A cabin installation planning method and system for UWB vital sign radar

Abstract

The application relates to the field of sensor deployment, and provides a cabin installation planning method and system for a UWB vital sign radar. The method comprises the following steps: performing probability weighting processing on a plurality of discrete space sampling points in a cabin according to cabin structure parameters and passenger behavior data to obtain a full-cabin detection requirement weight field; performing multipath propagation calculation on candidate links from a plurality of candidate installation points to a plurality of high-weight sampling points to obtain a time-varying shielding atlas; performing theoretical detection limit calculation on the plurality of candidate links, and screening an effective link set by a CRLB screening criterion; performing joint coverage optimization calculation on a candidate installation topology according to the effective link set and the time-varying shielding atlas to obtain an optimal installation scheme set; and after preliminary installation is completed through the optimal installation scheme set, performing residual error identification on reference target actual measurement data and a predicted value to obtain an installation correction instruction set. The application realizes full-process quantitative closed-loop planning for cabin installation of a UWB vital sign radar.

Description

Technical Field

[0001] This invention relates to the field of sensor deployment technology, and in particular to an in-cabin installation planning method and system for UWB vital signs radar. Background Technology

[0002] Ultra-wideband (UWB) vital signs radar, with its non-contact, strong penetration, and high range resolution characteristics, is widely used for occupant presence detection and vital signs monitoring in enclosed cabins such as vehicle cabins and aircraft cabins. In such scenarios, the radar needs to achieve reliable coverage of the target area within a limited space, and its installation location, antenna pointing, and multi-radar cooperative layout have a decisive impact on the final detection performance.

[0003] In existing technologies, the planning of in-cabin radar installation mainly relies on engineering experience for debugging and a small number of actual vehicle tests. It lacks a quantitative planning method that systematically integrates antenna pattern characteristics, in-cabin multipath propagation characteristics and crew dynamic behavior distribution. This results in the inability to predict blind zone distribution and assess the coverage benefits of multi-radar cooperation during the installation phase. After installation deviations occur, it is also difficult to locate the source of error and provide actionable correction criteria. As a result, the installation planning process is highly dependent on personal experience and the results are difficult to reuse. Summary of the Invention

[0004] This invention provides a method and system for planning the in-cabin installation of UWB life sign radar, in order to overcome the shortcomings of the prior art.

[0005] This invention provides an in-cabin installation planning method for UWB life sign radar, comprising: S1. Based on the cabin structure parameters and crew behavior data, multiple discrete spatial sampling points inside the cabin are subjected to probability weighting to obtain the full cabin detection requirement weight field. S2. Based on the weight field of the full cabin detection requirements, multipath propagation calculation is performed on the candidate links from multiple candidate installation points to multiple high-weight sampling points to obtain a time-varying occlusion map. S3. Based on the time-varying occlusion map and UWB radar physical parameters, perform theoretical detection limit calculations on multiple candidate links, and obtain a set of effective links by using the CRLB screening criterion. S4. Based on the effective link set and the time-varying occlusion map, perform joint coverage optimization calculation on the candidate installation topology to obtain the optimal installation scheme set; S5. After completing the initial installation using the optimal installation scheme set, residual identification is performed on the measured data and predicted values ​​of the reference target to obtain the installation correction instruction set.

[0006] According to the present invention, an in-cabin installation planning method for a UWB vital signs radar is provided, wherein step S1 further includes: S11. Collect the three-dimensional structural parameters of the cabin, the occupant behavior sample data and the human factors engineering constraint parameters, and statistically model the occupant's attitude distribution at multiple spatial sampling points to obtain the occupant attitude probability distribution function. S12. Based on the occupant posture probability distribution function, calculate the maximum value of the cosine of the angle between the occupant's chest cavity surface normal vector and multiple candidate observation directions, and perform a probability-weighted integral on the maximum value of the cosine of the angle to obtain the dynamic detection weight coefficients of multiple spatial sampling points. S13. Using the occupant posture probability distribution function as the weight, perform weighted statistics on the respiratory and cardiac displacement amplitudes at multiple spatial sampling points to obtain the expected amplitude of chest cavity micro-movement at multiple spatial sampling points. S14. Organize and store the dynamic detection weight coefficients and the expected amplitude of the thoracic cavity micro-movement in a three-dimensional voxel grid format to obtain the full cabin detection requirement weight field.

[0007] According to the present invention, an in-cabin installation planning method for a UWB vital signs radar, step S2 further includes: S21. Assign an equivalent attenuation coefficient within the UWB working frequency band to multiple material nodes in the three-dimensional structural model of the cabin. Based on the equivalent attenuation coefficient, calculate the penetration loss accumulation of the propagation path from multiple candidate installation points to multiple high-weight sampling points to obtain the static structural shading loss matrix. S22. Based on the occupant attitude probability distribution function, the additional occupant shading loss of multiple candidate links under various typical attitudes is summed by probability weighting to obtain the dynamic shading expected loss. S23. Perform path tracing on the single reflection path from multiple candidate installation points to the high-weight sampling point, and record the path gain and additional phase of multiple reflection paths; for the case where two points in a candidate installation point pair have effective propagation paths relative to the same sampling point, record the relative propagation delay difference and relative phase difference between the two paths, and mark them as a set of coherent synthesis candidate link pairs. S24. The static occlusion loss matrix, the dynamic occlusion expected loss, the path gain, the additional phase, the relative propagation delay difference, and the relative phase difference are organized and stored in a sparse tensor format to obtain a time-varying occlusion map.

[0008] According to the in-cabin installation planning method for UWB vital signs radar provided by the present invention, step S3 further includes: S31. Based on the sum of the static structural blocking loss matrix and the dynamic blocking expected loss in the time-varying blocking spectrum, and combined with the transmit power, antenna pattern gain and noise figure of the UWB radar, link budget calculation is performed on multiple candidate links to obtain the estimated signal-to-noise ratio of multiple candidate links. S32. Substitute the estimated signal-to-noise ratio, UWB center frequency and pulse accumulation number into the Cramer-Rao lower bound formula to calculate the theoretical minimum resolvable displacement of the sampling points corresponding to multiple candidate links. S33. Compare the theoretical minimum resolvable displacement with the expected amplitude of the thoracic cavity micro-motion at the corresponding sampling point in the weight field of the whole cabin detection requirement. Retain links whose theoretical minimum resolvable displacement is less than the preset value of the expected amplitude of the thoracic cavity micro-motion. For links that do not meet the conditions, force the corresponding single radar detectability metric to zero and filter to obtain a set of valid links.

[0009] According to the in-cabin installation planning method for UWB vital signs radar provided by the present invention, step S4 further includes: S41. For multiple effective links in the set of effective links, calculate the single radar detectability metric of multiple effective links based on the antenna pattern gain, the blocking correction term corresponding to the time-varying blocking pattern, and the sensitivity of the thoracic cavity micro-motion phase change. S42. For multiple link pairs in the coherent synthesis candidate link pair set relative to the same sampling point, the CRLB screening condition, the matching constraint between the relative propagation delay difference and the UWB range resolution, and the effective aperture range condition of the equivalent baseline direction angle are verified in sequence. For link pairs that simultaneously meet the conditions, the signal-to-noise ratio of each single radar is coherently synthesized by the maximum ratio merging to obtain the coherent synthesis signal-to-noise ratio and the virtual aperture expansion gain. S43. Using the minimization of the total number of activated radars, the maximization of joint coverage in high-weight areas, and the maximization of virtual aperture extension gain as multi-objective optimization functions, and constrained by the feasible region of mechanical installation, the lower limit of coverage, and the upper limit of the non-coherent coverage overlap ratio, a genetic algorithm is used to perform discrete topology search on the candidate installation point set. Then, the elevation angle and azimuth angle of multiple determined installation points are continuously optimized by gradient descent until convergence, thus obtaining the optimal installation scheme set.

