rAnd (3) estimating:

wherein the search range of t' is0 to T. In the retrieval process, when the formula (15) obtains the minimum value, the residual phase obtained after the time of flight t 'of all frequency points is closest to the actual receiving phase of the radio frequency signal receiving end, namely the t' at the moment is considered to be t_rThe estimation result is multiplied by the speed of light, and then the estimated value of the link distance of the Tag reflected signal can be obtained, wherein the distance is the distance from the radio frequency signal transmitting end to the Tag reflected signal and then to the receiving antenna in fig. 1.

According to formula (15), T is provided_iThe specific formula of (x, y, z) is:

where dis (Tx, x, y, z) represents the distance from the transmitting end of the rf signal to the coordinates (x, y, z) in fig. 1, and dis (i, x, y, z) represents the distance from the receiving antenna i to the coordinates (x, y, z) in fig. 1. Because the actual reflected signal phase of each frequency point of Tag received by the antenna i

As known, the flight time of the Tag reflected signal is the total flight time from the radio frequency signal transmitting end to the Tag reflection and then to the antenna i, so after the total link distance is obtained, the phase of each frequency point signal in a period received by the antenna i under the distance is simulated

Then the phase of the signal with the actual phase

Calculating the error of each frequency point by taking the difference, and taking the sum of absolute values of the errors as T_i(x, y, z) results.

Because the Tag reflected signal link is a broken line and the link distance is fixed, an ellipse can be obtained by taking the radio-frequency signal transmitting end and the antenna i as the focal points, and the closer the coordinates (x, y, z) are to the ellipse, the corresponding T_iThe smaller the (x, y, z) value will be. And D_ij(x, y, z) are analogous, T_i(x, y, z) also does not really achieve ranging, but calculates the distance weight of the coordinates (x, y, z) in F (x, y, z).

By this, the calculation of F (x, y, z) is completed.

Step three: the "heavy shock" mechanism. The process of solving the optimal solution by the PSO algorithm can be approximately regarded as the process of particle swarm convergence, but most particles may occasionally converge in a certain wrong area, the converged particles cannot jump out of the area under the constraint of a particle swarm position updating mechanism, other discrete particles are attracted, and finally the PSO algorithm obtains a wrong solution, and at the moment, the PSO algorithm is considered to be trapped in local optimization.

When the number of the particles in each group is reduced when the number of the positioning targets is increased, the probability that the PSO algorithm is involved in the local optimization is increased, and in order to avoid the situation, a 'heavy shock' mechanism is introduced into the system.

The "re-vibration" mechanism will assign two counters, gCon, to each particle group_mAnd lCon_mWherein, gCon_mFor recording "heavy shock" front gb_mNumber of iterations of successive stop updates, lCon_mFor "heavy shock" rear gb_mContinuously stopping the updated iteration times; and introduce L _ gb_mFor recording gb_mThe historical optimal solution of (a).

Wherein, gCon_mThe update formula of (2) is:

lCon_mthe update formula of (2) is:

L_gb_mthe update formula of (2) is:

i.e. gCon_mAnd lCon_mWhether or not to return to zero depends on L _ gb_mWhether or not to update, and L _ gb_mIs then dependent on gb_mWhether an update has occurred. On first iteration, L _ gb_mAnd gb_mThe same is true.

When a certain group of particle swarm is trapped in local optimum, the PSO algorithm is expressed as gb corresponding to the group_mStop updating, so the system proposes L _ gb_mMonitoring gb_mTo cope with two situations:

first, gb_mThe update is stopped. At this time, L _ gb_mThe update is also stopped, resulting in F (gb)_m)＝F(L_gb_m)，gCon_mStart counting when gCon_mWhen the maximum count value is reached, the system considers that the group of particle swarms is in local optimum, the're-shaking' mechanism is triggered, all the particles with the group code m can be randomly distributed with position and velocity vectors again, and L _ gb_mThe value remains unchanged, gCon_mAnd lCon_mReturn to zero, gb_mGo back to (0,0), then proceed to the next iteration as normal; in gCon_mUntil the maximum count is reached, as long as L _ gbm is updated, gCon will be updated_mReturn to zero and the iteration continues.

Second, a "heavy shock" mechanism triggers. At this time gb_mRecording will be resumed from (0,0,0) due to L _ gb_mThe value is unchanged as long as F (gb)_m)<F(L_gb_m)，lCon_mWill continue to count when lCon_mWhen its maximum count value is reached, the system triggers a "re-shake" again; in lCon_mBefore the maximum count is reached, as long as L _ gb_mWhen update occurs, gCon will be updated_mAnd lCon_mReturn to zero and the iteration continues. lCon_mCan effectively prevent the convergence from being too slow due to too poor random position of the particle swarm after the 'heavy shock' is triggered, or the convergence is too slow at the L _ gb_mAnd the local optimum is trapped before the update is triggered.

