Evaluation Method for Target Position Update Delay

Abstract

The present invention discloses a method for evaluating the delay of target position update, including the evaluation modeling of the delay of target position update and the quantization of the evaluation modeling; the modeling of the delay of target position update defines the delay of target position update as a combined function of hard delay and soft delay, and the mathematical model is as shown in #imgabs0#, where D represents the delay, D h represents the hard delay, and D s represents the soft delay; the hard delay is defined as the delay generated when the position data communicates with the control center or the cloud platform through the physical and / or spatial topology link according to a certain communication protocol; the soft delay is defined as the delay caused by the position update interval at the software algorithm level. The method proposed by the present invention solves the defects that the current delay of position update cannot be quantitatively evaluated or the evaluation is incomplete, significantly improves the feasibility and quantization of the evaluation of the delay of position update in the fields of communication, navigation, etc., and has great theoretical research value and engineering practice significance.

Description

Technical Field

[0001] The present invention belongs to the technical field of navigation and positioning, and particularly relates to a method for evaluating the delay of target position update. Background Art

[0002] As the final output of a navigation and positioning system, position, together with attitude and speed, constitutes an important part of the navigation solution. In various application environments or scenarios of navigation and positioning systems, it is often necessary to evaluate various indicators such as accuracy, delay, and robustness of the target position update information concerned and obtained in the system to evaluate the advantages and disadvantages of different navigation and positioning methods or systems. Here, the application environments or scenarios include, but are not limited to, open outdoor environments such as playgrounds, as well as satellite signal rejection environments such as building-intensive areas, residential buildings, supermarkets, airport halls, waiting rooms, theaters, underground parking lots, forests, canyons, mines, and tunnels; the targets include, but are not limited to, aircraft, ships, vehicles, people, etc.

[0003] Essentially, delay refers to the time difference between receiving information and sending information. Different from accurate and quantifiable accuracy estimation, the current estimation of the target position update delay is either ignored or estimated roughly. However, in some scenarios such as emergency rescue, rapid change of target position, or real-time update of target position information, the estimation of the target position update delay is particularly important. As is well known, the transmission of target position generally follows a push-pull protocol, that is, the control center issues a request command to obtain the position information of the target, or the target uploads the position information to the control center at a predetermined frequency, or a combination of the two methods. The existing methods for estimating or processing the target position update delay are as follows: 1) Ignoring the delay, for static, quasi-static, or low-dynamic targets, ignoring the delay is approximately feasible in engineering; 2) Rough estimation, for dynamic targets, with the help of a high-performance reference system (the performance generally needs to be one order of magnitude higher than the system to be evaluated, and high performance means low delay), at this time, by translating the time stamp, the position output of the system to be evaluated is aligned with the position output of the reference system, and the translation amount of the time stamp is the delay of the position update of the system to be evaluated; however, this method is affected by the position accuracy of the reference system, and the position error of the reference system will directly affect the delay estimation of the system to be evaluated. More importantly, in practice, there is often a lack of a reference system that provides accurate time stamps and high precision, making the quantitative evaluation of the position update delay particularly difficult.

[0004] In summary, the current difficulty in estimating the target position update delay is the lack of a quantifiable evaluation method or incomplete evaluation. To solve the above problems, the present invention proposes a method for evaluating the target position update delay, which is expected to fill the technical gap in this field. Summary of the Invention

[0005] To solve the technical problems proposed in the background art, the object of the present invention is to provide a method for evaluating the delay of target position update.

[0006] To achieve the above object, the technical solution adopted by the present invention is: a method for evaluating the delay of target position update, comprising the following steps:

[0007] Evaluating and modeling the delay of target position update;

[0008] Quantifying the evaluation model;

[0009] The evaluation and modeling of the delay of target position update defines the delay of target position update as a combined function of hard delay and soft delay, and the mathematical model is shown in Equation (1).

[0010]

[0011] Where D represents the delay, D h represents the hard delay, D s represents the soft delay, represents the combined mapping relationship, represents the combined function of hard delay and soft delay;

[0012] The hard delay is defined as the delay generated when the position data communicates with the control center or cloud platform through the physical topology link and / or spatial topology link according to a certain communication protocol; the delay generated by the physical topology link refers to the delay generated when the position data is transmitted by wire, and the wire methods include but are not limited to cables, optical fibers, etc.; the delay generated by the spatial topology link refers to the delay generated when the position data is transmitted wirelessly, including the effects of signal reflection, refraction, diffraction, and multipath effects, and the wireless methods include but are not limited to 3G, 4G, 5G and other networking and communication methods;

[0013] The soft delay is defined as the delay caused by the position update interval at the software algorithm level.

