Target Motion Analysis Method, Apparatus, Device and Storage Medium for Suppressing Outliers

Abstract

The present application provides a target motion analysis method, apparatus, device, and storage medium for suppressing outliers. The method includes: initializing the model parameters and covariance matrix of the target motion model; calculating the latest output vector according to the latest input vector and the target motion model, and calculating the latest residual vector according to the latest output vector and the latest observation vector; calculating the latest adjustment coefficient according to the norm of the latest residual vector and a preset penalty function, where the preset penalty function is used to suppress the growth rate of the latest adjustment coefficient with respect to the norm of the latest residual vector when the norm of the latest residual vector is greater than a preset threshold; updating the model parameters and covariance matrix based on an improved RLS algorithm, where in the improved RLS algorithm, when calculating the latest gain vector, the covariance matrix before update is replaced with its scalar multiplication matrix with the latest adjustment coefficient. Through the present application, the accuracy and stability of model parameter update in target tracking by the recursive least squares method can be improved.

Description

Technical Field

[0001] This application relates to the technical field of target tracking, and particularly to a target motion analysis method, device, equipment, and storage medium for suppressing outliers. Background Art

[0002] Target tracking has wide applications in many fields such as intelligent monitoring, unmanned driving, aerospace, etc. The recursive least squares method (RLS), as a commonly used parameter estimation method, has received much attention and application in the field of target tracking because it can update model parameters online in real time. It continuously updates the model parameters by using new observation vectors to adapt to the changes in the target motion state.

[0003] However, in actual target tracking scenarios, outliers often inevitably exist in the observation vectors. These outliers may be caused by various factors such as sensor failures, external interferences, and atypical motions of the target. In the prior art, the recursive least squares method is relatively sensitive to outliers in the observation vectors when updating model parameters. Due to its principle of minimizing the sum of squared residuals, outliers will produce large residual contributions in the calculation, which will in turn cause great interference to the model parameter update process. This seriously affects the accuracy and stability of the model parameter update, resulting in a decrease in target tracking accuracy and even possible target loss. Therefore, there is an urgent need for a method that can effectively suppress the influence of outliers in the observation vectors to improve the accuracy and stability of model parameter update in target tracking using the recursive least squares method. Summary of the Invention

[0004] This application provides a target motion analysis method, device, equipment, and storage medium for suppressing outliers, which can solve the technical problem that outliers in the prior art seriously affect the accuracy and stability of model parameter update.

[0005] In a first aspect, an embodiment of this application provides a target motion analysis method for suppressing outliers, and the target motion analysis method includes:

[0006] Initialize the model parameters and covariance matrix of the target motion model;

[0007] Calculate the latest output vector according to the latest input vector and the target motion model, and calculate the latest residual vector according to the latest output vector and the latest observation vector;

[0008] Calculate the latest adjustment coefficient according to the norm of the latest residual vector and a preset penalty function, where the preset penalty function is used to suppress the growth rate of the latest adjustment coefficient with the norm of the latest residual vector when the norm of the latest residual vector is greater than a preset threshold;

[0009] Based on the improved RLS algorithm, update the model parameters and covariance matrix according to the latest adjustment coefficient, the latest input vector, and the latest residual vector. Among them, when calculating the latest gain vector in the improved RLS algorithm, replace the covariance matrix before update with the adjusted covariance matrix, and the adjusted covariance matrix is the product matrix of the covariance matrix before update and the latest adjustment coefficient.

[0010] Further, in one embodiment, the preset penalty function is:

[0011]

[0012] Wherein, is the latest adjustment coefficient, r is the norm of the latest residual vector, is the preset threshold.

[0013] Further, in one embodiment, the preset penalty function is:

[0014]

[0015] Wherein, is the latest adjustment coefficient, r is the norm of the latest residual vector, is the preset threshold.

[0016] Further, in one embodiment, the preset penalty function is:

[0017]

[0018] Wherein, is the latest adjustment coefficient, r is the norm of the latest residual vector, is the preset threshold, is the adjustment parameter, , is the shape parameter, .

[0019] Further, in one embodiment, the preset penalty function is:

[0020]

[0021]

[0022]

[0023] Wherein, is the latest adjustment coefficient, r is the norm of the latest residual vector, is the preset threshold, is the adjustment parameter, , is the shape parameter, .

