Passive positioning method for MIMO-OTFS system

Abstract

The invention provides a passive positioning method for an MIMO-OTFS system. The method comprises the following steps: establishing communication connection between a transmitting end and a receiving end through a protocol; a transmitting end is used for transmitting signals, and a receiving end receives the signals through a channel tool to obtain original channel state information; performing CP decomposition operation on the original channel state information; performing signal subspace division and noise subspace division on the decomposed tensor in sequence to construct a mapping matrix, designing a spectral function by using the mapping matrix, and performing multipath parameter estimation through spectral peak search operation; and estimating motion attribute information of the target based on the accurately estimated channel parameters in combination with priori information of a perception environment to obtain position estimation and a motion track of the target. The method can effectively deal with a complex multi-path indoor environment, realizes a more accurate position estimation result by using an existing commercial terminal without a large-aperture array, and can greatly improve the robustness of a positioning system.

Description

Technical Field

[0001] The present invention relates to the technical field of computer vision and image processing, and in particular to a passive positioning method for a MIMO-OTFS system. Background Art

[0002] High-precision positioning methods based on MIMO-OTFS systems are based on channel parameter estimation. Currently, mainstream MIMO-OTFS channel parameter estimation methods are based on orthogonal matching pursuit and its related variants. Parameters include signal angle of arrival (AoA), signal time of flight (ToF), and Doppler frequency shift (DFS). However, these mainstream methods have the following common problems: (1) The performance of the orthogonal matching pursuit algorithm depends heavily on the actual sparsity of the signal. If the sparsity of the signal is lower than expected, the performance of the algorithm will degrade significantly.

[0003] (2) The orthogonal matching pursuit algorithm has high computational complexity, especially in large-scale MIMO systems. The orthogonal matching pursuit algorithm requires iterative search and update of the support set, which leads to high computational complexity.

[0004] (3) The orthogonal matching pursuit algorithm may encounter performance bottlenecks when dealing with high-dimensional channel estimation problems, and its parameter estimation accuracy is limited. Summary of the Invention

[0005] In view of the above situation, the main purpose of the present invention is to propose a target tracking method and system based on compact axial attention and detail enhancement to solve the above technical problems.

[0006] The present invention proposes a passive positioning method for a MIMO-OTFS system, the method comprising the following steps: Step 1: Establish a communication connection between the transmitter and the receiver through a protocol; Step 2: Use the transmitter to send signals, and the receiver receives the signals through the channel tool to obtain the original channel state information; Step 3: Perform CP decomposition on the original channel state information to obtain the decomposed tensor; Step 4: Perform signal subspace partitioning and noise subspace partitioning on the decomposed tensor in turn to construct a mapping matrix. Use the mapping matrix to design a spectral function, and perform multipath parameter estimation through a spectral peak search operation to obtain accurately estimated channel parameters. Step 5: Based on the accurately estimated channel parameters and the prior information of the perceived environment, the target's motion attribute information is estimated to obtain the target's position estimate and motion trajectory; Based on the target's position estimation and motion trajectory, the target positioning result is obtained.

[0007] Compared with the prior art, the present invention has the following beneficial effects: 1. This invention aims at passive positioning in outdoor high-dynamic scenarios under the MIMO-OTFS system and adopts a parameter estimation method based on tensor decomposition. This method avoids the operation of stacking the tensor data of the MIMO-OTFS system into a matrix and can effectively obtain multiple channel parameters including AoD, AoA, ToF and DFS. 2. The present invention can effectively cope with outdoor high-dynamic scenarios. Without the need for a large-aperture array, it improves existing commercial terminals, achieves more accurate position estimation results, and can greatly enhance the robustness of the positioning system. 3. The present invention effectively associates tensor decomposition with electrodynamics, realizes the association between channel parameters and target motion properties, solves the problems of low positioning accuracy and high difficulty of passive positioning systems, and provides a practical solution for the development of passive positioning.

[0008] Additional aspects and advantages of the present invention will be given in part in the following description and in part will be obvious from the following description, or will be learned through embodiments of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] Figure 1 This is a flowchart of the steps of the passive positioning method for the MIMO-OTFS system proposed in the present invention.

[0010] Figure 2 This is the motion attribute calculation and positioning tracking diagram of the passive positioning method for the MIMO-OTFS system proposed in this invention.

