Observation result determination method, device, electronic device and computer readable storage medium

Abstract

The embodiments of the present invention provide a method, device, electronic device and computer-readable storage medium for determining observation results, taking into account the typical characteristics of the observation error of the sensor: the observation error in the observation direction is large, and the observation error in the normal direction of the observation direction is small; the observation error is large at a position farther away from the sensor, and the observation error is small at a position closer to the sensor. The embodiments of the present invention propose to determine the observation error covariance matrix of the sensor according to the distance between the sensor and the observed target, and the observation direction of the sensor, and then determine the observation result filter value using a Kalman filter according to the determined observation error covariance matrix and the actual observation value of the sensor to the observed target. Therefore, the embodiments of the present invention can determine the observation error based on the observation conditions (observation direction and observation distance) of the sensor to the observed target to obtain an accurate observation result filter value.

Description

Technical Field

[0001] The present invention relates to the field of information processing technology, and in particular to an observation result determination method, device, electronic device and computer-readable storage medium. Background Art

[0002] A roadside radar sensor (hereinafter referred to as DRSU, Digital Road Side Unit) is a sensor that integrates radar and camera. This sensor is usually deployed on poles on both sides of the road to sense road traffic conditions. In actual applications, multiple DRSUs often have overlapping sensing areas. Therefore, multiple DRSUs may observe the same observation target and output observation results separately. In this case, the observation results of multiple DRSUs need to be fused to obtain the fused observation results.

[0003] In the related art, the averaging method is usually used to determine the fused observation results. However, since each DRSU has errors when observing the target, the observation errors of each DRSU on the observed target are also different. Therefore, the average value fusion method used in the related art cannot actually obtain accurate observation results.

[0004] Therefore, there is an urgent need for a method that can more accurately determine the observation results of the sensor on the observation target. Summary of the invention

[0005] In view of the above problems, an embodiment of the present invention provides a method, device, electronic device and computer-readable storage medium for determining an observation result, so as to overcome the above problems or at least partially solve the above problems.

[0006] According to a first aspect of an embodiment of the present invention, a method for determining an observation result is provided, the method comprising:

[0007] According to the distance between the target sensor position and the observed target, and the observation direction of the target sensor, a predetermined sensor observation error basic covariance matrix is ​​converted to determine the observation error covariance matrix of the target sensor;

[0008] The observation error covariance matrix of the target sensor, the observation matrix of the target sensor for the observed target in the current observation period, and the filtered value of the observation result of the observed target in the previous observation period are input into the Kalman filter to determine the filtered value of the observation result of the target sensor for the observed target in the current observation period.

[0009] Optionally, for a plurality of target sensors that observe the observation target, the method further includes:

[0010] According to the distances between the respective positions of the plurality of target sensors and the observed targets and the respective observation directions of the plurality of target sensors, respectively converting the predetermined sensor observation error basic covariance matrix to determine the respective observation error covariance matrix of the plurality of target sensors;

[0011] Vertically splicing the observation matrices of the observation target observed by the multiple target sensors in the same observation period to obtain a fused observation matrix;

[0012] Diagonally concatenating the observation error covariance matrices of the multiple target sensors to obtain a fused observation error covariance matrix;

[0013] The fused observation matrix and the fused observation error covariance matrix, as well as the filtered value of the fused observation result of the observed target in the previous observation cycle, are input into the Kalman filter to determine the filtered value of the fused observation result of the multiple target sensors on the observed target in the current observation cycle.

[0014] Optionally, the step of converting a predetermined sensor observation error basic covariance matrix according to the distance between the target sensor position and the observed target and the observation direction of the target sensor to determine the observation error covariance matrix of the target sensor includes:

[0015] For target sensors whose observation direction is the specified positive direction:

[0016] According to the distance between the target sensor and the observed target, a predetermined sensor observation error basic covariance matrix is ​​converted to obtain the observation error covariance matrix of the target sensor.

[0017] Optionally, the step of converting a predetermined sensor observation error basic covariance matrix according to the distance between the target sensor position and the observed target and the observation direction of the target sensor to determine the observation error covariance matrix of the target sensor includes:

[0018] For a target sensor whose observation direction and the specified positive direction have an angle of θ:

[0019] According to the distance between the target sensor and the observed target, a predetermined sensor observation error basic covariance matrix is ​​converted to obtain a distance-converted observation error covariance matrix of the target sensor;

[0020] Then, according to the angle between the observation direction of the target sensor and the specified positive direction, the distance-converted observation error covariance matrix of the target sensor is rotated to obtain the observation error covariance matrix of the target sensor.