[0010] According to the in-cabin installation planning method for a UWB vital signs radar provided by the present invention, in step S42, the expression for the coherent synthesized signal-to-noise ratio is:

[0011] in, For installation point and For sampling points The coherent synthesis signal-to-noise ratio, For installation point For sampling points The single radar signal-to-noise ratio, For installation point For sampling points The signal-to-noise ratio of a single radar; The expression for the virtual aperture expansion gain is:

[0012] in, To extend the gain for the virtual aperture, To activate the total number of radars, Sampling points Dynamic detection weight coefficient at the location, Sampling points The signal-to-noise ratio of coherent synthesis at the point.

[0013] According to the in-cabin installation planning method for UWB vital signs radar provided by the present invention, in step S43, the expression of the fitness function of the genetic algorithm is:

[0014] in, For the fitness function value, , , These are the trade-off coefficients for coverage integrity, virtual aperture extension gain, and radar quantity penalty, respectively. Sampling points Dynamic detection weight coefficient at the location, Sampling points Joint detectability measure at location, To cover the judgment threshold, To extend the gain of the virtual aperture, For the first The activation status of each candidate installation point This is the sampling point index value. This is the index value for the candidate installation point.

[0015] According to the in-cabin installation planning method for UWB vital signs radar provided by the present invention, step S5 further includes: S51. Place the preset standard reference targets of vibration frequency and displacement amplitude at multiple high-weight sampling points in sequence. For the sampling points marked as coherent synthesis activation in the optimal installation scheme set, coherently synthesize the echo signals by synchronous transmission of orthogonal code division waveforms and matched filtering separation, and collect the measured coherent synthesis signal-to-noise ratio. Collect the measured signal-to-noise ratio for the remaining sampling points. S52. Subtract the measured signal-to-noise ratio from the estimated signal-to-noise ratio to obtain the coverage residual; subtract the measured coherent synthesis signal-to-noise ratio from the virtual aperture expansion gain prediction value to obtain the coherent gain residual. S53. Perform correlation analysis on the spatial distribution characteristics of the coverage residual and the coherent gain residual to determine the error source type and obtain the error cause classification results. S54. Based on the error cause classification results, directly output the corresponding compensation amount for the installation height deviation or gain calibration deviation to obtain the installation correction instruction set.

[0016] According to the in-cabin installation planning method for UWB vital signs radar provided by the present invention, the error source type determination rule in step S53 is as follows: The coverage residual shows a global same-sign offset, which is determined to be either installation height deviation or gain calibration deviation. The coverage residual exhibits a unilateral gradient distribution, which is identified as an antenna attitude angle deviation. The coverage residual is concentrated in a local area and the corresponding phase residual is amplified, which is determined to be local multipath cancellation; If the coherent gain residual is greater than the convergence threshold, it is determined to be a phase synchronization delay deviation.

[0017] This invention also provides an in-cabin installation planning system for UWB life sign radar, used to execute an in-cabin installation planning method for UWB life sign radar as described above, including: Weighting module: used to perform probability weighting on multiple discrete spatial sampling points inside the cabin based on cabin structural parameters and crew behavior data, to obtain the weight field of the whole cabin detection requirements; Propagation module: used to perform multipath propagation calculations on candidate links from multiple candidate installation points to multiple high-weight sampling points based on the weight field of the full cabin detection requirements, and obtain time-varying occlusion maps; Calculation module: used to perform theoretical detection limit calculations on multiple candidate links based on the time-varying occlusion map and UWB radar physical parameters, and to obtain a set of effective links by using the CRLB screening criterion; Optimization module: used to perform joint coverage optimization calculation on candidate installation topologies based on the set of effective links and the time-varying occlusion map, to obtain the set of optimal installation schemes; Output module: After the initial installation is completed using the optimal installation scheme set, residual identification is performed on the measured data and predicted values ​​of the reference target to obtain the installation correction instruction set.

[0018] This invention discloses an in-cabin installation planning method and system for UWB vital signs radar. By incorporating the occupant attitude probability distribution function and the expected amplitude of chest cavity micro-movements into the detection requirement model, all subsequent planning calculations are driven by the locations of high-probability vital signs in actual occupant behavior scenarios, rather than by uniform physical space. This allows the dynamic detection weighting coefficient to accurately reflect the comprehensive detection value of different spatial locations under all driving conditions, significantly reducing ineffective planning investment in low-probability areas. Secondly, this invention calculates the path-by-path penetration loss accumulation of the equivalent attenuation coefficient of the in-cabin structural materials and the probability of dynamic occupant obstruction expected loss. The weighted fusion yields a time-varying occlusion map that simultaneously includes information on both static structural occlusion and dynamic occlusion caused by changes in occupant attitude. This avoids the occlusion estimation distortion problem caused by relying solely on static lookup tables in existing technologies. Furthermore, the labeling of the candidate links in the coherent synthesis process pre-embeds complete propagation parameters for subsequent coherent synthesis calculations. Secondly, before multi-radar topology optimization, this invention introduces a theoretical detection limit screening based on the Cramer-Rao lower bound. This compares the theoretical minimum resolvable displacement with the expected value of the chest cavity micro-motion amplitude, making a physical feasibility judgment for each candidate link at the information theory level. This ensures that the link set entering the optimization stage has already undergone electromagnetic propagation feasibility testing. The dual screening process, combining theoretical feasibility testing, narrows the search space of the genetic algorithm and avoids overly optimistic estimations caused by using engineering experience thresholds. In the joint coverage optimization stage, the invention introduces a virtual aperture extension gain term, causing the genetic algorithm to spontaneously favor installation location combinations with reasonable spacing that can form effective virtual apertures when searching for installation topologies. This jointly solves the multi-radar installation site selection and distributed coherent radar array design, achieving the additional signal-to-noise ratio gains from coherent synthesis without increasing hardware costs. For areas that do not meet the coherent synthesis conditions, the system automatically degenerates into an incoherent independent coverage mode, ensuring system robustness. In the residual identification stage after installation, the separation analysis mechanism of coverage residual and coherent gain residual enables the independent location of four types of error sources: installation height deviation, antenna attitude angle deviation, local multipath cancellation, and phase synchronization delay deviation. For antenna attitude angle deviation, the elevation and azimuth corrections are directly solved by the pseudo-inverse of the Jacobian matrix. For local multipath cancellation, the time-varying obstruction pattern is locally updated and theoretical screening and topology optimization are re-executed, forming a complete closed loop of three levels: coverage planning, theoretical screening, and installation verification. The final installation acceptance report includes a full-cabin joint detectability measurement distribution map and blind zone classification, providing archiveable quantitative proof of installation quality. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0020] Figure 1 A schematic diagram illustrating the in-cabin installation planning method for a UWB vital signs radar provided by this invention. Figure 2 This invention provides a schematic diagram of an in-cabin installation planning system for a UWB vital signs radar. Detailed Implementation

[0021] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, embodiments of this invention, and should not be construed as limiting the invention. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention. In the description of this invention, it should be understood that the terminology used is for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0022] The embodiments of the present invention are described below with reference to the figures.

[0023] like Figure 1 As shown, this invention provides an in-cabin installation planning method for UWB life sign radar, comprising: S1. Based on the cabin structure parameters and crew behavior data, multiple discrete spatial sampling points inside the cabin are subjected to probability weighting to obtain the weight field of the whole cabin detection requirements.

[0024] Step S1 further includes: S11. Collect the three-dimensional structural parameters of the cabin, occupant behavior sample data and human factors engineering constraint parameters, and statistically model the occupant attitude distribution at multiple spatial sampling points to obtain the occupant attitude probability distribution function.