[0011] Figure 3 This is a structural diagram of the passive positioning system of the passive positioning method for the MIMO-OTFS system proposed in the present invention.

[0012] Figure 4 Comparison of positioning performance of different algorithms in simulation environment for the passive positioning method for MIMO-OTFS system proposed by this invention Figure 1 .

[0013] Figure 5 Comparison of positioning performance of different algorithms in simulation environment for the passive positioning method for MIMO-OTFS system proposed by this invention Figure 2 . DETAILED DESCRIPTION

[0014] The following describes embodiments of the present invention in detail. Examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended only to explain the present invention and are not to be construed as limiting the present invention.

[0015] These and other aspects of the embodiments of the present invention will become clear with reference to the following description and accompanying drawings. In these descriptions and accompanying drawings, some specific implementations of the embodiments of the present invention are specifically disclosed to provide some ways to implement the principles of the embodiments of the present invention, but it should be understood that the scope of the embodiments of the present invention is not limited thereto.

[0016] See also Figure 1 The embodiment of the present invention proposes a passive positioning method for a MIMO-OTFS system, which includes the following steps: Step 1: Establish a communication connection between the transmitter and the receiver through a protocol.

[0017] Furthermore, a connection is established between two commercial multi-antenna devices for communication. The transmitter can be any device with a built-in commercial network card, such as a mobile phone or laptop, while the receiver is required to be able to interpret the OTFS signal. The two establish a connection through a protocol.

[0018] Step 2: Use the transmitter to send signals, and the receiver receives the signals through the channel tool to obtain the original channel state information.

[0019] Furthermore, when the 2D OTFS frame size in the delay-Doppler domain is , the number of transmitting antennas is , the number of receiving antennas is , the size of the OTFS channel tensor is ; Step 3: Perform CP decomposition on the original channel state information to obtain the decomposed tensor.

[0020] In step 3, the original channel state information is subjected to a typical or parallel factor decomposition operation to obtain the decomposed tensor. The corresponding relationship in the process is as follows: ; in, represents the OTFS channel tensor, Indicates the number of multipaths, represents the outer product, represents noise, represents the delay vector, represents the Doppler vector, represents the first steering vector, represents the second steering vector, Indicates the Path, represents the amplitude of the path, represents the third steering vector, Indicates the true The delay of the path, Indicates the true The Doppler of the path, Indicates the true Multiple incident angles for each path, Indicates the true The launch angle of the path.

[0021] The expression of the delay vector is as follows: ; in, represents the band-limited pulse shaping filter response, represents the system sampling interval, Indicates the delay field size of the OTFS frame, Indicates the system sampling interval Related results; The expression of the Doppler vector is as follows: ; in, represents the partial phase value associated with the Doppler shift, Indicates the Doppler domain size of the OTFS frame, represents the imaginary unit, represents pi, Indicates the 8th sampling interval of the system, represents the sine function; The expression for the first steering vector is as follows: ; in, Indicates the number of receiving antennas, Represents the first variable; The expression for the second steering vector is as follows: ; in, Indicates the number of transmitting antennas, Represents the second variable.

[0022] Step 4: Perform signal subspace partitioning and noise subspace partitioning on the decomposed tensor in turn to construct a mapping matrix, use the mapping matrix to design the spectral function, and perform multipath parameter estimation through spectral peak search operation to obtain accurately estimated channel parameters.

[0023] In step 4, the decomposed tensor is sequentially divided into signal subspace and noise subspace to construct a mapping matrix. The corresponding relationship is as follows: ; in, represents the constructed mapping matrix, The predicted The delay of each path , the predicted Doppler of the path , the predicted The angle of incidence of each path and represents the predicted The exit angle of each path Any of the four parameters, represents the conjugate transpose of the steering vector.

[0024] The spectral function is designed using the mapping matrix, and the corresponding process relationship is as follows: ; in, Indicates the value of the variable when the function value is maximum, represents the unit matrix, Representing variables The guiding vector.

[0025] Step 5: Based on the accurately estimated channel parameters and the prior information of the perceived environment, the target's motion attribute information is estimated to obtain the target's position estimate and motion trajectory; Based on the target's position estimation and motion trajectory, the target positioning result is obtained.