[0014] Further, the quantification of the evaluation model is the concretization of the mathematical model of the delay of target position update, that is, the superposition sum of the mathematical expectations of the hard delay D h and the soft delay D s , as shown in Equation (2).

[0015] D = E[D h + D s = E[D h + E[D s (2).

[0016] Equation (2) realizes the quantification and comprehensive evaluation of the delay index.

[0017] Further, the hard delay is a random variable, which is modeled as a normal distribution with a mean of μ and a variance of σ 2 , as shown in Equation (3),

[0018] D h ~N(μ, σ 2 ) (3)

[0019] That is, the probability density distribution function of the hard delay is as shown in Equation (4).

[0020]

[0021] Furthermore, the mathematical expectation of the random variable D h , as shown in Equation (5),

[0022]

[0023] where E[·] represents the mathematical expectation of the variable ·; Equation (5) shows that the mathematical expectation of D h is the mean μ.

[0024] Further, since it is impossible and unnecessary to obtain an infinite number of measurement values of the hard delay in a specific scenario in practice, it is only necessary to perform multiple measurements on a specific scenario, and then use the maximum likelihood estimation method to calculate the mean μ of the hard delay.

[0025] Further, the soft delay is modeled as a function of the location update frequency at the software algorithm level, as shown in Equation (6) or (7).

[0026]

[0027]

[0028] where F is the location update frequency, g(·) represents the functional relationship, and δ is the update frequency error;

[0029] The update frequency error is affected by the offset of the crystal oscillator frequency and the timer accuracy in the software algorithm, and is usually very small. After ignoring the location update frequency error δ, Equations (6) and (7) are uniformly simplified to Equation (8).

[0030]

[0031] Since the true location update events occur with equal probability within the time interval g(F), the mathematical expectation of the soft delay is as shown in Equation (9).

[0032]

[0033] Further, the update frequency is 0 - 500 Hz.

[0034] Advantages of the present invention: (1) The present invention fully and reasonably defines the position update delay, divides it into hard delay and soft delay, with a simple and clear concept, which deepens people's in-depth understanding and recognition of the delay index; (2) The method proposed by the present invention solves the defects that the current position update delay cannot be quantitatively evaluated or the evaluation is incomplete, significantly improves the feasibility and quantitativeness of the evaluation of the position update delay index in the fields of communication, navigation, etc., and has great theoretical research value and engineering practice significance. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 is a flowchart of the evaluation method for the target position update delay;

[0036] Figure 2 is a schematic diagram of the transmission of the position update information in the entire navigation and positioning system;

[0037] Figure 3 is the evaluation result of the hard delay and soft delay in the playground (open area) scenario;

[0038] Figure 4 is the evaluation result of the hard delay and soft delay in the building-intensive area scenario;

[0039] Figure 5 is the evaluation result of the hard delay and soft delay in the residential building scenario;

[0040] Figure 6 is the evaluation result of the hard delay and soft delay in the supermarket scenario;

[0041] Figure 7 is the evaluation result of the hard delay and soft delay in the airport hall scenario;

[0042] Figure 8 is the evaluation result of the hard delay and soft delay in the waiting room scenario;

[0043] Figure 9 is the evaluation result of the hard delay and soft delay in the theater scenario;

[0044] Figure 10 is the evaluation result of the hard delay and soft delay in the underground parking lot scenario;

[0045] Figure 11 is the evaluation result of the hard delay and soft delay in the forest scenario;

[0046] Figure 12 is the evaluation result of the hard delay and soft delay in the canyon scenario;

[0047] Figure 13 is the evaluation result of the hard delay and soft delay in the mine scenario;

[0048] Figure 14Evaluation results of hard delay and soft delay in tunnel scenarios;

[0049] Figure 15 For the evaluation results of hard delay under different soft delays in the playground (open area) scenario. Detailed implementation

[0050] To better understand the content of the present invention, the following further illustrates the content of the present invention in combination with specific implementation methods, but the protection scope of the present invention is not limited to the following embodiments.