[0025] Furthermore, the three-dimensional structural parameters of the cabin described in this invention include the geometric dimensions and spatial position information of rigid structures such as seats, doors and windows, A-pillars, and B-pillars, which are used to subsequently establish a three-dimensional coordinate system for the cabin space and define the spatial occupancy range of each structure; the occupant behavior sample data comes from the statistical analysis of large-sample real vehicle video recordings, specifically including the position records of the occupant's head, chest cavity, and limbs in three-dimensional space under different driving conditions such as constant speed driving, turning, and braking. For the above position records, this invention performs kernel density estimation processing to obtain the probability density value of the occupant's chest cavity at each spatial position; the ergonomic constraint parameters are based on relevant ergonomic standards, extracting the spatial distribution range of the chest cavity defined by the occupant's hip reference point envelope, and using this range as the spatial boundary for occupant posture statistical modeling to ensure that the modeling results do not exceed the posture range allowed by ergonomics.

[0026] After obtaining the above parameters, this invention discretizes the cabin space, dividing the three-dimensional space into a uniform grid with a resolution of no less than 3 cm, with each grid node serving as a spatial sampling point. For each sampling point, this invention inputs the records of chest cavity occurrence near that point from the occupant behavior sample data, along with ergonomic constraint parameters, into the statistical modeling process. Using the sampling point coordinates and the occupant's body orientation angle as dual independent variables, kernel density estimation is performed on the chest cavity position records in the neighborhood of the sampling point to obtain the probability density value of the occupant's chest cavity appearing at that sampling point with a certain body orientation. After repeating the above calculation for all sampling points and all body orientation angles, the final occupant attitude probability distribution function covering the entire cabin space is obtained. This function is stored in the form of a two-dimensional lookup table, where the row index is the spatial coordinates of the sampling point, the column index is the discretized interval of the body orientation angle, and each element in the table is the probability density value under the corresponding combination of sampling point and body orientation.

[0027] S12. Based on the occupant posture probability distribution function, calculate the maximum value of the cosine of the angle between the occupant's chest cavity surface normal vector and multiple candidate observation directions, and perform probability-weighted integration on the maximum value of the cosine of the angle to obtain the dynamic detection weight coefficients of multiple spatial sampling points.

[0028] Furthermore, the goal of calculating the dynamic detection weighting coefficient is to quantify the overall observable potential of each sampling point for vital sign detection after considering the probability of occurrence of all occupant postures. Specifically, in step S12, for each sampling point, the present invention first calculates the cosine value of the angle between the occupant's chest cavity surface normal vector and each candidate observation direction at the sampling point on all discretized body orientation angles. The chest cavity surface normal vector is uniquely determined by the occupant's body orientation angle and points directly forward of the chest cavity. The candidate observation directions are unit vectors pointing from the sampling point to each candidate installation position. After calculating the cosine value for all candidate observation directions, the maximum value is taken as the projection coefficient of chest cavity micro-movement under that body orientation on the optimal observation direction, and is denoted as the observable potential value of the sampling point under that body orientation.

[0029] Subsequently, this invention uses the probability density values ​​of each body orientation corresponding to the sampling point in the occupant posture probability distribution function as integral weights to perform a weighted summation of the observable potential values ​​on all discretized body orientation angles, obtaining the dynamic detection weight coefficient for that sampling point. The physical meaning of this coefficient is: considering the probability of an occupant appearing at that sampling point under all driving conditions and the distribution of chest cavity orientation at that time, the statistically expected observability of vital signs at that sampling point. A higher dynamic detection weight coefficient indicates a greater overall probability that the occupant's chest cavity will appear at that sampling point in actual use scenarios and can be effectively observed; therefore, priority should be given to ensuring coverage quality in this area in subsequent planning.

[0030] The expression for the dynamic detection weight coefficient in step S12 is:

[0031] in, Sampling points Dynamic detection weight coefficient at the location; Let be the occupant posture probability distribution function, representing the occupant's chest cavity facing the body angle. Appearing at sampling points The probability density at that location; For the body orientation angle The maximum value of the cosine of the angle between the normal vector of the occupant's chest cavity surface and the directions of all candidate mounting points reflects the projection coefficient of the chest cavity micro-motion in the optimal observation direction under this attitude; the integral is performed in the orientation angle space of all body postures.

[0032] S13. Using the occupant posture probability distribution function as weight, perform weighted statistics on the respiratory and cardiac displacement amplitudes at multiple spatial sampling points to obtain the expected amplitude of chest cavity micro-movement at multiple spatial sampling points.

[0033] In step S13, the present invention aims to calculate the expected amplitude of chest cavity micro-movement and establish a displacement reference value reflecting the intensity of the actual occupant's vital signs signal for each sampling point, which is used for the theoretical detection limit screening in subsequent step S3. Furthermore, the human chest cavity produces periodic micro-displacements driven by both breathing and heartbeat. The displacement amplitude varies under different postures; specifically, the chest cavity displacement amplitude caused by breathing in a sitting position is approximately four millimeters, and the heartbeat amplitude is approximately 0.5 mm; while in a lying position, the displacement amplitude is approximately 6 mm, and the heartbeat amplitude is approximately 0.8 mm. Based on this, in step S13 of the present invention, for each sampling point, the probability density values ​​of each body orientation corresponding to the sampling point in the occupant posture probability distribution function are used as weights to perform weighted summation of the respiratory and heartbeat displacement amplitudes in the sitting and lying positions, respectively, to obtain the statistical expectation value of the chest cavity micro-motion displacement after considering the probability of occurrence of each occupant posture at the sampling point, that is, the expectation value of the chest cavity micro-motion amplitude, in millimeters. This value reflects the actual expected chest cavity micro-motion signal amplitude at the sampling point after considering the occupant posture probability distribution, and is the benchmark for judging whether a candidate link theoretically has the ability to detect vital signs.

[0034] The expression for the expected amplitude of thoracic cavity micromotion in step S13 is:

[0035] in, Sampling points Expected amplitude of pleural micromovement at the location; For the crew, the first Typical body posture orientation Appearing at sampling points The probability density at that location; For the first The displacement amplitude caused by thoracic respiration or heartbeat of the occupant under various typical body postures; summation is performed across all typical body posture orientations. This is the index value for typical body posture.

[0036] S14. Organize and store the dynamic detection weight coefficients and the expected amplitude of the thoracic cavity micro-movement in a three-dimensional voxel grid format to obtain the full cabin detection requirement weight field.

[0037] In step S14, the dynamic detection weight coefficients of each sampling point obtained in S12 and the expected amplitude of thoracic micro-motion of each sampling point obtained in S13 are organized and stored in a three-dimensional voxel grid format according to the three-dimensional spatial coordinate index of the sampling points. During storage, this invention stores two numerical fields, the dynamic detection weight coefficient and the expected amplitude of thoracic micro-motion, on each voxel node simultaneously. After the above storage is completed for all sampling points in the entire cabin, the entire voxel grid constitutes the weight field of the entire cabin detection requirement. After storage, this invention compares the dynamic detection weight coefficients of all voxel nodes in the grid with a preset importance threshold node by node, and marks the voxel nodes whose dynamic detection weight coefficients exceed the threshold as high-weight sampling points. The set of high-weight sampling points is used as the input of the key target location for propagation calculation in step S2.

[0038] S2. Based on the weight field of the full cabin detection requirements, multipath propagation calculation is performed on the candidate links from multiple candidate installation points to multiple high-weight sampling points to obtain a time-varying occlusion map.

[0039] Step S2 further includes: S21. Assign equivalent attenuation coefficients within the UWB working frequency band to multiple material nodes in the three-dimensional structural model of the cabin. Based on the equivalent attenuation coefficients, calculate the penetration loss accumulation of the propagation paths from multiple candidate installation points to multiple high-weight sampling points to obtain the static structural shading loss matrix.