[0026] See also Figure 2 In step 5, based on the accurately estimated channel parameters and the prior information of the perceived environment, the target's motion attribute information is estimated to obtain the target's position estimate and motion trajectory. The specific steps include: Applying electrodynamics to correlate the receiving end with the target movement speed and the target movement direction, thereby obtaining correlation information between the receiving end and the target movement speed, and correlation information between the receiving end and the target movement direction; The target position and motion trajectory are estimated by combining the association information between the receiving end and the target speed and the association information between the receiving end and the target direction with the accurately estimated channel parameters.

[0027] Electrodynamics is applied to correlate the receiving end with the target's velocity and direction, and the correlation information between the receiving end and the target's velocity and direction is obtained. The corresponding relationship is as follows: ; in, Indicates that the signal reaches the receiving end The frequency of Indicates that the signal reaches the receiving end The frequency of Indicates the receiving end The Doppler shift between the transmitter and the represents the speed of light, Indicates the receiving end The Doppler shift between the transmitter and Indicates the frequency of the transmitting end wave, Indicates the angle between the target motion direction and the horizontal direction, Indicates the angle between the transmitter to the target and the transmitter to the horizontal direction. Indicates the receiving end To the receiving end With the receiving end The angle between the target and Indicates the target to the receiving end With the receiving end To the receiving end The angle between the horizontal directions.

[0028] The target is calculated by combining the association information between the receiving end and the target's speed, and the association information between the receiving end and the target's direction with the accurately estimated channel parameters to obtain the target's position estimate and motion trajectory. The corresponding relationship is as follows: ; in, represents the inverse cotangent function, It represents the angle between the transmitter TX and the receiver RX1 and between the receiver RX1 and the receiver RX1 in the horizontal direction. It represents the angle between the horizontal direction of the receiving end RX2 to the transmitting end TX and the horizontal direction between the transmitting end TX to the transmitting end TX. Indicates the target's speed. represents the Doppler shift, represents the cosine function, express minus The angle difference.

[0029] For further information, see Figure 3The positioning system mainly includes a transmitting end, a receiving end and a perception data processing module.

[0030] The transmitter is a multi-antenna device used to send signals in a specified frequency band; the receiver is a multi-antenna receiving module that receives signals in the same frequency band as the transmitter.

[0031] The sensor data processing module is responsible for acquiring key information, such as the CSI data tensor, from the receiving device and storing it in a specific format. The received raw channel measurement data includes, but is not limited to, the CSI data tensor, the received signal strength indicator for each antenna, timestamp, automatic gain power, transmit rate, and other parameters. The stored channel information data is further run through the tensor decomposition positioning algorithm to obtain angle, delay, and Doppler shift information, which is then combined with environmental information to obtain positioning results.

[0032] There is no restriction on the type of wireless signal used in the communication between the sending module and the receiving module, including but not limited to ultra-wideband signals, cellular signals, Bluetooth signals, Wi-Fi signals and other signal forms.

[0033] See also Figure 4 ,In the same outdoor simulation scenario, the positioning performance of different algorithms is compared. Figure 4 shown.

[0034] like Figure 4 As shown in Figure a, the AoD estimation accuracy of the proposed method, the Cramer-Rao lower bound (CRLB), and TDLoc-OFDM are first compared. Since the PG-OMP and 3D-NOMP algorithms cannot perform off-angle estimation, they are not included in this comparison. Simulation results show that the purple solid line with square markers represents the theoretical error bound for AoD, with a mean absolute error (MAE) of 0.022°. The blue solid lines with inverted triangle and circle markers represent the proposed method and TDLoc-OFDM, respectively, with MAE values ​​of 0.065° and 0.598° for AoD estimation. Clearly, the proposed method significantly outperforms TDLoc-OFDM in AoD estimation performance and is closer to the Cramer-Rao lower bound.

[0035] Secondly, the angle of arrival (AoA) estimation accuracy of the method in this application, PG-OMP, 3D-NOMP, CRLB and TDLoc-OFDM is compared and analyzed. Figure 4 As shown in Figure b, the method in this application achieves the lowest MAE value (about 0.096°) in AoA estimation, while the MAE values ​​of AoA estimation of PG-OMP, 3D-NOMP and TDLoc-OFDM are 1.679°, 1.029° and 0.721°, respectively.

