Sensitivity fusion positioning algorithm based on tensor decomposition

Abstract

The invention discloses a tensor decomposition-based flux fusion positioning algorithm, which comprises the following steps of: estimating a channel transmission coefficient of each subcarrier by using a single communication time slot by utilizing an OFDM (Orthogonal Frequency Division Multiplexing) technology and the characteristics of an MIMO (Multiple Input Multiple Output) system, constructing a virtual array and a virtual signal matrix, converting into a third-order tensor according to a certain method, performing CP tensor decomposition on the third-order tensor, and finally obtaining the flux fusion positioning algorithm. And the azimuth angle and distance of each target are calculated to realize the sympathetic fusion. The azimuth angle and the distance of the target can be estimated by using a single communication time slot without searching the target in the space, so that higher estimation precision and higher operation speed can be kept.

Description

Technical Field

[0001] The present invention belongs to the field of communication technologies, and more specifically, relates to a communication-sensing fusion positioning algorithm based on tensor decomposition. Background Art

[0002] With the development of mobile communication technologies represented by 5G, communication-sensing fusion has become an important development direction, aiming to enable the system to sense and locate targets while communicating. Currently, communication antenna systems using OFDM+MIMO technology are quite common. When such a system transmits a communication symbol in a communication time slot, all transmitting array elements modulate communication symbols on all subcarriers, which improves the communication rate. However, it also introduces non-orthogonality between the signals of different transmitting array elements, resulting in the inability to distinguish different symbols on the same subcarrier in the signals reflected by the target, restricting the application of many direction-of-arrival (DOA) estimation algorithms in the system and making it difficult to achieve communication-sensing fusion. Therefore, in existing communication-sensing fusion solutions, the system needs to intersperse and transmit a fixed waveform positioning time slot that cannot be used for communication in the communication time slot, receive the positioning time slot signal reflected by the target, and calculate the position of the positioning target. Communication and sensing cannot be fully integrated, resulting in a decrease in the communication rate of the system.

[0003] In view of this, the present invention is specifically proposed. Summary of the Invention

[0004] To solve the above technical problems, the basic concept of the technical solution adopted by the present invention is as follows:

[0005] Utilize the characteristics of OFDM technology and MIMO systems, use a single communication time slot to estimate the channel transmission coefficients of each subcarrier, construct a virtual array and a virtual signal matrix, and convert them into a third-order tensor according to a certain method, perform CP tensor decomposition on it, calculate the azimuth angles and distances of each target, and achieve communication-sensing fusion.

[0006] It includes the following steps. Step 1: The transmitting antenna array transmits a communication time slot, the receiving antenna array receives the signal reflected by the target, and demodulates it to obtain demodulated symbols according to the OFDM technical solution. Step 2: Estimate the channel transmission coefficient matrix G of each subcarrier based on the modulated symbols and demodulated symbols. Step 3: Vectorize the channel transmission coefficient matrices G of all subcarriers and form a matrix H as the virtual signal matrix. Step 4: Construct a third-order tensor T from the virtual signal matrix H according to a certain method. Step 5: Perform CP tensor decomposition on the tensor T to obtain a vector h containing the azimuth angle and distance information of each target, and calculate the azimuth angle and distance of each target.

[0007] The present invention has the following beneficial effects compared with the prior art:

[0008] The main innovation of the present invention lies in utilizing the characteristics of OFDM technology and MIMO system, estimating the channel transmission coefficients of each subcarrier using a single communication time slot, constructing a virtual signal matrix to achieve communication and sensing integration, without the need to use a positioning time slot with a fixed waveform. At the same time, tensor theory is introduced to solve the communication and sensing integration problem, raising the virtual signal matrix to a third-order tensor, and directly estimating the position information of each target using the CP tensor decomposition algorithm, without the need to search for targets in a given space. While maintaining a high estimation accuracy, it has a fast operating speed, can estimate the azimuth and distance of the target using a single communication time slot, and does not need to search for targets in space, and can maintain a high estimation accuracy and a fast operating speed.

[0009] The following further describes in detail the specific implementation manners of the present invention with reference to the accompanying drawings. Description of the Drawings

[0010] In the accompanying drawings:

[0011] Figure 1 is the flow chart of the implementation method of the present invention;

[0012] Figure 2 is the relative position relationship between the virtual array and two groups of array elements. Specific Embodiment

[0013] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. The following embodiments are used to illustrate the present invention.