[0040] In step S21, based on the three-dimensional structural model of the cabin established in step S11, this invention assigns an equivalent attenuation coefficient within the UWB working frequency band to each material node in the model. This equivalent attenuation coefficient is a quantitative representation of the penetration loss per unit thickness of a specific material to electromagnetic signals within the UWB frequency band. The values ​​for different materials vary significantly. Metal structures strongly block UWB signals, while glass and foam-filled seats result in moderate penetration loss. In this invention, the values ​​of the material equivalent attenuation coefficients are obtained through electromagnetic simulation software or anechoic chamber measurements and stored in a material parameter lookup table. After assigning the material values, this invention uses a simplified ray tracing method combining geometric optics and physical optics to establish a propagation calculation engine. Specifically, a virtual ray beam is emitted along a straight line from the emission point, and the intersection of the ray with each geometric object in the three-dimensional structural model is detected segment by segment. For each detected penetrating material, the corresponding equivalent attenuation coefficient is read from the material parameter lookup table and multiplied by the penetration thickness of that segment to obtain the loss contribution. All penetration loss contributions are accumulated along the entire ray path to obtain the total penetration loss of that path.

[0041] Based on this, the present invention starts from each candidate installation point, emits a virtual ray beam to each high-weight sampling point and performs the above-mentioned cumulative calculation. After completing the calculation for each combination of all candidate installation points and all high-weight sampling points, a static structural occlusion loss matrix is ​​constructed using the candidate installation points and high-weight sampling points as dual indices. Each element in the matrix stores the cumulative penetration loss value of the corresponding path.

[0042] S22. Based on the occupant attitude probability distribution function, the additional occupant shading loss of multiple candidate links under various typical attitudes is summed by probability weighting to obtain the dynamic occupant shading expected loss.

[0043] Furthermore, since the occupant's own obstruction of the signal propagation path changes dynamically with the occupant's posture, the static structural obstruction loss matrix cannot reflect this dynamic characteristic. Therefore, this invention superimposes the statistical expectation of the occupant's dynamic obstruction on the static loss.

[0044] This invention uses simplified geometry to perform equivalent modeling of occupants, including: the torso is approximately an ellipsoid, the limbs are approximately cylinders, and the geometric dimensions of each part are set according to anthropometry standard data.

[0045] For each typical attitude covered by the occupant attitude probability distribution function defined in step S11, this invention places the corresponding occupant equivalent scatterer model into the corresponding position of the cabin's three-dimensional structural model, and re-performs ray tracing calculations for the occupant equivalent scatterer under each typical attitude to obtain the additional occupant ...

[0046] The expression for the expected loss of dynamic occlusion in step S22 is:

[0047] in, Candidate installation points To the sampling point The expected loss due to dynamic occlusion of the link; For the crew, the first A typical body posture appeared at the sampling point The probability density at that location; For the first The equivalent scatterer of the crew in a typical body shape to the link Additional occlusion loss is generated; summation is performed across all typical body orientations.

[0048] S23. Perform path tracing on the single reflection path from multiple candidate installation points to the high-weight sampling point, and record the path gain and additional phase of multiple reflection paths; for the case where two points in a candidate installation point pair have effective propagation paths relative to the same sampling point, record the relative propagation delay difference and relative phase difference between the two paths, and mark them as a set of coherent synthesis candidate link pairs.

[0049] Furthermore, in addition to the direct path, UWB signals also have a primary reflection path inside the cabin, generated by reflective surfaces such as the roof lining, the inside of the windshield, and the inner door panels. The primary reflection path is the propagation path of the signal from the transmitting point, after one specular reflection by a certain reflective surface inside the cabin, to the receiving point. Its path gain depends on the reflection coefficient of the reflective surface and the total path length, and the additional phase depends on the total path length and the phase shift introduced by the reflection.

[0050] In step S23, the present invention tracks all primary reflection paths between each candidate installation point and each high-weight sampling point one by one. Specifically, for each combination of candidate installation point and high-weight sampling point, the potential reflective surfaces inside the cabin are traversed. Based on the mirror principle, virtual mirror points that meet the reflection geometry conditions are determined. The complete reflection path through the reflective surface is calculated. The path gain and additional phase of the path are calculated based on the reflection coefficient of the reflective surface material. The above values ​​are recorded in the multipath parameter table of the corresponding link.

[0051] After completing the multipath recording of each candidate installation point, the present invention further performs pairwise checks on the candidate installation point pairs: for the two installation points in the candidate installation point pair, if there are effective propagation paths from both to the same high-weight sampling point, the propagation delay of each of the two paths is calculated, and the relative propagation delay difference is obtained by subtracting the two. At the same time, the signal phase difference caused by the two paths at the receiving end is calculated to obtain the relative phase difference. The triple formed by the installation point pair and the corresponding sampling point is marked into the coherent synthesis candidate link pair set for use in the coherent MIMO virtual aperture calculation in step S4.

[0052] S24. The static occlusion loss matrix, the dynamic occlusion expected loss, the path gain, the additional phase, the relative propagation delay difference, and the relative phase difference are organized and stored in a sparse tensor format to obtain a time-varying occlusion map.

[0053] In step S24, the present invention organizes and stores the static structural occlusion loss matrix obtained in S21, the dynamic occlusion expected loss obtained in S22, the path gain and additional phase of each reflection path obtained in S23, and the relative propagation delay difference and relative phase difference of each link pair in the coherent synthesis candidate link pair set in a sparse tensor format according to the dual indexes of candidate installation points and high-weight sampling points, to obtain a time-varying occlusion map. The sparse tensor is a data structure that only stores non-zero elements and their corresponding index positions. It is suitable for the situation in the present invention where a large number of links inside the cabin suffer losses approaching infinity due to complete occlusion, and the corresponding elements do not need to be stored. Compared with the dense matrix storage method, it can significantly reduce memory usage.

[0054] For the obtained time-varying occlusion map, the record of each valid link includes static structural penetration loss, dynamic occlusion expected loss, path gain and additional phase of each first reflection path, and if the link pair belongs to the coherent synthesis candidate link pair set, the relative propagation delay difference and relative phase difference are additionally stored. All fields are retrieved and read / written using the candidate installation point number and the high-weight sampling point number as a joint index.

[0055] S3. Based on the time-varying occlusion map and UWB radar physical parameters, perform theoretical detection limit calculations on multiple candidate links, and obtain a set of effective links by using the CRLB screening criterion.

[0056] Step S3 further includes: S31. Based on the sum of the static structural blocking loss matrix and the dynamic blocking expected loss in the time-varying blocking spectrum, and combined with the transmit power, antenna pattern gain and noise figure of the UWB radar, link budget calculation is performed on multiple candidate links to obtain the estimated signal-to-noise ratio of multiple candidate links.

[0057] In step S31, this invention uses the static structural blocking loss matrix and the expected dynamic blocking loss stored in the time-varying blocking map as a basis, and accumulates them link by link to obtain the total comprehensive path loss for each candidate link. Then, the total comprehensive path loss, along with the UWB radar's transmit power, antenna pattern gain, and noise figure, is substituted into the radar equations for link budget calculation. The antenna pattern gain describes the antenna's ability to radiate or receive electromagnetic energy in a specific spatial direction; its value varies with the transmit and receive directions. This invention uses a lookup table interpolation method to read the corresponding gain value from pre-measured or simulated antenna pattern data based on the azimuth angle from the candidate installation point to the corresponding high-weight sampling point. The noise figure, a parameter describing the level of thermal noise introduced by the radar receiver itself, together with the system bandwidth and operating temperature, determines the receiver's noise floor power. Finally, this invention adds the transmit antenna pattern gain to the transmit power, subtracts the total comprehensive path loss, adds the receive antenna pattern gain, and subtracts the receiver's noise floor power to obtain the estimated signal-to-noise ratio for the sampling point corresponding to the candidate link. After performing the above calculations on all valid candidate links stored in the time-varying occlusion map one by one, a predicted signal-to-noise ratio dataset with candidate installation points and high-weight sampling points as dual indices is obtained.

[0058] The expression for estimating the signal-to-noise ratio in step S31 is:

[0059] in, Candidate installation points To the sampling point Estimated signal-to-noise ratio of the link; This refers to the UWB radar transmit power. For the transmitting antenna in the transmission direction The radiation pattern gain on the top; For the receiving antenna in the receiving direction The radiation pattern gain on the top; This represents the penetration loss of the corresponding link in the static structure occlusion loss matrix; The expected loss due to dynamic occlusion of the corresponding link; This refers to the free space path loss of the link; The receiver noise figure; For thermal noise power, given by Boltzmann constant System operating temperature With receiver bandwidth A joint decision.