[0021] Optionally, according to the distance between the target sensor and the observed target, a predetermined sensor observation error basic covariance matrix is ​​converted to obtain an observation error covariance matrix of the target sensor, including:

[0022] According to the distance between the target sensor and the observed target, and a linear function, a predetermined sensor observation error basic covariance matrix is ​​converted;

[0023] The linear function is: R′=f′(R, d);

[0024] Wherein, R′ represents the observation error covariance matrix of the target sensor; R represents the sensor observation error covariance matrix; d represents the distance between the target sensor and the observed target; a, b, k are preset constants.

[0025] Optionally, rotating the distance-reduced observation error covariance matrix of the target sensor according to the angle between the observation direction of the target sensor and the specified positive direction includes:

[0026] Determine a coordinate rotation matrix according to the angle θ between the target sensor observation direction and the specified positive direction;

[0027] Performing diagonal concatenation on the coordinate rotation matrix to obtain a rotation matrix;

[0028] Rotating the distance-reduced observation error covariance matrix of the target sensor according to the rotation matrix;

[0029] Among them, the coordinate rotation matrix is

[0030] The rotation matrix is:

[0031] According to a second aspect of an embodiment of the present invention, there is provided an observation result determination device, the device comprising:

[0032] A first conversion module is used to convert a predetermined sensor observation error basic covariance matrix according to the distance between the target sensor position and the observed target and the observation direction of the target sensor to determine the observation error covariance matrix of the target sensor;

[0033] The first determination module is used to input the observation error covariance matrix of the target sensor, the observation matrix of the target sensor for the observed target in the current observation period, and the observation result filter value of the observed target in the previous observation period into the Kalman filter to determine the observation result filter value of the target sensor for the observed target in the current observation period.

[0034] Optionally, for a plurality of target sensors that observe the observation target, the device further includes:

[0035] A second conversion module is used to convert the predetermined sensor observation error basic covariance matrix according to the distance between the respective positions of the multiple target sensors and the observed targets and the respective observation directions of the multiple target sensors, so as to determine the observation error covariance matrix of each of the multiple target sensors;

[0036] A first splicing module, used for vertically splicing the observation matrices of the observation target observed by the multiple target sensors in the same observation period to obtain a fused observation matrix;

[0037] A second splicing module is used to perform diagonal splicing on the observation error covariance matrices of the multiple target sensors to obtain a fused observation error covariance matrix;

[0038] The second determination module is used to input the fused observation matrix and the fused observation error covariance matrix, as well as the filtered value of the fused observation result of the observed target in the previous observation period, into the Kalman filter to determine the filtered value of the fused observation result of the multiple target sensors on the observed target in the current observation period.

[0039] Optionally, the first conversion module includes:

[0040] For target sensors whose observation direction is the specified positive direction:

[0041] The conversion submodule is used to convert a predetermined sensor observation error basic covariance matrix according to the distance between the target sensor and the observed target to obtain the observation error covariance matrix of the target sensor.

[0042] Optionally, the first conversion module includes:

[0043] For a target sensor whose observation direction and the specified positive direction have an angle of θ:

[0044] A distance conversion submodule, used to convert a predetermined sensor observation error basic covariance matrix according to the distance between the target sensor and the observed target, to obtain a distance conversion observation error covariance matrix of the target sensor;

[0045] The rotation submodule is used to rotate the distance-converted observation error covariance matrix of the target sensor according to the angle between the observation direction of the target sensor and the specified positive direction to obtain the observation error covariance matrix of the target sensor.

[0046] Optionally, the distance submodule is specifically used to:

[0047] According to the distance between the target sensor and the observed target, and a linear function, a predetermined sensor observation error basic covariance matrix is ​​converted;

[0048] Wherein, the linear function is: R′=f′(R, d);

[0049] Wherein, R′ represents the observation error covariance matrix of the target sensor; R represents the sensor observation error covariance matrix; d represents the distance between the target sensor and the observed target; a, b, k are preset constants.