[0024] Further, in one embodiment, the preset penalty function is:

[0025]

[0026]

[0027]

[0028] where is the latest adjustment coefficient, r is the norm of the latest residual vector, is the preset threshold, is the adjustment parameter, , is the shape parameter, .

[0029] Further, in one embodiment, the steps of updating the model parameters and covariance matrix according to the latest adjustment coefficient, the latest input vector, and the latest residual vector based on the improved RLS algorithm include:

[0030] Calculating the latest gain vector according to the first formula, and the first formula is:

[0031]

[0032] where is the latest gain vector, is the adjusted covariance matrix, is the latest input vector, is the forgetting factor, ;

[0033] Calculating the new model parameters according to the second formula, and the second formula is:

[0034]

[0035] where is the updated model parameter, is the model parameter before update, is the latest observation vector, is the latest output vector, is the latest residual vector;

[0036] Calculating the new model parameters according to the third formula, and the third formula is:

[0037]

[0038] where is the updated covariance matrix, is the covariance matrix before update, and I is the identity matrix.

[0039] In a second aspect, an embodiment of the present application further provides a target motion analysis device for suppressing outliers, where the target motion analysis device includes:

[0040] An initialization module for initializing the model parameters and covariance matrix of the target motion model;

[0041] A prediction update module for calculating a latest output vector according to the latest input vector and the target motion model, and calculating a latest residual vector according to the latest output vector and the latest observation vector;

[0042] An adjustment and suppression module for calculating a latest adjustment coefficient according to the norm of the latest residual vector and a preset penalty function, where the preset penalty function is used to suppress the growth rate of the latest adjustment coefficient with respect to the norm of the latest residual vector when the norm of the latest residual vector is greater than a preset threshold;

[0043] A recursive update module for updating the model parameters and covariance matrix based on an improved RLS algorithm according to the latest adjustment coefficient, the latest input vector, and the latest residual vector, where in the improved RLS algorithm, when calculating the latest gain vector, the covariance matrix before update is replaced with an adjusted covariance matrix, and the adjusted covariance matrix is a multiplication matrix of the covariance matrix before update and the latest adjustment coefficient.

[0044] In a third aspect, an embodiment of the present application further provides a target motion analysis device for suppressing outliers, where the target motion analysis device includes a processor, a memory, and a target motion analysis program stored on the memory and executable by the processor. When the target motion analysis program is executed by the processor, the steps of the above target motion analysis method are implemented.

[0045] In a fourth aspect, an embodiment of the present application further provides a storage medium, on which a target motion analysis program is stored. When the target motion analysis program is executed by a processor, the steps of the above target motion analysis method are implemented.

[0046] In the present application, when the norm of the latest residual vector is greater than a preset threshold, the latest observation vector is considered an outlier. When the norm of the latest residual vector is greater than the preset threshold, the growth rate of the latest adjustment coefficient with respect to the norm of the latest residual vector is suppressed. When calculating the latest gain vector, the covariance matrix before update is replaced with its multiplication matrix with the latest adjustment coefficient, thereby reducing the sensitivity of the latest gain vector to outliers and reducing the interference of outliers on the update of model parameters. Through the present application, the influence of outliers in the observation vector can be effectively suppressed, and the accuracy and stability of model parameter update in target tracking by the recursive least squares method are improved. Description of the Drawings

[0047] Figure 1 Schematic flowchart of the target motion analysis method in an embodiment of the present application;

[0048] Figure 2 Schematic diagram of the functional modules of the target motion analysis device in an embodiment of the present application;

[0049] Figure 3 Schematic diagram of the hardware structure of the target motion analysis device involved in the solution of the embodiment of the present application. Detailed implementation manners

[0050] To enable those skilled in the art to better understand the solution of the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present application.

[0051] First, some technical terms in the present application are explained to facilitate the understanding of the present application by those skilled in the art.

[0052] In the target tracking scenario, an outlier is an observation value in a set of observation values that deviates too much from the average value (for example, exceeding twice the standard deviation range). The reasons for the occurrence of outliers include sensor failures, external interferences, non - typical movements of the target, etc., resulting in the noise probability distribution in the sensor observation values deviating from the Gaussian distribution and showing heavy - tailed characteristics.