[0060] S32. Substitute the estimated signal-to-noise ratio, UWB center frequency, and pulse accumulation number into the Cramer-Rao lower bound formula to calculate the theoretical minimum resolvable displacement of the sampling points corresponding to multiple candidate links.

[0061] Furthermore, the Cramer-Rao lower bound is a theoretical tool in statistical estimation theory used to describe the lower bound of the estimation variance that any unbiased estimator can reach. In the application scenario of this invention, the Cramer-Rao lower bound is used to calculate the theoretical minimum resolvable displacement of the occupant's chest cavity by UWB radar under the estimated signal-to-noise ratio of the current candidate link, that is, the minimum displacement amplitude that the link can resolve under the optimal estimation condition.

[0062] Specifically, this invention substitutes the estimated signal-to-noise ratio obtained in S31, the center frequency of the UWB radar, and the pulse accumulation number into the Cramer-Rhodes lower bound formula to calculate the theoretical minimum resolvable displacement of the sampling points corresponding to each candidate link. The UWB center frequency determines the phase sensitivity of the radar ranging; a higher center frequency results in a greater phase change per unit displacement and a stronger theoretical resolution. The pulse accumulation number refers to the total number of pulses transmitted and received by the radar for the same target within a detection cycle; a larger accumulation number is equivalent to averaging the signal more times, resulting in a higher equivalent signal-to-noise ratio and a smaller theoretical minimum resolvable displacement. These three input quantities together determine the theoretical detection limit of the radar for small displacements under the current link conditions. The above calculations are performed on all candidate links one by one to obtain the theoretical minimum resolvable displacement dataset for each link.

[0063] The expression for the theoretical minimum resolvable displacement in step S32 is:

[0064] in, Candidate installation points To the sampling point The theoretical minimum resolvable displacement corresponding to the link is the lower limit of the standard deviation of the displacement estimation error determined based on the Cramer-Rao lower bound under the current link signal-to-noise ratio and impulse accumulation conditions. The speed of light; The center frequency of the UWB radar; The estimated signal-to-noise ratio of the corresponding link obtained in step S31 is taken as a linear value here; The pulse accumulation count is the total number of pulses emitted and accumulated by the radar against the same target within one detection cycle.

[0065] S33. Compare the theoretical minimum resolvable displacement with the expected amplitude of the thoracic cavity micro-motion at the corresponding sampling point in the weight field of the whole cabin detection requirement. Retain links whose theoretical minimum resolvable displacement is less than the preset value of the expected amplitude of the thoracic cavity micro-motion. For links that do not meet the conditions, force the corresponding single radar detectability metric to zero and filter to obtain a set of valid links.

[0066] In step S33, the present invention compares the theoretical minimum resolvable displacement of each candidate link obtained in S32 with the expected amplitude of chest cavity micro-motion stored at the corresponding sampling point in the weight field of the whole cabin detection requirements. The expected amplitude of chest cavity micro-motion represents the actual expected chest cavity displacement signal amplitude at the sampling point in a real occupant scenario, and is the physical benchmark for determining whether a candidate link can actually detect vital signs. The specific judgment rule is as follows: if the theoretical minimum resolvable displacement of a candidate link is less than a preset proportion of the expected amplitude of chest cavity micro-motion at the corresponding sampling point (this proportion is 1 / 3, corresponding to a sufficient margin in the required detection signal-to-noise ratio), then the link is determined to theoretically have the ability to detect occupant chest cavity micro-motion, and is retained in the set of valid links; if the theoretical minimum resolvable displacement of a candidate link is greater than or equal to the value corresponding to the preset proportion, then the link is determined to theoretically be unable to distinguish chest cavity micro-motion signals under the current signal-to-noise ratio conditions, and the single radar detectability metric corresponding to the link is forcibly set to 0, and the link will no longer be included in any calculations in subsequent step S4. After completing the above comparison and screening of all candidate links, all retained links are summarized to obtain the set of valid links.

[0067] S4. Based on the set of effective links and the time-varying occlusion map, perform joint coverage optimization calculation on the candidate installation topologies to obtain the set of optimal installation schemes.

[0068] Step S4 further includes: S41. For multiple effective links in the set of effective links, calculate the single radar detectability metric of multiple effective links based on the antenna pattern gain, the blocking correction term corresponding to the time-varying blocking pattern, and the sensitivity of the thoracic cavity micro-motion phase change.

[0069] In step S41, for each link in the effective link set, the present invention first obtains the transmit antenna gain and receive antenna gain in that direction by looking up a table in the antenna pattern data based on the directional angle from the candidate installation point to the corresponding high-weight sampling point. Then, it reads the static structural occlusion loss and dynamic occlusion expected loss of the link from the time-varying occlusion spectrum, adds the two types of losses together with the antenna pattern gain and substitutes them into the link budget to obtain the signal-to-noise ratio of the link after complete occlusion correction.

[0070] Subsequently, this invention calculates the sensitivity of the thoracic cavity micro-motion phase change, aiming to characterize the amplitude of the signal phase change caused by a unit displacement of the occupant's thoracic cavity in the radar observation direction. This is determined by the cosine of the angle between the UWB signal wavelength, the thoracic cavity surface normal vector, and the radar observation direction. A larger cosine value indicates a more significant projection of the thoracic cavity micro-motion in the observation direction, resulting in higher phase change sensitivity. Specifically, this invention inputs the signal-to-noise ratio (SNR) and the thoracic cavity micro-motion phase change sensitivity into the calculation function of the single radar detectability metric. Links that simultaneously satisfy both an SNR exceeding the detection threshold and a thoracic cavity micro-motion phase change sensitivity exceeding the phase detection threshold have a positive single radar detectability metric output; otherwise, the output is zero. After completing the above calculations for all links in the effective link set, a single radar detectability metric dataset is obtained, indexed by candidate installation points and high-weight sampling points.

[0071] The expression for the single radar detectability measure in step S41 is:

[0072] in, Candidate installation points For sampling points The single radar detectability metric, with a value of 0 or 1; This is an indicator function; it takes the value 1 if the condition inside the parentheses is true, and 0 otherwise. The estimated signal-to-noise ratio of the corresponding link obtained in step S31; The signal-to-noise ratio detection threshold is determined by the radar system calibration. Sampling points The occupant's chest cavity was slightly displaced at the candidate installation point. The sensitivity to phase changes caused by the observation direction is determined by both the UWB signal wavelength and the projection coefficient of the thoracic cavity micromotion, and is expressed as follows:

[0073] in, The wavelength of the UWB signal; The sampling points obtained in step S13 Expected amplitude of pleural micromovement at the location; Sampling points Normal vector of occupant's thoracic cavity surface and candidate mounting point The cosine of the angle between the observation directions; This is the threshold for phase detection sensitivity.

[0074] S42. For multiple link pairs in the coherent synthesis candidate link pair set relative to the same sampling point, verify the CRLB screening condition, the matching constraint between the relative propagation delay difference and the UWB range resolution, and the effective aperture range condition of the equivalent baseline direction angle in sequence. For link pairs that simultaneously meet the conditions, coherently synthesize the signal-to-noise ratio of each single radar by maximum ratio combining to obtain the coherent synthesis signal-to-noise ratio and the virtual aperture expansion gain.

[0075] In step S42, the expression for the coherent combining signal-to-noise ratio is:

[0076] in, For installation point and For sampling points The coherent synthesis signal-to-noise ratio, For installation point For sampling points The single radar signal-to-noise ratio, For installation point For sampling points The signal-to-noise ratio of a single radar; The expression for the virtual aperture expansion gain is:

[0077] in, To extend the gain of the virtual aperture, To activate the total number of radars, Sampling points Dynamic detection weight coefficient at the location, Sampling points The signal-to-noise ratio of coherent synthesis at the location.