[0051] Embodiment 1

[0052] As Figure 1 shown, a method for evaluating the target position update delay includes the following steps:

[0053] Evaluation modeling of the target position update delay;

[0054] Quantification of the evaluation modeling;

[0055] The evaluation modeling of the target position update delay defines the target position update delay as a combined function of hard delay and soft delay, and the mathematical model is as shown in Equation (1),

[0056]

[0057] where D represents delay, D h represents hard delay, D s represents soft delay, represents the combined mapping relationship, represents the combined function of hard delay and soft delay.

[0058] The hard delay is defined as the delay when the position data communicates with the control center or cloud platform through the physical and spatial topology links according to a certain communication protocol; the physical topology link delay refers to the delay generated when the position data is transmitted by wire; the wire methods include but are not limited to optical cables, cables, etc.; the spatial topology link delay refers to the delay generated when the position data is transmitted wirelessly, including the effects of signal reflection, refraction, diffraction, and multipath in the environment; the wireless methods include but are not limited to: 3G, 4G, 5G and other networking and communication methods.

[0059] The soft delay is defined as the delay caused by the position update interval at the software algorithm level.

[0060] The quantification of the evaluation modeling is the concretization of the mathematical model of the target position update delay, that is, the superposition sum of the mathematical expectations of the hard delay D h and the soft delay D s , as shown in Equation (2),

[0061] D = E[D h + D s = E[D h + E[D s (2).

[0062] The hard delay is a random variable, modeled as a normal distribution with a mean of μ and a variance of σ 2 , as shown in Equation (3),

[0063] D h ~ N(μ, σ 2 ) (3)

[0064] That is, the probability density distribution function of D h is as shown in Equation (4),

[0065]

[0066] The mathematical expectation of the random variable D h can be obtained, as shown in Equation (5),

[0067]

[0068] where E[·] represents the mathematical expectation of the variable ·; Equation (5) shows that the mathematical expectation of D h is the mean μ;

[0069] Similarly, the mathematical expectation of the second moment can be obtained, as shown in Equation (10),

[0070]

[0071] Therefore, according to the variance calculation formula, the variance σ 2 is obtained, as shown in Equation (11),

[0072] σ 2 = E[D h 2 - E[D h 2 (11)

[0073] Since it is impossible and unnecessary to obtain an infinite number of measurement values of the hard delay in a specific scenario in practice, it is only necessary to perform multiple measurements on a specific scenario, and then use the maximum likelihood estimation method to calculate the mean μ and variance σ of the hard delay 2 .

[0074] The soft delay is modeled as a function of the position update frequency at the software algorithm level, as shown in Equation (6) or (7),

[0075]

[0076] ​

[0077] Where F is the position update frequency, g(·) represents the functional relationship, and δ is the update frequency error;

[0078] The update frequency error is affected by the offset of the crystal oscillator frequency and the timer accuracy in the software algorithm, and is usually very small. After ignoring the position update frequency error δ, equations (6) and (7) are uniformly simplified to equation (8) as follows:

[0079]

[0080] Since the real position update events occur with equal probability within the time interval g(F), the mathematical expectation of the soft delay is obtained as shown in equation (9).

[0081]

[0082] The update frequency is 0 - 500 Hz.

[0083] Embodiment 2

[0084] As Figure 2 shown, it represents the transmission schematic diagram of the position update information in the entire navigation and positioning system. The position update information of targets such as aircraft, ships, vehicles, and people is transmitted wirelessly to the cloud and the control center via wireless networks such as 3G / 4G / 5G. Among them, the wireless network and the cloud communicate by wired means such as optical cables and cables, or can also communicate by wireless means; the cloud and the control center generally communicate by wired means such as optical cables and cables; the wireless means here include but are not limited to networking and communication methods such as 3G, 4G, and 5G. By deploying this system to specific application environments such as playgrounds (open areas), building-intensive areas, residential buildings, supermarkets, airport halls, waiting rooms, theaters, underground parking lots, forests, canyons, mines, tunnels, etc., the cloud or the control center can obtain the target position information and conduct the analysis and evaluation of the target position update delay.

[0085] Embodiment 3

[0086] Apply the evaluation method in Embodiment 1 to the following test experiment scenarios: playgrounds (open areas), building-intensive areas, residential buildings, supermarkets, airport halls, waiting rooms, theaters, underground parking lots, forests, canyons, mines, tunnels. Deploy the system in Embodiment 2 in each scenario respectively, and the cloud or the control center obtains the target position information and conducts the analysis and evaluation of the target position update delay. The analysis and evaluation results of the position update delay in each scenario are shown in Embodiments 4 - 15, and the summary results are shown in Embodiment 16.