[0053] To make the purpose, technical solution and advantages of the present application clearer, the following will further describe the embodiments of the present application in detail with reference to the accompanying drawings.

[0054] In a first aspect, an embodiment of the present application provides a target motion analysis method for suppressing outliers.

[0055] Figure 1 Schematic flowchart of the target motion analysis method in an embodiment of the present application is shown.

[0056] Referring to Figure 1 , in an embodiment, the target motion analysis method includes the following steps:

[0057] S1. Initialize the model parameters and covariance matrix of the target motion model.

[0058] Optionally, the target motion model can be a linear model or a non - linear model.

[0059] Optionally, the model parameters can be initialized to zero vectors or random vectors, and the covariance matrix can be initialized to a large diagonal matrix to represent a large uncertainty in the initial estimate.

[0060] S2. Calculate the latest output vector based on the latest input vector and the target motion model, and calculate the latest residual vector based on the latest output vector and the latest observation vector.

[0061] Exemplarily, is the input vector at time k, is the observation vector at time k, is the output vector at time k, is the residual vector at time k, , is the target motion model, calculate The model parameters used for the calculation are the model parameters updated at time k-1 .

[0062] S3. Calculate the latest adjustment coefficient based on the norm of the latest residual vector and a preset penalty function, where the preset penalty function is used to suppress the growth rate of the latest adjustment coefficient with respect to the norm of the latest residual vector when the norm of the latest residual vector is greater than a preset threshold.

[0063] Specifically, the norm is a way to measure a vector or a matrix. It can map a vector or a matrix to a non-negative real number, which is used to measure the size of the vector or the matrix.

[0064] Optionally, in this embodiment, the norm can be the L1 norm or the L2 norm.

[0065] In this embodiment, it is determined whether the latest observation vector is an outlier by the magnitude relationship between the norm of the latest residual vector and the preset threshold. The preset penalty function is segmented at the preset threshold. When the norm of the latest residual vector is less than or equal to the preset threshold, the latest adjustment coefficient increases as the norm of the latest residual vector increases. When the norm of the latest residual vector is greater than the preset threshold, the latest adjustment coefficient decreases as the norm of the latest residual vector increases, or remains unchanged, or increases as the norm of the latest residual vector increases, but the growth rate is less than the growth rate when the norm of the latest residual vector is less than or equal to the preset threshold.

[0066] S4. Based on the improved RLS algorithm, update the model parameters and the covariance matrix according to the latest adjustment coefficient, the latest input vector, and the latest residual vector. In the improved RLS algorithm, when calculating the latest gain vector, the covariance matrix before the update is replaced with an adjusted covariance matrix, and the adjusted covariance matrix is a product matrix of the covariance matrix before the update and the latest adjustment coefficient.

[0067] Specifically, in the existing RLS algorithm, the update formula is as follows:

[0068]

[0069]

[0070]

[0071] Among them, is the gain vector at time k, is the input vector at time k, is the forgetting factor, , is the covariance matrix updated at time k, is the model parameter updated at time k, is the observation vector at time k, is the output vector at time k, is the residual vector at time k.

[0072] To simplify the description, for time k, is the latest input vector, is the latest observation vector, is the latest output vector, is the latest residual vector, is the latest gain vector, is the covariance matrix before update, is the covariance matrix after update, is the model parameter before update, is the model parameter after update.

[0073] The gain vector is used to determine the influence degree of the observation vector at time k on the parameter update. In the calculation formula, the numerator is used to combine historical experience with current data to preliminarily determine the direction and amplitude of parameter update. In the denominator, is used to control the influence of old data on the current estimate. When , the weights of all data are the same. When , the weights of old data will gradually decrease over time.

[0074] In the denominator, represents the degree of correlation between current data and historical data. When is larger, it indicates that there is a strong correlation between current data and historical data, or in other words, the new information contained in current data is relatively less. At this time, the denominator is larger, and the value of the gain vector will be relatively smaller, meaning that when updating the parameter, the dependence on current data will be reduced, and more reliance will be placed on historical experience. Conversely, when When the value is small, it indicates that the current data brings more new information, the denominator is small, and the gain vector will be relatively large, so that the current data will be more fully utilized during parameter update.