[0078] Furthermore, the basic principle of virtual aperture expansion is that when two or more radars have effective propagation paths to the same target, coherent synthesis of the two signals after phase alignment is equivalent to forming a virtual antenna array with a larger aperture, thereby obtaining a higher signal-to-noise ratio than when each radar operates independently.

[0079] Based on the above parameters and principles, this invention performs three condition verifications on each of the coherent synthesis candidate link pairs stored in the time-varying occlusion map. The first verification is the CRLB screening condition, meaning that both links in the link pair have passed the screening in step S3 and belong to the valid link set. The second verification is the matching constraint between the relative propagation delay difference and the UWB range resolution. The UWB range resolution is determined by the radar signal bandwidth; the larger the bandwidth, the higher the range resolution. The relative propagation delay difference must be less than half the delay corresponding to the range resolution, meaning the two signals can be aligned and superimposed in the time domain without range aliasing. The third verification is the effective aperture range condition of the equivalent baseline direction angle. The equivalent baseline refers to the line connecting two installation points. The direction and length of the equivalent baseline jointly determine the angular resolution of the virtual aperture. The baseline direction angle must fall within the effective virtual aperture coverage area, meaning the baseline length is within a reasonable range relative to the UWB wavelength. If it is too short, the virtual aperture gain is insignificant; if it is too long, the phase becomes blurred.

[0080] For link pairs that simultaneously meet the above three conditions, this invention employs the maximum ratio combining method to coherently synthesize the single radar signal-to-noise ratios (SNRs) of the two signals. The maximum ratio combining process involves weighting the two signals by the square root of their respective SNRs, summing the results, and then squaring the sum to obtain the coherent synthesized SNR. Compared to the sum of the SNRs of the two signals operating independently, the coherent synthesized SNR additionally includes a cross-correlation term between the two signals; this cross-correlation term is the source of the SNR gain in coherent synthesis. Subsequently, using the dynamic detection weight coefficients of each sampling point in the full-cabin detection demand weight field as weights, the difference between the coherent synthesized SNR and the individual radar SNRs at all sampling points that meet the coherent synthesis conditions is weighted and summed, then normalized by the total number of activated radars to obtain the virtual aperture expansion gain.

[0081] For sampling points that do not meet any of the three conditions, they are degraded to an incoherent independent coverage mode. The joint detectability metric is calculated in the form of a probabilistic complementary product of the detectability metrics of each individual radar. The probabilistic complementary product of the joint detectability metric means that the undetected probability of each individual radar for that sampling point is multiplied together, and then the product is subtracted by one to obtain the joint probability of successful detection by at least one radar. This is used to quantify the joint coverage capability of the sampling point by multiple radars incoherently cooperating.

[0082] S43. Using the minimization of the total number of activated radars, the maximization of joint coverage in high-weight areas, and the maximization of virtual aperture extension gain as multi-objective optimization functions, and constrained by the feasible region of mechanical installation, the lower limit of coverage, and the upper limit of the non-coherent coverage overlap ratio, a genetic algorithm is used to perform discrete topology search on the candidate installation point set. Then, the elevation angle and azimuth angle of multiple determined installation points are continuously optimized by gradient descent until convergence, thus obtaining the optimal installation scheme set.

[0083] In step S43, the expression for the fitness function of the genetic algorithm is:

[0084] in, For the fitness function value, , , These are the trade-off coefficients for coverage integrity, virtual aperture extension gain, and radar quantity penalty, respectively. Sampling points Dynamic detection weight coefficient at the location, Sampling points Joint detectability measure at location, To cover the judgment threshold, To extend the gain of the virtual aperture, For the first The activation status of each candidate installation point This is the sampling point index value. This is the index value for the candidate installation point.

[0085] Furthermore, the execution flow of the genetic algorithm is as follows: First, several sets of candidate solutions are randomly generated to form an initial population, with each set of candidate solutions representing a combination of activation states of a candidate installation point; then, the fitness function value is calculated for each set of candidate solutions, with a higher fitness function value indicating a better solution; according to the fitness function value, selection, crossover, and mutation operations are performed on the candidate solutions in the population to generate a new generation population; the fitness function value is repeatedly calculated and selection, crossover, and mutation are performed on the new generation population, and the population is continuously updated until the fitness function value converges.

[0086] Furthermore, the fitness function of this invention consists of three terms: the first term is a weighted sum of the number of sampling points whose joint detectability metric exceeds the coverage determination threshold, calculated using the dynamic detection weight coefficients of each sampling point. This yields the coverage integrity term, where the coverage determination threshold is the minimum requirement value of the joint detectability metric for determining whether a sampling point is effectively covered. The second term is the virtual aperture extension gain, reflecting the synergistic benefits of the current installation topology in the coherent synthesis dimension. The third term is a negative value of the total number of active radars, serving as a cost penalty to suppress the tendency to infinitely increase the number of radars. These three terms are weighted and superimposed using trade-off coefficients to form the complete fitness function.

[0087] During the search, this invention uses a genetic algorithm to perform a discrete search in the combination space of the activation states of candidate installation points. After determining the topology of the installation points, for each determined active installation point, this invention further uses gradient descent to continuously optimize its pitch and azimuth angles in a continuous space. The optimization objective of gradient descent is the weighted integral of the single radar detectability metric of the installation point with respect to each high-weight sampling point in the sub-region it is responsible for covering. In each iteration, the angle parameters are updated along the direction of the partial derivative of the single radar detectability metric with respect to the pitch and azimuth angles until the change in the angle parameters is lower than the convergence threshold.

[0088] The genetic algorithm performs topological discrete search and gradient descent attitude optimization alternately until the fitness function value changes less than the convergence threshold over multiple generations. The output is a set of optimal installation schemes, which includes the precise spatial coordinates, pitch and azimuth angles, working mode label (coherent synthetic activation or incoherent independent) of each activated installation point, and a coverage division map.

[0089] S5. After completing the initial installation using the optimal installation scheme set, residual identification is performed on the measured data and predicted values ​​of the reference target to obtain the installation correction instruction set.

[0090] Step S5 further includes: S51. The preset standard reference targets for vibration frequency and displacement amplitude are placed sequentially at multiple high-weight sampling points. For the sampling points marked as coherent synthesis activation in the optimal installation scheme set, the echo signals are coherently synthesized by synchronous transmission of orthogonal code division waveform and matched filtering separation, and the measured coherent synthesis signal-to-noise ratio is collected. The measured signal-to-noise ratio is collected for the remaining sampling points.

[0091] In step S51, the present invention places the standard reference target sequentially at each high-weight sampling point marked in the weight field of the full-cabin detection requirements. For the sampling points marked as coherent synthesis activation in the optimal installation scheme set, the corresponding radars are synchronously triggered to transmit orthogonal code division waveforms according to the orthogonal code division waveform scheme determined in step S42. Orthogonal code division waveforms refer to the spread spectrum waveforms transmitted by different radars that are orthogonal to each other in the code domain. The receiver separates the echo signals of different radars in the code domain by performing matched filtering processing with each transmitted code pattern. Then, the phase compensation alignment and amplitude weighted synthesis of each separated signal are performed to obtain the measured coherent synthesis signal-to-noise ratio of the sampling point.

[0092] In addition, for sampling points in the optimal installation scheme set that are not marked as coherent synthesis activation, each corresponding radar independently transmits and receives, and directly reads the signal-to-noise ratio of the echo signal to obtain the measured signal-to-noise ratio of the sampling point.

[0093] S52. Subtract the measured signal-to-noise ratio from the estimated signal-to-noise ratio to obtain the coverage residual; subtract the measured coherent synthesis signal-to-noise ratio from the virtual aperture expansion gain prediction value to obtain the coherent gain residual.