[0036] Third, the DFS estimation accuracy of the method in this application is compared with that of traditional methods. Figure 4 c shows that the DFS estimation MAE values ​​of the method in this application, PG-OMP, 3D-NOMP and TDLoc-OFDM are 2.651Hz, 6.035Hz, 6.347Hz and 17.935Hz, respectively.

[0037] The following section focuses on the comparison of the method in this application with other benchmark methods in terms of time delay (ToF) estimation accuracy. Figure 4 As shown in Figure d, the ToF estimation MAE values ​​of the method in this application, PG-OMP, 3D-NOMP and TDLoc-OFDM are 14.232ns, 10.357ns, 10.357ns and 62.346ns, respectively.

[0038] The proposed method is compared with the Cramer-Rao lower bound and TDLoc-OFDM for position estimation performance. PG-OMP and 3D-NOMP are not included in this comparison because they are dedicated to channel estimation. Figure 4 The simulation results of e show that the position estimation MAE value of the method in this application is 0.68m, which is significantly better than 4.97m of TDLoc-OFDM and is closest to the performance of the Cramer-Rao lower bound of 0.14m.

[0039] Based on the above analysis, two main conclusions can be drawn: 1. Compared with the MIMO-OFDM system (TDLoc-OFDM), the MIMO-OTFS system (the method in this application / PG-OMP / 3D-NOMP) is more suitable for high-dynamic positioning scenarios due to its significantly superior channel parameter estimation accuracy (AoD, AoA, ToF and DFS); 2. Compared with algorithms such as OMP, tensor decomposition has more advantages in channel parameter estimation because it can retain more structural information while effectively combating multipath effects (easy to satisfy the Kruskal conditions of fourth-order tensor decomposition).

[0040] See also Figure 5 ,In outdoor simulation scenarios, the relationship between algorithm performance and signal-to-noise ratio is as follows Figure 5 shown.

[0041] like Figure 5 As shown in a, the method proposed in this application has a stronger ability to resist environmental noise, and its MAE value of AoD estimation is always stable at around 0.06°. Figure 5For the AoA estimation in b, the proposed method always maintains the best performance, with a MAE value of about 0.08°. In comparison, the MAE values ​​of AoA estimation of PG-OMP and 3D-NOMP are 1.35° and 0.88° respectively, while the average precision of AoA estimation of TDLoc-OFDM (0.73°) is better, which indirectly verifies the advantage of tensor decomposition technology in parameter estimation. Figure 5 c and Figure 5 DFS and position estimation in d. The proposed algorithm maintains stability and achieves optimal estimation performance under both high and low signal-to-noise ratios. Its DFS and position MAE values ​​are stable at around 2.01Hz and 0.66m, respectively.

[0042] It should be understood that various components of the present invention can be implemented using hardware, software, firmware, or a combination thereof. In the above-described embodiments, multiple steps or methods can be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, any one of the following technologies known in the art or a combination thereof can be used: a discrete logic circuit having logic gate circuits for implementing logic functions on data signals, an application-specific integrated circuit having suitable combinational logic gate circuits, a programmable gate array (PGA), a field-programmable gate array (FPGA), etc.

[0043] Throughout this specification, reference to terms such as "one embodiment," "some embodiments," "examples," "specific examples," or "some examples" means that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0044] The above-described embodiments merely illustrate several implementations of the present invention, and while their descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that a person skilled in the art would be able to make numerous variations and improvements without departing from the spirit of the present invention, all of which fall within the scope of protection of the present invention. Therefore, the scope of protection of the present invention shall be determined by the appended claims.

Claims

1. A passive positioning method for a MIMO-OTFS system, characterized in that: The method comprises the following steps: Step 1: Establish a communication connection between the transmitter and the receiver through a protocol; Step 2: Use the transmitter to send signals, and the receiver receives the signals through the channel tool to obtain the original channel state information; Step 3: Perform CP decomposition on the original channel state information to obtain the decomposed tensor; Step 4: Perform signal subspace partitioning and noise subspace partitioning on the decomposed tensor in turn to construct a mapping matrix. Use the mapping matrix to design a spectral function, and perform multipath parameter estimation through a spectral peak search operation to obtain accurately estimated channel parameters. Step 5: Based on the accurately estimated channel parameters and the prior information of the perceived environment, the target's motion attribute information is estimated to obtain the target's position estimate and motion trajectory; Based on the target's position estimation and motion trajectory, the target positioning result is obtained.