[0050] Optionally, the rotating submodule is specifically used for:

[0051] Determine a coordinate rotation matrix according to the angle θ between the target sensor observation direction and the specified positive direction;

[0052] Performing diagonal concatenation on the coordinate rotation matrix to obtain a rotation matrix;

[0053] Rotating the distance-reduced observation error covariance matrix of the target sensor according to the rotation matrix;

[0054] Among them, the coordinate rotation matrix is

[0055] The rotation matrix is:

[0056] According to a third aspect of an embodiment of the present invention, there is provided an electronic device, including:

[0057] A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor is configured to: execute any one of the observation result determination methods described in the first aspect above.

[0058] According to a fourth aspect of an embodiment of the present invention, a computer-readable storage medium is provided, wherein a computer program stored in the medium enables a processor to execute any one of the observation result determination methods described in the first aspect above.

[0059] In the embodiment of the present invention, the typical characteristics of the observation error of the sensor are considered to be: the observation error in the observation direction is large, and the observation error in the normal direction of the observation direction is small; the observation error at a position far from the sensor is large, and the observation error at a position close to the sensor is small. The embodiment of the present invention proposes to determine the observation error covariance matrix of the sensor according to the distance between the sensor and the observation target and the observation direction of the sensor, and then determine the observation result filter value using a Kalman filter according to the determined observation error covariance matrix and the actual observation value of the sensor to the observation target.

[0060] Therefore, the embodiment of the present invention can determine the observation error based on the observation conditions (observation direction and observation distance) of the sensor on the observation target to obtain an accurate observation result filtering value. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments of the present invention will be briefly introduced below. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative labor.

[0062] Figure 1 is a flowchart of the steps of a method for determining an observation result in an embodiment of the present invention;

[0063] Figure 2 is a flowchart of another method for determining an observation result in an embodiment of the present invention;

[0064] Figure 3 Schematic diagram of an observation result determination device in an embodiment of the present invention. DETAILED DESCRIPTION

[0065] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0066] A method for determining an observation result in an embodiment of the present invention is described below. Figure 1 As shown, the observation result determination method may specifically include the following steps:

[0067] Step S101 : converting a predetermined sensor observation error basic covariance matrix according to the distance between the target sensor position and the observed target and the observation direction of the target sensor to determine the observation error covariance matrix of the target sensor.

[0068] In the embodiment of the present invention, the observation model of the sensor is defined as:

[0069]

[0070] Among them, the 4-dimensional vector represents the real state of the target, and x and y represent the real x-coordinate and y-coordinate of the target respectively. and Indicates the speed of the target in the x direction and the speed in the y direction.

[0071] Represents the sensor's observation matrix, the first two dimensions represent the position of the observed target, and the last two dimensions represent the speed of the observed target. The observed value has errors, which are caused by random vectors express.

[0072] Assume that the sensor observes in the specified positive direction, and assume that the sensor's observation error in the x direction is uncorrelated with the observation error in the y direction, and assume that the sensor's observation of velocity is uncorrelated with the observation error of position (that is, the four random variables η x ,η y , The two are not related).

[0073] So the covariance matrix of these four random variables can be expressed as a square matrix with non-zero elements on the main diagonal:

[0074]

[0075] The matrix R is the predetermined sensor observation error basic covariance matrix.

[0076] In the embodiment of the present invention, the typical characteristics of the observation error of the sensor are considered to be: the observation error in the observation direction is large, and the observation error in the normal direction of the observation direction is small; the observation error at a position far from the sensor is large, and the observation error at a position close to the sensor is small. The embodiment of the present invention proposes to determine the observation error covariance matrix of the sensor according to the distance between the sensor and the observation target and the observation direction of the sensor.

[0077] For example, for ease of understanding, a two-dimensional coordinate system is assumed for explanation. Assume that there are two sensors: sensor A that observes along the X-axis direction and sensor B that observes along the Y-axis direction. For an observation target traveling along the X-axis direction, at the same observation distance, the observation error of sensor A is greater than the observation error of sensor B. For observation targets a and b (the X coordinate of b is greater than a) traveling along the X-axis direction, the observation error of sensor A for observation target b is greater than that for observation target a.

[0078] Based on this, in an embodiment of the present invention, the matrix R is converted based on the distance between the target sensor position and the observed target, and the observation direction of the target sensor, so as to determine the observation error covariance matrix of the target sensor.

[0079] Step S102, input the observation error covariance matrix of the target sensor, the observation matrix of the target sensor for the observed target in the current observation period, and the filtered value of the observation result of the observed target in the previous observation period into the Kalman filter to determine the filtered value of the observation result of the target sensor for the observed target in the current observation period.