[0014] As Figures 1 to 2 shown, the specific steps are as follows:

[0015] Assume that in a communication system using OFDM and MIMO technologies, it includes N t transmitting array elements with a spacing of d t , and N r receiving array elements with a spacing of d r . The distance between the transmitting antenna array and the receiving antenna array can be ignored relative to the distance from the target to the system; the subcarrier spacing is Δf, the number of subcarriers is K, and M modulated symbols are transmitted on each subcarrier in a time slot. There are Q far-field targets in space, with an azimuth of θ q , a distance of R q , and a reflection coefficient of β q for the signal. Denote the m-th modulated symbol transmitted by the t-th transmitting array element on the k-th subcarrier as s t [k,m]. Demodulate the received signal reflected by the target according to the OFDM technology solution to obtain the demodulated symbol. Denote the m-th demodulated symbol received by the r-th receiving array element on the k-th subcarrier as d r[k, m]. Then the mathematical model of the system can be shown as follows:

[0016]

[0017]

[0018] Among them, equation (2) represents the channel transmission coefficient. Stack all the modulation symbols on the k-th subcarrier into matrix S k , and stack all the demodulation symbols into matrix D k , and estimate the channel transmission matrix:

[0019]

[0020]

[0021]

[0022] Vectorize G k , and denote it as h k ;

[0023] Perform the above operations on K subcarriers, and stack the K h k into matrix H:

[0024]

[0025] H can be regarded as a virtual linear array containing N t N r array elements, receiving the virtual signals transmitted by Q targets, and obtaining the virtual signal matrix.

[0026] Construct tensor T according to H in the following steps:

[0027] Select two groups of array elements in the virtual linear array, and the two groups of array elements can exactly obtain the translation distance d r in space. Then set the third-order tensor and satisfy:

[0028]

[0029] Set the fourth-order tensor and satisfy:

[0030]

[0031] Perform a tensor expansion of H” modulo {1}{3}{2,4} and denote it as T:

[0032]

[0033]

[0034]

[0035] Q vectors h q contains the azimuth and distance information of Q targets. Perform CP tensor decomposition on T to obtain these Q vectors h q estimated values;

[0036] Calculate the azimuth θ q and distance R q of each target according to the following formula:

[0037]

[0038]

[0039] Complete the positioning of Q targets and achieve communication-sensing fusion with a single communication time slot.

[0040] Algorithm evaluation metrics

[0041] (1) Root mean square error RSME: Reflects the estimation accuracy of the algorithm. The smaller the value, the more accurate the estimation. Its expression is as follows:

[0042]

[0043]

[0044] where RSME θ represents the root mean square error of the algorithm in azimuth estimation, and RSME R represents the root mean square error in distance estimation. L represents the number of Monte Carlo trials, Q represents the number of targets, θ l,q , R l,q represent the actual azimuth and distance of the target, represents the estimated value of the algorithm.

[0045] (2) Average running time: Reflects the running speed of the algorithm. The smaller the value, the faster the speed.

[0046] The evaluation quality is shown in Table 1:

[0047]

[0048]

Claims

1. A joint sensing and positioning algorithm based on tensor decomposition, characterized in that By utilizing the characteristics of OFDM technology and MIMO systems, the channel transmission coefficients of each subcarrier are estimated using a single communication time slot, a virtual array and a virtual signal matrix are constructed, and they are converted into a third-order tensor according to a certain method. Then, CP tensor decomposition is performed on it to calculate the azimuth and distance of each target, realizing the integration of communication and sensing.

2. The general perception fusion positioning algorithm based on tensor decomposition according to claim 1, wherein It includes the following steps. Step 1: The transmitting antenna array transmits a communication time slot, and the receiving antenna array receives the signal reflected by the target and demodulates it to obtain demodulated symbols according to the OFDM technology solution. Step 2: Estimate the channel transmission coefficient matrix G of each subcarrier based on the modulated symbols and the demodulated symbols. Step 3: Vectorize the channel transmission coefficient matrix G of all subcarriers and form a matrix H as the virtual signal matrix. Step 4: Construct the virtual signal matrix H as a third-order tensor T according to a certain method. Step 5: Perform CP tensor decomposition on the tensor T to obtain a vector h containing the azimuth and distance information of each target, and calculate the azimuth and distance of each target.

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