[0087] Embodiment 4

[0088] As Figure 3As shown, it is the result of evaluating the hard delay and soft delay using the method of the present invention under the playground (open area); in this scenario, the hard delay and soft delay are each measured 100 times to obtain the corresponding test results of the hard delay and soft delay. Among them, since the position update frequency is set to 100 Hz, the mathematical expectation of the soft delay is always 10 milliseconds; the test results of the hard delay show obvious fluctuation characteristics. The normal fitting is performed on the 100 hard delay measurement results to obtain the frequency histogram, and the expectation of the hard delay is obtained as 10.01 milliseconds and the variance is 0.01 milliseconds.

[0089] Example 5

[0090] As Figure 4 shown, it is the result of evaluating the hard delay and soft delay using the method of the present invention in the scenario of a building-intensive area; in this scenario, the hard delay and soft delay are each measured 100 times to obtain the corresponding test results of the hard delay and soft delay. Among them, since the position update frequency is set to 100 Hz, the mathematical expectation of the soft delay is always 10 milliseconds; the test results of the hard delay show obvious fluctuation characteristics. The normal fitting is performed on the 100 hard delay measurement results to obtain the frequency histogram, and the expectation of the hard delay is obtained as 10.20 milliseconds and the variance is 0.09 milliseconds.

[0091] Example 6

[0092] As Figure 5 shown, it is the result of evaluating the hard delay and soft delay using the method of the present invention in the scenario of a residential building; in this scenario, the hard delay and soft delay are each measured 100 times to obtain the corresponding test results of the hard delay and soft delay. Among them, since the position update frequency is set to 100 Hz, the mathematical expectation of the soft delay is always 10 milliseconds; the test results of the hard delay show obvious fluctuation characteristics. The normal fitting is performed on the 100 hard delay measurement results to obtain the frequency histogram, and the expectation of the hard delay is obtained as 13.04 milliseconds and the variance is 0.26 milliseconds.

[0093] Example 7

[0094] As Figure 6 shown, it is the result of evaluating the hard delay and soft delay using the method of the present invention in the scenario of a supermarket; in this scenario, the hard delay and soft delay are each measured 100 times to obtain the corresponding test results of the hard delay and soft delay. Among them, since the position update frequency is set to 100 Hz, the mathematical expectation of the soft delay is always 10 milliseconds; the test results of the hard delay show obvious fluctuation characteristics. The normal fitting is performed on the 100 hard delay measurement results to obtain the frequency histogram, and the expectation of the hard delay is obtained as 13.93 milliseconds and the variance is 0.24 milliseconds.

[0095] Example 8

[0096] As Figure 7As shown, it is the result of evaluating the hard delay and soft delay using the method of the present invention in the airport hall scenario; in this scenario, the hard delay and soft delay are each measured 100 times, and the corresponding test results of the hard delay and soft delay are obtained. Among them, since the position update frequency is set to 100 Hz, the mathematical expectation of the soft delay is always 10 milliseconds; the test results of the hard delay show obvious fluctuation characteristics. The 100 hard delay measurement results are subjected to normal fitting to obtain a frequency histogram, and the expectation of the hard delay is obtained as 16.06 milliseconds and the variance is 0.37 milliseconds.

[0097] Example 9

[0098] As Figure 8 shown, it is the result of evaluating the hard delay and soft delay using the method of the present invention in the waiting room scenario; in this scenario, the hard delay and soft delay are each measured 100 times, and the corresponding test results of the hard delay and soft delay are obtained. Among them, since the position update frequency is set to 100 Hz, the mathematical expectation of the soft delay is always 10 milliseconds; the test results of the hard delay show obvious fluctuation characteristics. The 100 hard delay measurement results are subjected to normal fitting to obtain a frequency histogram, and the expectation of the hard delay is obtained as 16.03 milliseconds and the variance is 0.37 milliseconds.

[0099] Example 10

[0100] As Figure 9 shown, it is the result of evaluating the hard delay and soft delay using the method of the present invention in the theater scenario; in this scenario, the hard delay and soft delay are each measured 100 times, and the corresponding test results of the hard delay and soft delay are obtained. Among them, since the position update frequency is set to 100 Hz, the mathematical expectation of the soft delay is always 10 milliseconds; the test results of the hard delay show obvious fluctuation characteristics. The 100 hard delay measurement results are subjected to normal fitting to obtain a frequency histogram, and the expectation of the hard delay is obtained as 15.05 milliseconds and the variance is 0.22 milliseconds.