After W iterations are finished, L _ gb_mThe position, i.e. the position estimate for each of the M objects, is recorded. Thus, multi-target 3D positioning is completed. The above-mentioned embodiments are merely preferred embodiments of the present invention, which are not intended to limit the present inventionThe scope of protection is not limited to the embodiments described above, and all equivalent substitutions, modifications, etc. made by the disclosure of the present invention should be included in the scope of protection of the present invention for those skilled in the same field.

Claims (7)

1. A Radio Frequency Identification (RFID) indoor single-station multi-target 3D positioning system based on carrier phase is characterized by comprising an RFID reader, a one-transmitting multi-receiving Frequency hopping transceiver, a plurality of RFID passive tags, a plurality of antennas, a gigabit switch, a data processing terminal, a plurality of gigabit network cables and a plurality of Radio Frequency cables. The RFID reader-writer is connected with the antenna through a radio frequency cable, transmits a signal carrying RFID command information and receives a signal reflected by the RFID passive tag, so that information interaction between the reader-writer and the tag is realized; the frequency hopping transceiver device is provided with a radio frequency signal transmitting end and a plurality of radio frequency signal receiving ends, wherein the radio frequency signal transmitting end is connected with an antenna through a radio frequency cable and transmits frequency hopping signals, and the radio frequency signal receiving ends are connected with the antenna through the radio frequency cable and receive signals reflected by a label; the data processing terminal is connected with the frequency hopping transceiver through the kilomega network cable and the kilomega switch, the label reflection signal data received by the receiving end are sent to the data processing terminal through the kilomega network cable and the switch, and the data processing terminal is responsible for controlling hardware control and subsequent data processing and positioning.

2. The RFID indoor single-station multi-target 3D positioning system based on the carrier phase as claimed in claim 1, characterized by comprising an RFID indoor single-station multi-target 3D positioning method based on the carrier phase, characterized by comprising the following steps:

(1) establishing communication with the RFID passive tag by using a reader-writer, and realizing multi-tag identification by the reader-writer through anti-collision detection;

(2) the method comprises the steps that a one-to-many receiving frequency hopping transceiver device obtains reflected signals of an RFID passive tag in a communication state under a plurality of different carrier frequencies by adopting a frequency hopping technology, analyzes tag Electronic Product Code (EPC) information in the reflected signals according to an RFID protocol, and then stores the information of the reflected signals of each frequency point respectively according to the EPC;

(3) the method comprises the steps of recognizing different targets by using different EPCs, and bringing information of frequency point reflection signals under each EPC into a Joint Particle Swarm Optimization (JPSO) algorithm to complete multi-target 3D positioning.

3. The RFID indoor single-station multi-target 3D positioning method based on the carrier phase as claimed in claim 2, wherein in the step (3), the JPSO algorithm comprises the following specific steps:

first, the JPSO algorithm is initialized. After one round of frequency hopping is finished, M EPCs are analyzed, the maximum iteration number of the JPSO algorithm is W, the particle swarm number is G, the G particles are divided into M groups, each group of G/M particles has two attributes of position and speed, and the positions and the speeds of all the particles are randomly distributed during initialization. The three-dimensional coordinate position of the g particle in the m group is pos_g,m＝(x_g,y_g,z_g) Velocity vector is v_g＝(v_xg,v_yg,v_zg) After initialization is completed, iteration of the JPSO algorithm is started;

second, multi-objective 3D localization. In the JPSO algorithm iteration process, the position pos of the particle g_g,mPos 'in a Next State'_g,mThe update formula is:

pos'_g,m＝pos_g,m+v_g＝(x_g+v_xg,y_g+v_yg,z_g+v_zg) (1)

accordingly, v_gA lower state v'_gThe update formula is:

v'_g＝α*v_g+c_pb*(pb_g-pos'_g,m)+c_gb*(gb_m-pos'_g,m) (2)

wherein alpha represents the velocity v of the particle g in the previous state_gInfluence factor of c_pbThe influence factor, coordinate pb, representing the historical optimum position of the particle_gRepresenting the historical optimum position of the particle g, c_gbRepresenting globally optimal shadowsSound factor, coordinate gb_mRepresenting the global historical optimal location of the mth group.

In the w-th iterative calculation, pb_gThe update formula of (2) is:

where F (x, y, z) is a weight calculation function, and its parameters are coordinate points. On the first iteration, pb_g＝pos_g,m(ii) a In subsequent iterations, if the current position pos of the particle g_g,mWeight by F (x, y, z) greater than pb_gThe weight of the current recording position obtained by F (x, y, z), namely F (pos)_g,m)>F(pb_g) Then pb will_gThe recorded position is updated to the current position of the particle g, otherwise pb_gRemain unchanged. Accordingly, gb_mThe update formula of (2) is:

finally, in the iterative computation process, the JPSO algorithm updates the're-vibration' count of each group according to the global optimal coordinate updating condition, if the counter reaches a threshold value N, the group of particle swarm triggers the're-vibration', each particle in the group randomly allocates a position and a speed again for iteration, other groups are not influenced, and the iteration continues; and when the iteration is finished, each group of historical global optimal coordinates recorded by the JPSO is the multi-target 3D positioning result.