[0075] It can be seen that will affect the degree of dependence on historical experience during the update of control parameters. When calculating the latest gain vector in the improved RLS algorithm proposed in this embodiment, the covariance matrix before update is replaced by , is the latest adjustment coefficient, so as to flexibly control the degree of dependence on historical experience during parameter update according to the latest observed data. When the latest observed data may be an outlier, compared with the existing algorithm, it increases the degree of dependence on historical experience and reduces the interference of outliers on parameter update.

[0076] Specifically, step S4 specifically includes:

[0077] Calculate the latest gain vector according to the first formula, and the first formula is:

[0078]

[0079] where is the latest gain vector, is the adjusted covariance matrix, is the latest input vector, is the forgetting factor, ;

[0080] Calculate the new model parameters according to the second formula, and the second formula is:

[0081]

[0082] where is the updated model parameter, is the model parameter before update, is the latest observed vector, is the latest output vector, is the latest residual vector;

[0083] Calculate the new model parameters according to the third formula, and the third formula is:

[0084]

[0085] where is the updated covariance matrix, is the covariance matrix before update, and I is the identity matrix.

[0086] Thus, in this embodiment, when the norm of the latest residual vector is greater than the preset threshold, the latest observation vector is considered an outlier. When the norm of the latest residual vector is greater than the preset threshold, the growth rate of the latest adjustment coefficient with respect to the norm of the latest residual vector is suppressed. When calculating the latest gain vector, the covariance matrix before update is replaced with its product matrix with the latest adjustment coefficient, thereby reducing the sensitivity of the latest gain vector to outliers and reducing the interference of outliers on the update of model parameters. Through this embodiment, the influence of outliers in the observation vector can be effectively suppressed, and the accuracy and stability of the model parameter update in the recursive least squares method for target tracking are improved.

[0087] Further, in one embodiment, the preset penalty function is:

[0088]

[0089] where is the latest adjustment coefficient, r is the norm of the latest residual vector, is the preset threshold.

[0090] In this embodiment, the preset penalty function adopts the Huber penalty function. The Huber penalty function grows linearly when the error is large and is less sensitive to outliers than the traditional squared error loss function (such as the loss function in the least squares method). It can, to a certain extent, suppress the influence of outliers on the estimation result, making the estimation result more stable and reliable, and having good robustness to outliers.

[0091] Further, in one embodiment, the preset penalty function is:

[0092]

[0093] where is the latest adjustment coefficient, r is the norm of the latest residual vector, is the preset threshold.

[0094] In this embodiment, the preset penalty function adopts the Tukey penalty function. The biggest advantage of the Tukey penalty function is its strong resistance to outliers. In the traditional least squares method, the square of the error will cause outliers to have a greater impact on the result. However, after the residual exceeds the preset threshold, the value of the Tukey penalty function no longer increases, effectively suppressing the interference of outliers and making the estimation result more robust.

[0095] Further, in one embodiment, the preset penalty function is:

[0096]

[0097] where is the latest adjustment coefficient, r is the norm of the latest residual vector, is a preset threshold value, is an adjustment parameter, , is a shape parameter, .

[0098] In this embodiment, the preset penalty function is set based on the Generalized Minimax Concave Penalty (GMCP for short). The GMCP calculation result is added with 1 to obtain the latest adjustment coefficient. GMCP adaptively adjusts the penalty intensity according to the error magnitude in the data. For relatively small errors, it imposes a relatively large penalty, prompting the model to better fit the normal data points; while for relatively large errors, i.e., possible outliers, the penalty intensity increases relatively slowly, avoiding excessive influence of these outliers on the model estimation.

[0099] It is found in the simulation experiment that directly using GMCP as the preset penalty function has poor effects, and after adding 1 to the GMCP calculation result, the effects are significantly improved. After adding 1, it can be regarded as further enhancing the adjustment intensity of parameter estimation on the basis of the original penalty. In this way, it can more significantly affect the gain calculation, making the model more inclined to select parameter values that meet the constraint conditions during the optimization process. In addition, after adding 1, its numerical characteristics can be changed, making the result within a more appropriate range when performing multiplication operations with other terms, ensuring the stability and rationality of the calculation process.

[0100] Furthermore, in one embodiment, the preset penalty function is:

[0101]

[0102]

[0103]

[0104] wherein, is the latest adjustment coefficient, r is the norm of the latest residual vector, is a preset threshold value, is an adjustment parameter, , is a shape parameter, .