[0094] In step S52, the present invention subtracts the measured signal-to-noise ratio at each high-weight sampling point collected in S51 from the estimated signal-to-noise ratio of the corresponding link obtained by the link budget calculation in step S31 point by point to obtain the coverage residual of each sampling point. The meaning of the obtained coverage residual is the deviation between the signal strength measured after actual installation and the signal strength predicted based on the occlusion map in the planning stage.

[0095] Subsequently, the present invention subtracts the measured coherent synthesis signal-to-noise ratio at each coherent synthesis activation sampling point collected in S51 from the virtual aperture expansion gain prediction value of the corresponding sampling point calculated in step S42 point by point to obtain the coherent gain residual of each sampling point. The obtained coherent gain residual reflects the coherent synthesis signal-to-noise ratio loss caused by phase synchronization error or propagation parameter estimation deviation in the actual coherent synthesis process.

[0096] After performing the above two types of residual calculations on all high-weight sampling points, the covered residual dataset and the coherent gain residual dataset are obtained. Both are stored with the spatial coordinates of the high-weight sampling points as the index.

[0097] S53. Perform correlation analysis on the spatial distribution characteristics of the coverage residual and the coherent gain residual to determine the error source type and obtain the error cause classification results.

[0098] The error source type determination rules in step S53 are as follows: if the coverage residual shows a global same sign offset, it is determined to be an installation height deviation or gain calibration deviation; if the coverage residual shows a unilateral gradient distribution, it is determined to be an antenna attitude angle deviation; if the coverage residual is concentrated in a local area and the corresponding phase residual is amplified, it is determined to be a local multipath cancellation; if the coherent gain residual is greater than the convergence threshold, it is determined to be a phase synchronization delay deviation.

[0099] In step S53, the present invention aims to perform correlation analysis on the spatial distribution of the coverage residual dataset across all high-weight sampling points. Specifically, the present invention first calculates the mean and spatial gradient of the coverage residuals at all sampling points. The mean reflects the global offset of the residuals, and the spatial gradient reflects the monotonic variation trend of the residuals from one side of the cabin to the other.

[0100] Subsequently, this invention outputs the error cause classification results according to the following judgment rules: If the coverage residuals of all sampling points have the same sign and similar amplitude, exhibiting a global same-sign offset, then the error source is determined to be installation height deviation or antenna absolute gain calibration deviation. These two types of errors will have a uniform impact on signal strength in all directions, leading to a global systematic offset. If the coverage residuals show a monotonically changing gradient distribution from sampling points on one side of the cabin to sampling points on the other side, then the error source is determined to be installation deviation of the antenna elevation angle or azimuth angle. Installation angle deviation will cause the overall antenna pattern to deflect, resulting in a deflection direction. The signal strength at one sampling point increases while the signal strength at the other side decreases, forming a unilateral gradient distribution. If the coverage residual is concentrated at several adjacent sampling points in a local area and the corresponding phase residual is significantly larger, it is determined that there is multipath cancellation in the local area. Multipath cancellation refers to the phase cancellation of signals from different propagation paths, resulting in an abnormally low signal strength in the local area. If the coherent gain residual exceeds the preset convergence threshold, it is determined that there is a phase synchronization delay deviation, that is, the actual delay difference between the two coherent synthesized signals deviates from the predicted value recorded in step S23, resulting in inaccurate phase alignment and a decrease in coherent synthesis gain.

[0101] After completing the above analysis for all high-weighted sampling points, the error cause classification results are output, and finally the judgment conclusion for each type of error and the corresponding set of affected sampling points are recorded.

[0102] S54. Based on the error cause classification results, directly output the corresponding compensation amount for the installation height deviation or gain calibration deviation to obtain the installation correction instruction set.

[0103] In step S54, based on the error cause classification results output in S53, the present invention calculates the corresponding correction amount for different error types. Specifically, for cases determined to be installation height deviation or gain calibration deviation, since the mean of the global coverage residual directly reflects the systematic deviation of height or gain, the present invention converts the mean of the coverage residual into the corresponding height correction amount or gain compensation amount, and directly outputs it as a correction command. For cases determined to be antenna attitude angle deviation, the present invention constructs a Jacobian matrix of antenna pattern gain with respect to installation elevation and azimuth angles. The Jacobian matrix is ​​a matrix describing the partial derivatives of the output quantity with respect to each input parameter. Each element is the partial derivative value of the antenna pattern gain with respect to the elevation or azimuth angle at the current installation angle. By performing least squares on the row vectors of the Jacobian matrix corresponding to the coverage residual vector in the direction of each sampling point, that is, multiplying the pseudo-inverse of the Jacobian matrix with the coverage residual vector, the elevation and azimuth correction amounts that minimize the overall residual are obtained. For cases determined to be local multipath cancellation, this invention triggers an update of the multipath path parameters for the corresponding local region in the time-varying occlusion map. The coverage residual and phase residual of this local region are used as new observations to correct the path gain and additional phase of the corresponding link in the original time-varying occlusion map. Then, steps S3 to S4 are re-executed to optimize the local region using the updated time-varying occlusion map. For cases determined to be phase synchronization delay deviation, the coherent gain residual is substituted into the delay error estimation formula to calculate the actual delay deviation, outputting the delay compensation correction amount, and triggering an update of the relative phase difference data for the corresponding link pair in the time-varying occlusion map. Finally, all the above types of correction amounts are sorted from high to low according to the dynamic detection weight coefficient and then output to obtain the installation correction instruction set.

[0104] like Figure 2 As shown, the present invention also provides an in-cabin installation planning system for UWB life sign radar, comprising: Weighting module 100: Used to perform probability weighting on multiple discrete spatial sampling points inside the cabin based on cabin structural parameters and crew behavior data, to obtain the weight field of the whole cabin detection requirements; Propagation module 200: Based on the weight field of the full cabin detection requirements, it performs multipath propagation calculations on candidate links from multiple candidate installation points to multiple high-weight sampling points to obtain a time-varying occlusion map; Calculation module 300: Based on the time-varying occlusion map and UWB radar physical parameters, it performs theoretical detection limit calculations on multiple candidate links and obtains a set of effective links by using the CRLB screening criterion; Optimization module 400: used to perform joint coverage optimization calculation on candidate installation topologies based on the effective link set and the time-varying occlusion map, to obtain the optimal installation scheme set; Output module 500: After the initial installation is completed using the optimal installation scheme set, residual identification is performed on the measured data and predicted values ​​of the reference target to obtain the installation correction instruction set.

[0105] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0106] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the in-cabin installation planning method for UWB vital signs radar described in various embodiments or some parts of embodiments.

[0107] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for planning the in-cabin installation of a UWB life sign radar, characterized in that, include: S1. Based on the cabin structure parameters and crew behavior data, multiple discrete spatial sampling points inside the cabin are subjected to probability weighting to obtain the full cabin detection requirement weight field. S2. Based on the weight field of the full cabin detection requirements, multipath propagation calculation is performed on the candidate links from multiple candidate installation points to multiple high-weight sampling points to obtain a time-varying occlusion map. S3. Based on the time-varying occlusion map and UWB radar physical parameters, perform theoretical detection limit calculations on multiple candidate links, and obtain a set of effective links by using the CRLB screening criterion. S4. Based on the effective link set and the time-varying occlusion map, perform joint coverage optimization calculation on the candidate installation topology to obtain the optimal installation scheme set; S5. After completing the initial installation using the optimal installation scheme set, residual identification is performed on the measured data and predicted values ​​of the reference target to obtain the installation correction instruction set.

2. The in-cabin installation planning method for a UWB life sign radar according to claim 1, characterized in that, Step S1 further includes: S11. Collect the three-dimensional structural parameters of the cabin, the occupant behavior sample data and the human factors engineering constraint parameters, and statistically model the occupant's attitude distribution at multiple spatial sampling points to obtain the occupant attitude probability distribution function. S12. Based on the occupant posture probability distribution function, calculate the maximum value of the cosine of the angle between the occupant's chest cavity surface normal vector and multiple candidate observation directions, and perform a probability-weighted integral on the maximum value of the cosine of the angle to obtain the dynamic detection weight coefficients of multiple spatial sampling points. S13. Using the occupant posture probability distribution function as the weight, perform weighted statistics on the respiratory and cardiac displacement amplitudes at multiple spatial sampling points to obtain the expected amplitude of chest cavity micro-movement at multiple spatial sampling points. S14. Organize and store the dynamic detection weight coefficients and the expected amplitude of the thoracic cavity micro-movement in a three-dimensional voxel grid format to obtain the full cabin detection requirement weight field.