4. The JPSO algorithm as claimed in claim 3, wherein in the JPSO algorithm step, the weighting function F (x, y, z) has the following formula:

wherein D is_ij(x, y, z) represents coordinates (x, y, z) relative to frequency hopping transceiverDirectional weights, T, for the device antenna i and antenna j_i(x, y, z) represents the distance weight of the coordinates (x, y, z) to the frequency hopping transceiving device antenna i.

5. A particular formula of weight function F (x, y, z) according to claim 4, characterized in that in F (x, y, z) the direction weight D_ijThe specific formula of (x, y, z) is:

wherein diag (. cndot.) represents taking the diagonal elements of the matrix and arranging them into a row vector, U_ij,all＝[U_ij,1,U_ij,2,...,U_ij,B]Representing the set of all frequency point noise subspaces of an antenna i and an antenna j, B is the number of frequency hopping frequency points, U_ij,allMiddle b element U_ij,bThe frequency point f of the antenna i and the antenna j obtained by a Multiple Signal Classification (MUSIC) algorithm_bLower label reflection signal noise subspace, beta_ijA directional response matrix is represented, which is specified as follows:

where dis (i, x, y, z) represents the distance of the antenna i to the coordinates (x, y, z). In the formula (6) < beta >_ij ^T*U_ij,allThe results are:

in a similar manner, in formula (6)

The results are:

the denominator of equation (6) results in:

6. a particular formula of a weight function F (x, y, z) according to claim 4, characterized in that in F (x, y, z), the distance weight T is_iThe specific formula of (x, y, z) is:

where dis (Tx, x, y, z) represents the distance from the transmitting end of the frequency hopping transceiver device to the coordinates (x, y, z), and dis (i, x, y, z) represents the distance from the receiving antenna i to the coordinates (x, y, z). Because the actual reflected signal phase of each frequency point of the label received by the antenna i

The known time of flight of the signal reflected by the tag is the total time of flight from the transmitting end of the frequency hopping transceiver to the tag for reflection and then to the antenna i, so that after the total link distance is obtained, the phase of the signal at each frequency point in a period received by the antenna i under the distance is simulated

Then the phase of the signal with the actual phase

Calculating the error of each frequency point by taking the difference, and taking the sum of absolute values of the errors as T_i(x, y, z) results.

7. The JPSO algorithm according to claim 3, wherein in the JPSO algorithm steps, the method for realizing the "heavy shock" mechanism comprises:

the "re-vibration" mechanism will assign two counters, gCon, to each particle group_mAnd lCon_mWherein, gCon_mFor recording "heavy shock" front gb_mNumber of iterations of successive stop updates, lCon_mFor "heavy shock" rear gb_mContinuously stopping the updated iteration times; and introduce L _ gb_mFor recording gb_mThe historical optimal solution of (a).

Wherein, gCon_mThe update formula of (2) is:

lCon_mthe update formula of (2) is:

L_gb_mthe update formula of (2) is:

i.e. gCon_mAnd lCon_mWhether or not to return to zero depends on L _ gb_mWhether or not to update, and L _ gb_mIs then dependent on gb_mWhether an update has occurred. On first iteration, L _ gb_mAnd gb_mThe same is true.

When a group of particle swarm falls into local optimum, the corresponding gb of the group_mStop updating, can pass through L _ gb_mMonitoring gb_mThe update status of (2) handles the following two cases:

first, gb_mThe update is stopped. At this time, L _ gb_mThe update is also stopped, resulting in F (gb)_m)＝F(L_gb_m)，gCon_mStart counting when gCon_mWhen the maximum count value is reached, the system considers the group of particle swarms to be in local optimum, and the 'heavy shock' mechanism is triggered and groupedAll particles with the number m will be re-randomly assigned the position and velocity vectors, L _ gb_mThe value remains unchanged, gCon_mAnd lCon_mReturn to zero, gb_mGo back to (0,0,0), then proceed to the next iteration normally; in gCon_mBefore the maximum count is reached, as long as L _ gb_mWhen update occurs, gCon will be updated_mReturn to zero and the iteration continues.

Second, a "heavy shock" mechanism triggers. At this time gb_mRecording will be resumed from (0,0,0) due to L _ gb_mThe value is unchanged as long as F (gb)_m)<F(L_gb_m)，lCon_mWill continue to count when lCon_mWhen its maximum count value is reached, the system triggers a "re-shake" again; in lCon_mBefore the maximum count is reached, as long as L _ gb_mWhen update occurs, gCon will be updated_mAnd lCon_mReturn to zero and the iteration continues. lCon_mCan effectively prevent the convergence from being too slow due to too poor random position of the particle swarm after the 'heavy shock' is triggered, or the convergence is too slow at the L _ gb_mAnd the local optimum is trapped before the update is triggered.

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