[0105] In this embodiment, the preset penalty function is comprehensively set based on the Huber penalty function and GMCP. The Huber penalty function calculation result is multiplied by the sum of the GMCP calculation result and 1, integrating the advantages of the two penalty functions and enhancing the suppression effect on outliers.

[0106] Furthermore, in one embodiment, the preset penalty function is:

[0107]

[0108]

[0109]

[0110] Among them, is the latest adjustment coefficient, r is the norm of the latest residual vector, is the preset threshold, is the adjustment parameter, , is the shape parameter, .

[0111] In this embodiment, the preset penalty function is comprehensively set based on the Tukey penalty function and GMCP. The calculation result of the Tukey penalty function is multiplied by the sum of the calculation result of GMCP and 1, integrating the advantages of the two penalty functions to enhance the suppression effect on outliers.

[0112] In a second aspect, the embodiment of the present application further provides a target motion analysis device for suppressing outliers.

[0113] Figure 2 shows a schematic diagram of the functional modules of the target motion analysis device in an embodiment of the present application.

[0114] Referring to Figure 2 , in an embodiment, the target motion analysis device includes:

[0115] An initialization module 10, configured to initialize the model parameters and covariance matrix of the target motion model;

[0116] A prediction update module 20, configured to calculate the latest output vector according to the latest input vector and the target motion model, and calculate the latest residual vector according to the latest output vector and the latest observation vector;

[0117] An adjustment and suppression module 30, configured to calculate the latest adjustment coefficient according to the norm of the latest residual vector and the preset penalty function, where the preset penalty function is used to suppress the growth rate of the latest adjustment coefficient with the norm of the latest residual vector when the norm of the latest residual vector is greater than the preset threshold;

[0118] A recursive update module 40, configured to update the model parameters and covariance matrix based on the improved RLS algorithm according to the latest adjustment coefficient, the latest input vector, and the latest residual vector, where in the improved RLS algorithm, when calculating the latest gain vector, the covariance matrix before update is replaced with an adjusted covariance matrix, and the adjusted covariance matrix is a scalar multiplication matrix of the covariance matrix before update and the latest adjustment coefficient.

[0119] Further, in one embodiment, the preset penalty function is:

[0120]

[0121] Wherein, is the latest adjustment coefficient, r is the norm of the latest residual vector, is the preset threshold.

[0122] Further, in one embodiment, the preset penalty function is:

[0123]

[0124] Wherein, is the latest adjustment coefficient, r is the norm of the latest residual vector, is the preset threshold.

[0125] Further, in one embodiment, the preset penalty function is:

[0126]

[0127] Wherein, is the latest adjustment coefficient, r is the norm of the latest residual vector, is the preset threshold, is the adjustment parameter, , is the shape parameter, .

[0128] Further, in one embodiment, the preset penalty function is:

[0129]

[0130]

[0131]

[0132] Wherein, is the latest adjustment coefficient, r is the norm of the latest residual vector, is the preset threshold, is the adjustment parameter, , is the shape parameter, .

[0133] Further, in one embodiment, the preset penalty function is:

[0134]

[0135]

[0136]

[0137] Among them, is the latest adjustment coefficient, r is the norm of the latest residual vector, is the preset threshold, is the adjustment parameter, , is the shape parameter, .

[0138] Furthermore, in one embodiment, the recursive update module 40 is used for:

[0139] Calculating the latest gain vector according to the first formula, and the first formula is:

[0140]

[0141] Among them, is the latest gain vector, is the adjusted covariance matrix, is the latest input vector, is the forgetting factor, ;

[0142] Calculating the new model parameter according to the second formula, and the second formula is:

[0143]

[0144] Among them, is the updated model parameter, is the model parameter before update, is the latest observation vector, is the latest output vector, is the latest residual vector;

[0145] Calculating the new model parameter according to the third formula, and the third formula is:

[0146]

[0147] Among them, is the updated covariance matrix, is the covariance matrix before update, and I is the identity matrix.

[0148] Among them, the function implementation of each module in the above target motion analysis device corresponds to each step in the above target motion analysis method embodiment, and its function and implementation process will not be elaborated here one by one.

