# US 12,181,558 B2 · Method for estimating object angle with high-angle analysis using large-scale MIMO array antenna > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 12,181,558 B2 - Document ID: US12181558B2 - Status: Granted - Kind: B2 - Application number: 17/728,381 - Filed: 2022-04-25 - Published: 2023-04-27 - Granted: 2024-12-31 - Earliest claimed: 2021-10-27 - Assignees: Cubtek Inc. - Applicants: Cubtek Inc. - Inventors: Hsiao-Ning Wang - Web page: https://patentgrep.com/patent/US12181558B2 ## CPC - G01S 13/42: Simultaneous measurement of distance and other co-ordinates (indirect measurement G01S13/46) - G01S 2013/0245: {Radar with phased array antenna} - G01S: RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES ## Abstract A method for estimating object angle with high-angle analysis using a large-scale MIMO array antenna, wherein the array antenna receives the input signal matrix which is the transmission or reflection signal of at least one object. The method includes: step S1: inputting the input signal matrix to a first calculation model to obtain a target object amount and a rough object angle with respect to the location thereof; step S2: inputting the object amount and the input signal matrix to a second calculation model for singular value decomposition to obtain a noise matrix; step S3: obtaining an iteration angle range from the rough angle in S1; step S4: inputting the plurality of pursuit matrices corresponding to the iteration angle range and the noise matrix to a third calculation model for angle range iteration, thereby acquiring an accurate object angle. ## Claims ### Claim 1 A method for estimating object angle with high-angle analysis using large-scale MIMO array antenna, which receives an input signal matrix through an array antenna, and the input signal matrix is a transmission signal or reflection signal of at least one target object, the method comprising following steps: step S1: inputting an input signal matrix to a first calculation model, and inputting a plurality of angle related pursuit matrices for comparison; when the plurality of pursuit matrices satisfy conditions of the first calculation model, a target object amount and a rough object angle with respect to a location of a target object are obtained; step S2: inputting the target object amount and the input signal matrix to a second calculation model for carrying out a singular value decomposition, and obtaining a noise matrix; S3: obtaining an iteration angle range from the rough object angle in S1; and S4: inputting the plurality of pursuit matrices corresponding to the iteration angle range and the noise matrix to a third calculation model for an angle range iteration, thereby obtaining an accurate object angle. ### Claim 2 (depends on 1) The method of claim 1, wherein the step S1 further comprises a step S1a: the first calculation model carrying out a comparison to identify if there is a correlation between the input signal matrix and the plurality of pursuit matrices; if there is no correlation between one of the plurality of pursuit matrices and the input signal matrix, step S1a is repeated for carrying out the correlation comparison between the next one of the plurality of pursuit matrices and the input signal matrix; if there is a correlation between one of the plurality of pursuit matrices and the input signal matrix, a step S1b is then performed: carrying out a residual value calculation according to the pursuit matrix and the input signal matrix that are related. ### Claim 3 (depends on 2) The method of claim 2, wherein the step S1b defines the pursuit matrix related to the input signal matrix as a first pursuit matrix and performs the residual value calculation using the first pursuit matrix and the input signal matrix. ### Claim 4 (depends on 3) The method of claim 3, wherein the step S1 further comprises a step S1c: comparing the residual value with a residual threshold value; if the residual value presents a singular sharp reduction and is lower than or equal to the residual threshold value, the first calculation model obtains the target object amount and the rough object angle; if the residual value is larger than the residual threshold value, a step S1d is performed: carrying out a comparison of the reduction degree of the residual value. ### Claim 5 (depends on 4) The method of claim 4, wherein, in the step S1d, if the reduction degree of the residual value is larger than a significant threshold value, a step S1e is performed: storing the first pursuit matrix, and converting the input signal matrix into a second input signal matrix unrelated to the first pursuit matrix, and proceeding to step S1a to perform the correlation comparison between the second input signal matrix and others of the plurality of pursuit matrices; if the reduction degree of the residual value is smaller than the significant threshold value, the step S1d goes back to the step S1a to carry out the correlation comparison between the input signal matrix and others of the plurality of pursuit matrices. ### Claim 6 (depends on 2) The method of claim 2, wherein, in the step S1a, a scope of angle of the plurality of pursuit matrices ranges from −90 to 90 degrees, and the corresponding plurality of pursuit matrices are orderly compared with the input signal matrix for correlation comparison based on a first interval angle as a unit. ### Claim 7 (depends on 6) The method of claim 6, wherein the first interval angle ranges from 1/18 and 1/180 of the angle range of the plurality of pursuit matrices. ### Claim 8 (depends on 3) The method of claim 3, wherein, in the step S1b, the first calculation model calculates the residual value through a least square method. ### Claim 9 (depends on 1) The method of claim 1, wherein, in the step S2, the input signal matrix is first converted into a covariance matrix and then undergoes the singular value decomposition with the target object amount. ### Claim 10 (depends on 6) The method of claim 6, wherein, in the step S3, the iteration angle range is obtained from the rough object angle plus and minus an allowance angle. ### Claim 11 (depends on 10) The method of claim 10, wherein the allowance angle is smaller than the first interval angle. ### Claim 12 (depends on 10) The method of claim 10, wherein, in the step S4, the iteration angle range is orderly inputted to the third calculation model for the angle range iteration with the noise matrix based on a second interval angle as a unit. ### Claim 13 (depends on 12) The method of claim 12, wherein the second interval angle ranges from 1/10 and 1/100 of the angle range of the iteration angle range. ### Claim 14 (depends on 1) The method of claim 1, wherein, in the step S4, the third calculation model carries out the iteration with the iteration angle range and the noise matrix through an orthogonal calculation method. ### Claim 15 (depends on 1) The method of claim 1, wherein, in the step S4, after the iteration of the third calculation model, a curve of an angle and power is obtained, and an angle corresponding to a significant peak value on the curve is the accurate object angle. ### Claim 16 (depends on 1) The method of claim 1, wherein the input signal matrix is obtained from a reflection signal of the target object by the array antenna of a radar disposed on a vehicle.