3. The in-cabin installation planning method for a UWB life sign radar according to claim 1, characterized in that, Step S2 further includes: S21. Assign an equivalent attenuation coefficient within the UWB working frequency band to multiple material nodes in the three-dimensional structural model of the cabin. Based on the equivalent attenuation coefficient, calculate the penetration loss accumulation of the propagation path from multiple candidate installation points to multiple high-weight sampling points to obtain the static structural shading loss matrix. S22. Based on the occupant attitude probability distribution function, the additional occupant shading loss of multiple candidate links under various typical attitudes is summed by probability weighting to obtain the dynamic shading expected loss. S23. Perform path tracing on the single reflection path from multiple candidate installation points to the high-weight sampling point, and record the path gain and additional phase of multiple reflection paths; for the case where two points in a candidate installation point pair have effective propagation paths relative to the same sampling point, record the relative propagation delay difference and relative phase difference between the two paths, and mark them as a set of coherent synthesis candidate link pairs. S24. The static occlusion loss matrix, the dynamic occlusion expected loss, the path gain, the additional phase, the relative propagation delay difference, and the relative phase difference are organized and stored in a sparse tensor format to obtain a time-varying occlusion map.

4. The in-cabin installation planning method for a UWB life sign radar according to claim 1, characterized in that, Step S3 further includes: S31. Based on the sum of the static structural blocking loss matrix and the dynamic blocking expected loss in the time-varying blocking spectrum, and combined with the transmit power, antenna pattern gain and noise figure of the UWB radar, link budget calculation is performed on multiple candidate links to obtain the estimated signal-to-noise ratio of multiple candidate links. S32. Substitute the estimated signal-to-noise ratio, UWB center frequency and pulse accumulation number into the Cramer-Rao lower bound formula to calculate the theoretical minimum resolvable displacement of the sampling points corresponding to multiple candidate links. S33. Compare the theoretical minimum resolvable displacement with the expected amplitude of the thoracic cavity micro-motion at the corresponding sampling point in the weight field of the whole cabin detection requirement. Retain links whose theoretical minimum resolvable displacement is less than the preset value of the expected amplitude of the thoracic cavity micro-motion. For links that do not meet the conditions, force the corresponding single radar detectability metric to zero and filter to obtain a set of valid links.

5. The in-cabin installation planning method for a UWB life sign radar according to claim 1, characterized in that, Step S4 further includes: S41. For multiple effective links in the set of effective links, calculate the single radar detectability metric of multiple effective links based on the antenna pattern gain, the blocking correction term corresponding to the time-varying blocking pattern, and the sensitivity of the thoracic cavity micro-motion phase change. S42. For multiple link pairs in the coherent synthesis candidate link pair set relative to the same sampling point, the CRLB screening condition, the matching constraint between the relative propagation delay difference and the UWB range resolution, and the effective aperture range condition of the equivalent baseline direction angle are verified in sequence. For link pairs that simultaneously meet the conditions, the signal-to-noise ratio of each single radar is coherently synthesized by the maximum ratio merging to obtain the coherent synthesis signal-to-noise ratio and the virtual aperture expansion gain. S43. Using the minimization of the total number of activated radars, the maximization of joint coverage in high-weight areas, and the maximization of virtual aperture extension gain as multi-objective optimization functions, and constrained by the feasible region of mechanical installation, the lower limit of coverage, and the upper limit of the non-coherent coverage overlap ratio, a genetic algorithm is used to perform discrete topology search on the candidate installation point set. Then, the elevation angle and azimuth angle of multiple determined installation points are continuously optimized by gradient descent until convergence, thus obtaining the optimal installation scheme set.

6. The in-cabin installation planning method for a UWB life sign radar according to claim 5, characterized in that, In step S42, the expression for the coherent combining signal-to-noise ratio is: in, For installation point and For sampling points The coherent synthesis signal-to-noise ratio, For installation point For sampling points The single radar signal-to-noise ratio, For installation point For sampling points The signal-to-noise ratio of a single radar; The expression for the virtual aperture expansion gain is: in, To extend the gain of the virtual aperture, To activate the total number of radars, Sampling points Dynamic detection weight coefficient at the location, Sampling points The signal-to-noise ratio of coherent synthesis at the location.

7. The in-cabin installation planning method for a UWB life sign radar according to claim 5, characterized in that, In step S43, the expression for the fitness function of the genetic algorithm is: in, For the fitness function value, , , These are the trade-off coefficients for coverage integrity, virtual aperture extension gain, and radar quantity penalty, respectively. Sampling points Dynamic detection weight coefficient at the location, Sampling points Joint detectability measure at location, To cover the judgment threshold, To extend the gain of the virtual aperture, For the first The activation status of each candidate installation point This is the sampling point index value. This is the index value for the candidate installation point.

8. The in-cabin installation planning method for a UWB life sign radar according to claim 1, characterized in that, Step S5 further includes: S51. Place the preset standard reference targets of vibration frequency and displacement amplitude at multiple high-weight sampling points in sequence. For the sampling points marked as coherent synthesis activation in the optimal installation scheme set, coherently synthesize the echo signals by synchronous transmission of orthogonal code division waveforms and matched filtering separation, and collect the measured coherent synthesis signal-to-noise ratio. Collect the measured signal-to-noise ratio for the remaining sampling points. S52. Subtract the measured signal-to-noise ratio from the estimated signal-to-noise ratio to obtain the coverage residual; subtract the measured coherent synthesis signal-to-noise ratio from the virtual aperture expansion gain prediction value to obtain the coherent gain residual. S53. Perform correlation analysis on the spatial distribution characteristics of the coverage residual and the coherent gain residual to determine the error source type and obtain the error cause classification results. S54. Based on the error cause classification results, directly output the corresponding compensation amount for the installation height deviation or gain calibration deviation to obtain the installation correction instruction set.

9. The in-cabin installation planning method for a UWB life sign radar according to claim 8, characterized in that, The rule for determining the error source type in step S53 is as follows: The coverage residual shows a global same-sign offset, which is determined to be either installation height deviation or gain calibration deviation. The coverage residual exhibits a unilateral gradient distribution, which is identified as an antenna attitude angle deviation. The coverage residual is concentrated in a local area and the corresponding phase residual is amplified, which is determined to be local multipath cancellation; If the coherent gain residual is greater than the convergence threshold, it is determined to be a phase synchronization delay deviation.

10. An in-cabin installation planning system for UWB life sign radar, used to execute an in-cabin installation planning method for UWB life sign radar as described in any one of claims 1 to 9, characterized in that, include: Weighting module: used to perform probability weighting on multiple discrete spatial sampling points inside the cabin based on cabin structural parameters and crew behavior data, to obtain the weight field of the whole cabin detection requirements; Propagation module: used to perform multipath propagation calculations on candidate links from multiple candidate installation points to multiple high-weight sampling points based on the weight field of the full cabin detection requirements, and obtain time-varying occlusion maps; Calculation module: used to perform theoretical detection limit calculations on multiple candidate links based on the time-varying occlusion map and UWB radar physical parameters, and to obtain a set of effective links by using the CRLB screening criterion; Optimization module: used to perform joint coverage optimization calculation on candidate installation topologies based on the set of effective links and the time-varying occlusion map, to obtain the set of optimal installation schemes; Output module: After the initial installation is completed using the optimal installation scheme set, residual identification is performed on the measured data and predicted values ​​of the reference target to obtain the installation correction instruction set.

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