[0149] In a third aspect, an embodiment of the present application provides a target motion analysis device for suppressing outliers. The target motion analysis device can be a device with data processing functions such as a personal computer (PC), a laptop computer, a server, etc.

[0150] Figure 3 The schematic hardware structure diagram of the target motion analysis device involved in the embodiment solution of the present application is shown.

[0151] Referring to Figure 3 In an embodiment of the present application, the target motion analysis device may include a processor, a memory, a communication interface, and a communication bus.

[0152] Among them, the communication bus can be of any type and is used to interconnect the processor, the memory, and the communication interface.

[0153] The communication interface includes interfaces such as input / output (I / O) interfaces, physical interfaces, and logical interfaces for implementing the interconnection of internal components of the target motion analysis device, as well as interfaces for implementing the interconnection of the target motion analysis device with other devices (such as other computing devices or user devices). The physical interface can be an Ethernet interface, an optical fiber interface, an ATM interface, etc.; the user device can be a display screen (Display), a keyboard (Keyboard), etc.

[0154] The memory can be various types of storage media, such as random access memory (RAM), read-only memory (ROM), non-volatile RAM (NVRAM), flash memory, optical memory, hard disk, programmable ROM (PROM), erasable PROM (EPROM), electrically erasable PROM (EEPROM), etc.

[0155] The processor can be a general-purpose processor. The general-purpose processor can call the target motion analysis program stored in the memory and execute the target motion analysis method provided by the embodiment of the present application. For example, the general-purpose processor can be a central processing unit (CPU). Among them, the method executed when the target motion analysis program is called can refer to the various embodiments of the target motion analysis method of the present application, which will not be elaborated here.

[0156] Those skilled in the art can understand that Figure 3The hardware structure shown does not constitute a limitation on this application. It may include more or fewer components than those shown, or combine certain components, or have different component arrangements.

[0157] Fourthly, an embodiment of this application also provides a storage medium.

[0158] A target motion analysis program is stored on the storage medium of this application. When the target motion analysis program is executed by a processor, the steps of the target motion analysis method as described above are implemented.

[0159] Among them, the method implemented when the target motion analysis program is executed can refer to the various embodiments of the target motion analysis method of this application, which will not be elaborated here.

[0160] It should be noted that the serial numbers of the above embodiments of this application are only for description and do not represent the superiority or inferiority of the embodiments.

[0161] The terms "including" and "having" and any variations thereof in the description of the specification, claims and drawings of this application are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but optionally further includes steps or units not listed, or optionally further includes other steps or units inherent to these processes, methods, products or devices. The descriptions of terms such as "first", "second" and "third" are used to distinguish different objects, etc., and do not represent a sequence, nor do they limit that "first", "second" and "third" are of different types.

[0162] In the description of the embodiments of this application, terms such as "exemplary", "for example" or "for instance" are used to represent examples, illustrations or explanations. Any embodiment or design solution described as "exemplary", "for example" or "for instance" in the embodiments of this application should not be construed as being more preferred or having more advantages than other embodiments or design solutions. Exactly, the use of words such as "exemplary", "for example" or "for instance" is intended to present relevant concepts in a specific manner.

[0163] In the description of the embodiments of this application, unless otherwise specified, " / " means "or". For example, A / B may mean A or B; "and / or" in the text is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B may mean: A exists alone, A and B exist simultaneously, and B exists alone. In addition, in the description of the embodiments of this application, "a plurality of" means two or more than two.

[0164] In some of the processes described in the embodiments of the present application, multiple operations or steps appear in a specific order. However, it should be understood that these operations or steps may not be executed in the order in which they appear in the embodiments of the present application or may be executed in parallel. The serial numbers of the operations are only used to distinguish different operations, and the serial numbers themselves do not represent any order of execution. In addition, these processes may include more or fewer operations, and these operations or steps may be executed in sequence or in parallel, and these operations or steps may be combined.

[0165] Through the description of the above embodiments, those skilled in the art can clearly understand that the above-described embodiment methods can be implemented by means of software plus a necessary general hardware platform. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art can be embodied in the form of a software product. The computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) as described above and includes several instructions for causing a terminal device to execute the methods described in the various embodiments of the present application.

[0166] The above are only the preferred embodiments of the present application, and do not limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made by using the content of the specification and drawings of the present application, or directly or indirectly applied in other related technical fields, shall be equally included in the patent protection scope of the present application.