[0101] Example 11

[0102] As Figure 10 shown, it is the result of evaluating the hard delay and soft delay using the method of the present invention in the underground parking lot scenario; in this scenario, the hard delay and soft delay are each measured 100 times, and the corresponding test results of the hard delay and soft delay are obtained. Among them, since the position update frequency is set to 100 Hz, the mathematical expectation of the soft delay is always 10 milliseconds; the test results of the hard delay show obvious fluctuation characteristics. The 100 hard delay measurement results are subjected to normal fitting to obtain a frequency histogram, and the expectation of the hard delay is obtained as 19.01 milliseconds and the variance is 0.62 milliseconds.

[0103] Example 12

[0104] As Figure 11As shown, it is the result of evaluating the hard delay and soft delay using the method of the present invention in a forest scenario; in this scenario, the hard delay and soft delay are each measured 100 times, and the corresponding test results of the hard delay and soft delay are obtained. Among them, since the position update frequency is set to 100 Hz, the mathematical expectation of the soft delay is always 10 milliseconds; the test results of the hard delay show obvious fluctuation characteristics. The normal fitting is performed on the 100 hard delay measurement results to obtain a frequency histogram, and the expectation of the hard delay is obtained as 20.0 milliseconds and the variance is 0.17 milliseconds.

[0105] Example 13

[0106] As Figure 12 shown, it is the result of evaluating the hard delay and soft delay using the method of the present invention in a canyon scenario; in this scenario, the hard delay and soft delay are each measured 100 times, and the corresponding test results of the hard delay and soft delay are obtained. Among them, since the position update frequency is set to 100 Hz, the mathematical expectation of the soft delay is always 10 milliseconds; the test results of the hard delay show obvious fluctuation characteristics. The normal fitting is performed on the 100 hard delay measurement results to obtain a frequency histogram, and the expectation of the hard delay is obtained as 25.03 milliseconds and the variance is 1.86 milliseconds.

[0107] Example 14

[0108] As Figure 13 shown, it is the result of evaluating the hard delay and soft delay using the method of the present invention in a mine scenario; in this scenario, the hard delay and soft delay are each measured 100 times, and the corresponding test results of the hard delay and soft delay are obtained. Among them, since the position update frequency is set to 100 Hz, the mathematical expectation of the soft delay is always 10 milliseconds; the test results of the hard delay show obvious fluctuation characteristics. The normal fitting is performed on the 100 hard delay measurement results to obtain a frequency histogram, and the expectation of the hard delay is obtained as 13.77 milliseconds and the variance is 8.74 milliseconds.

[0109] Example 15

[0110] As Figure 14 shown, it is the result of evaluating the hard delay and soft delay using the method of the present invention in a tunnel scenario; in this scenario, the hard delay and soft delay are each measured 100 times, and the corresponding test results of the hard delay and soft delay are obtained. Among them, since the position update frequency is set to 100 Hz, the mathematical expectation of the soft delay is always 10 milliseconds; the test results of the hard delay show obvious fluctuation characteristics. The normal fitting is performed on the 100 hard delay measurement results to obtain a frequency histogram, and the expectation of the hard delay is obtained as 13.98 milliseconds and the variance is 9.07 milliseconds.

[0111] Example 16

[0112] The results of Examples 4 to 15 were tabulated to obtain the evaluation results under different experimental scenarios, as shown in Table 1. Among them, according to Equation (2) provided by the present invention, the overall quantitative evaluation of the delay was carried out. The overall quantitative evaluation results of the delay in Examples 4 to 15 were 15.01 milliseconds, 15.20 milliseconds, 18.04 milliseconds, 18.93 milliseconds, 21.06 milliseconds, 21.03 milliseconds, 20.05 milliseconds, 24.01 milliseconds, 25.0 milliseconds, 30.03 milliseconds, 18.77 milliseconds, and 18.98 milliseconds respectively; while the existing methods cannot achieve such quantification and evaluation.