2. The passive positioning method for the MIMO-OTFS system according to claim 1, characterized in that: In step 3, a typical or parallel factor decomposition operation is performed on the original channel state information to obtain a decomposed tensor. The relationship between the corresponding process is as follows: ； in, represents the OTFS channel tensor, Indicates the number of multipaths, represents the outer product, represents noise, represents the delay vector, represents the Doppler vector, represents the first steering vector, represents the second steering vector, Indicates the Path, represents the amplitude of the path, represents the third steering vector, Indicates the true The delay of the path, Indicates the true The Doppler of the path, Indicates the true Multiple incident angles for each path, Indicates the true The launch angle of the path.

3. The passive positioning method for the MIMO-OTFS system according to claim 2, characterized in that: The expression of the delay vector is as follows: ； in, represents the band-limited pulse shaping filter response, represents the system sampling interval, Indicates the delay field size of the OTFS frame, Indicates the system sampling interval Related results; The expression of the Doppler vector is as follows: ； in, represents the partial phase value associated with the Doppler shift, Indicates the Doppler domain size of the OTFS frame, represents the imaginary unit, represents pi, represents the sine function; The expression for the first steering vector is as follows: ； in, Indicates the number of receiving antennas, Represents the first variable; The expression for the second steering vector is as follows: ； in, Indicates the number of transmitting antennas, Represents the second variable.

4. The passive positioning method for the MIMO-OTFS system according to claim 3, characterized in that: In step 4, the decomposed tensor is sequentially divided into signal subspace and noise subspace to construct a mapping matrix. The corresponding relationship is as follows: ； in, represents the constructed mapping matrix, The predicted The delay of each path , the predicted Doppler of the path , the predicted The angle of incidence of each path and represents the predicted The exit angle of each path Any of the four parameters, represents the conjugate transpose of the steering vector.

5. The passive positioning method for the MIMO-OTFS system according to claim 4, characterized in that: The spectral function is designed using the mapping matrix, and the corresponding process relationship is as follows: ； in, Indicates the value of the variable when the function value is maximum, represents the unit matrix, Representing variables The guiding vector.

6. The passive positioning method for the MIMO-OTFS system according to claim 5, characterized in that: In step 5, based on the accurately estimated channel parameters and combined with the prior information of the perceived environment, the target's motion attribute information is estimated to obtain the target's position estimate and motion trajectory, which specifically includes the following steps: Applying electrodynamics to correlate the receiving end with the target movement speed and the target movement direction, thereby obtaining correlation information between the receiving end and the target movement speed, and correlation information between the receiving end and the target movement direction; The target position and motion trajectory are estimated by combining the association information between the receiving end and the target speed and the association information between the receiving end and the target direction with the accurately estimated channel parameters.

7. The passive positioning method for the MIMO-OTFS system according to claim 6, characterized in that: Electrodynamics is applied to correlate the receiving end with the target's velocity and direction, and the correlation information between the receiving end and the target's velocity and direction is obtained. The corresponding relationship is as follows: ； in, Indicates that the signal reaches the receiving end The frequency of Indicates that the signal reaches the receiving end The frequency of Indicates the receiving end The Doppler shift between the transmitter and represents the speed of light, Indicates the receiving end The Doppler shift between the transmitter and Indicates the frequency of the transmitting end wave, Indicates the angle between the target motion direction and the horizontal direction, Indicates the angle between the transmitter to the target and the transmitter to the horizontal direction. Indicates the receiving end To the receiving end With the receiving end The angle between the target and Indicates the target to the receiving end With the receiving end To the receiving end The angle between the horizontal directions.

8. The passive positioning method for the MIMO-OTFS system according to claim 7, characterized in that: The target is calculated by combining the association information between the receiving end and the target's speed, and the association information between the receiving end and the target's direction with the accurately estimated channel parameters to obtain the target's position estimate and motion trajectory. The corresponding relationship is as follows: ； in, represents the inverse cotangent function, Indicates the transmission end TX to the receiving end With the receiving end To the receiving end The angle between the horizontal directions, Indicates the receiving end The angle between the horizontal direction from the transmitter TX to the transmitter TX, Indicates the target's speed. represents the Doppler shift, represents the cosine function, express minus The angle difference.

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