Claims

1. A target motion analysis method for suppressing outliers, characterized in that, The target motion analysis method includes: Initializing the model parameters and covariance matrix of the target motion model; Calculating the latest output vector according to the latest input vector and the target motion model, and calculating the latest residual vector according to the latest output vector and the latest observation vector; Calculating the latest adjustment coefficient according to the norm of the latest residual vector and a preset penalty function, where the preset penalty function is used to suppress the growth rate of the latest adjustment coefficient with the norm of the latest residual vector when the norm of the latest residual vector is greater than a preset threshold; Based on the improved RLS algorithm, updating the model parameters and covariance matrix according to the latest adjustment coefficient, the latest input vector, and the latest residual vector, where in calculating the latest gain vector, the improved RLS algorithm replaces the covariance matrix before update with an adjusted covariance matrix, and the adjusted covariance matrix is a scalar multiplication matrix of the covariance matrix before update and the latest adjustment coefficient.

2. The target motion analysis method according to claim 1, wherein The preset penalty function is: Among them, is the latest adjustment coefficient, and r is the norm of the latest residual vector, is the preset threshold value.

3. The target motion analysis method according to claim 1, wherein The preset penalty function is: Among them, is the latest adjustment coefficient, and r is the norm of the latest residual vector, is the preset threshold value.

4. The target motion analysis method according to claim 1, wherein The preset penalty function is: Among them, is the latest adjustment coefficient, r is the norm of the latest residual vector, is the preset threshold, is the adjustment parameter, , is the shape parameter, .

5. The target motion analysis method according to claim 1, wherein The preset penalty function is: Among them, is the latest adjustment coefficient, r is the norm of the latest residual vector, is the preset threshold, is the adjustment parameter, , is the shape parameter, .

6. The target motion analysis method according to claim 1, characterized in that The preset penalty function is: Among them, is the latest adjustment coefficient, r is the norm of the latest residual vector, is the preset threshold, is the adjustment parameter, , is the shape parameter, .

7. The target motion analysis method according to any one of claims 1 to 6, characterized in that, The step of updating the model parameters and covariance matrix according to the latest adjustment coefficient, the latest input vector, and the latest residual vector based on the improved RLS algorithm includes: Calculating the latest gain vector according to the first formula, and the first formula is: Among them, is the latest gain vector, is the adjusted covariance matrix, is the latest input vector, is the forgetting factor, ; Calculating the new model parameters according to the second formula, and the second formula is: Among them, is the updated model parameter, is the model parameter before update, is the latest observation vector, is the latest output vector, is the latest residual vector; Calculating the new model parameters according to the third formula, and the third formula is: Among them, is the updated covariance matrix, is the covariance matrix before update, and I is the identity matrix.

8. An object motion analysis device for suppressing outliers, characterized in that, The target motion analysis device includes: An initialization module for initializing the model parameters and covariance matrix of the target motion model; A prediction update module for calculating the latest output vector according to the latest input vector and the target motion model, and calculating the latest residual vector according to the latest output vector and the latest observation vector; An adjustment suppression module for calculating the latest adjustment coefficient according to the norm of the latest residual vector and a preset penalty function, where the preset penalty function is used to suppress the growth rate of the latest adjustment coefficient with the norm of the latest residual vector when the norm of the latest residual vector is greater than a preset threshold; A recursive update module for updating the model parameters and covariance matrix based on the improved RLS algorithm according to the latest adjustment coefficient, the latest input vector, and the latest residual vector, where in calculating the latest gain vector, the improved RLS algorithm replaces the covariance matrix before update with an adjusted covariance matrix, and the adjusted covariance matrix is a scalar multiplication matrix of the covariance matrix before update and the latest adjustment coefficient.

9. An object motion analysis device for suppressing outliers, characterized in that, The target motion analysis device includes a processor, a memory, and a target motion analysis program stored on the memory and executable by the processor. When the target motion analysis program is executed by the processor, the steps of the target motion analysis method described in any one of claims 1 to 7 are implemented.

10. A storage medium, characterized in that, A target motion analysis program is stored on the storage medium. When the target motion analysis program is executed by a processor, the steps of the target motion analysis method described in any one of claims 1 to 7 are implemented.

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