[0113] Table 1. Evaluation Results of Delays under Different Experimental Scenarios

[0114]

[0115] Example 17

[0116] The magnitudes of the hard delays were tested when the test location update frequencies were 1 Hz, 2 Hz, 5 Hz, 10 Hz, 20 Hz, 25 Hz, 50 Hz, 100 Hz, 200 Hz, 250 Hz, and 500 Hz, and then the influence of the soft delay on the hard delay was evaluated. The test results are as Figure 15 shown, and the corresponding results were tabulated in Table 2. Among them, the μ change rate refers to the change rate of the current hard delay relative to the average value of each hard delay (here it is 10.0 milliseconds); σ 2 change rate refers to the change rate of the variance σ 2 of the current hard delay relative to the average value of the variances σ 2 of each hard delay (here it is 0.01 millisecond).

[0117] Table 2. Influence of Different Location Update Frequencies on Hard Delay

[0118]

[0119] In Figure 15 the first sub - figure in, the ordinate represents the quantitative results of the soft delay under different location update frequencies To make the display scale of the coordinate axes reasonable, we add a bias of 100 Hz to the position update frequency represented by the abscissa and then take the logarithm, i.e., log(F + 100). The other subfigures show the normal fitting frequency histograms of measuring the hard delay 100 times on the playground (open area) when the position update frequencies are 1 Hz, 2 Hz, 5 Hz, 10 Hz, 20 Hz, 25 Hz, 50 Hz, 100 Hz, 200 Hz, 250 Hz, and 500 Hz (for easy display, the number of occurrences of the corresponding values on the abscissa is simplified to the frequency). Combining the influence of the hard delay on the soft delay in Example 16 and the influence of the soft delay on the hard delay in this example, it can be seen that the superposition of the expected values of the hard delay and the soft delay in the proposed formula (2) holds, that is, the hard delay and the soft delay are independent of each other, thus demonstrating that the proposed methods for modeling, quantifying, and evaluating the delay are feasible and effective.

[0120] The above description is only the specific implementation manner of the present invention and not all implementation manners. Any equivalent transformation of the technical solution of the present invention adopted by those of ordinary skill in the art by reading the specification of the present invention is covered by the claims of the present invention.

Claims

1. Evaluation method for target position update delay, characterized in that It includes the following steps: Evaluation and modeling of the target position update delay; Quantification of the evaluation and modeling; In the evaluation and modeling of the target position update delay, the target position update delay is defined as a combined function of a hard delay and a soft delay, and the mathematical model is shown in Equation (1). Among them, D represents delay, D h represents hard delay, D s represents soft delay, represents combined mapping relationship, represents the combined function of hard delay and soft delay; The hard delay is defined as the delay generated when the position data communicates with the control center or the cloud platform through the physical topology link and / or the spatial topology link according to a certain communication protocol; the delay generated by the physical topology link refers to the delay generated when the position data is transmitted by wire; the delay generated by the spatial topology link refers to the delay generated when the position data is transmitted wirelessly. The soft delay is defined as the delay caused by the position update interval at the software algorithm level. The hard delay is a random variable and is modeled as a normal distribution with a mean of μ and a variance of σ 2 as shown in Equation (3). D h ~N(μ,σ 2 )(3) That is, the probability density distribution function of the hard delay is shown in Equation (4) as follows: Furthermore, the random variable D h has a mathematical expectation as shown in Equation (5). where E[·] represents the mathematical expectation of the variable ·; Equation (5) indicates that the h mathematical expectation of D is the mean μ; The soft delay is modeled as a function of the position update frequency at the software algorithm level, as shown in Equation (6) or (7). Where F is the position update frequency, g(·) represents the functional relationship, and δ is the update frequency error. The update frequency error is affected by the offset of the crystal oscillator frequency and the timer accuracy in the software algorithm, and is usually very small. After ignoring the position update frequency error δ, Equations (6) and (7) are uniformly simplified to Equation (8). Since the real position update events occur with equal probability within the time interval g(F), the mathematical expectation of the soft delay is shown in Equation (9).

2. The evaluation method for the delay in updating the target position according to claim 1, wherein The quantification of the evaluation modeling is the concretization of the mathematical model of the target position update delay, that is, the hard delay D h and the soft delay D s which is the superposition sum of the mathematical expectations, as shown in Equation (2). D = E[D h + D s = E[D h + E[D s (2).

3. The evaluation method for the delay of target position update according to claim 1, wherein Since it is impossible and unnecessary to obtain infinitely many measurement values of the hard delay in a specific scenario in practice, only multiple measurements of the specific scenario are required, and then the mean value μ of the hard delay is calculated by the maximum likelihood estimation method.

4. The method for evaluating the delay of target position update according to claim 1, wherein The update frequency is 0 to 500 Hz.

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