[0080] In the embodiment of the present invention, through step S101, the observation covariance matrix when the target sensor observes the observation target at any distance and in any direction can be obtained.

[0081] In the embodiment of the present invention, the following formula is used to describe the target motion model:

[0082]

[0083] Among them, x k represents the x-coordinate of the target at time k, y k represents the y coordinate of the target at time k; represents the speed of the target in the x direction at time k (the derivative of the position with respect to time t), represents the speed of the target in the y direction at time k (the derivative of the position with respect to time t); Δt represents the time step from time k-1 to time k; — represents the acceleration of the target in the x direction at time k-1 (the second-order derivative of position with respect to time t), Represents the acceleration of the target in the y direction at time k-1 (the second-order derivative of position with respect to time t).

[0084] Also suppose: Represents the motion state of the observed target at time k.

[0085] By s k The time series {s k},k=1,2,3,...,t,... is called the true state trajectory of the observed target.

[0086] s k =A k-1 s k-1 +Γ k-1 ξ k-1 ;

[0087] in:

[0088] It is called the state transfer matrix. As long as Δt remains unchanged, Ak-1 It is time-invariant;

[0089] It is called the dynamic noise coupling matrix. As long as Δt remains unchanged, Γ k-1 It is time-invariant;

[0090] It is called the system dynamic noise at time k-1. In practical applications, since the acceleration of the observed target comes from the driver's control signal (acceleration, deceleration, turning) and interference signals (such as uphill and downhill, wind influence), the acceleration can be treated as a Gaussian random variable.

[0091] Therefore, based on the observation error covariance matrix, the observation matrix of the target sensor on the observed target in the current observation period, and the observation result filter value of the observed target in the previous observation period, the Kalman filter can be used to determine the observation result filter value of the target sensor on the observed target in the current observation period. Thus, the embodiment of the present invention can determine the observation error based on the observation conditions (observation direction and observation distance) of the sensor on the observed target and obtain an accurate observation result filter value.

[0092] For multiple target sensors that observe the observed target, the embodiment of the present invention proposes another method for determining the observation result, such as Figure 2 As shown, the observation result determination method in this embodiment may specifically include the following steps:

[0093] Step S201, according to the distances between the positions of the multiple target sensors and the observed targets, and the observation directions of the multiple target sensors, respectively convert the predetermined sensor observation error basic covariance matrix to determine the observation error covariance matrix of the multiple target sensors.

[0094] This step is similar to the above step S101 and will not be described again here.

[0095] Step S202 , vertically splicing the observation matrices of the observation target observed by the multiple target sensors in the same observation period to obtain a fused observation matrix.

[0096] In the embodiment of the present invention, when there are multiple target sensors, and the multiple sensors observe the same observation target, multiple observation matrices will be generated for the same target.

[0097] In this case, the observation matrices of the observation targets observed by multiple target sensors in the same observation period are vertically spliced ​​to obtain a fused observation matrix.

[0098] For example, suppose there are two target sensors observing the same observation target, and the two sensors are numbered #1 and #2 respectively. For the same target, at time k, the observation matrix of sensor #1 is The observation error covariance matrix is The measurement matrix of sensor #2 is The observation error covariance matrix is In the embodiment of the present invention, and Splice vertically into a large observation vector:

[0099]

[0100] Step S203 , performing diagonal splicing on the observation error covariance matrices of the plurality of target sensors to obtain a fused observation error covariance matrix.

[0101] In the embodiment of the present invention, two sensors numbered #1 and #2 are still used as an example for explanation. Since the observation values ​​generated by sensor #1 and sensor #2 are unrelated, and Diagonally concatenate to form a new observation covariance matrix:

[0102]

[0103] In the embodiment of the present invention, the unit matrix and Concatenate into a new matrix:

[0104]

[0105] At this time, the observation equation is:

[0106] Step S204, input the fused observation matrix and the fused observation error covariance matrix, as well as the filtered value of the fused observation result of the observed target in the previous observation period, into the Kalman filter to determine the filtered value of the fused observation result of the multiple target sensors on the observed target in the current observation period.

[0107] In an embodiment of the present invention, based on the fused matrix and the fused observation error covariance matrix, as well as the filtered value of the fused observation result of the observed target in the previous observation period, the Kalman filter can be used to perform a filtering process to obtain the filtered value of the fused observation result for the observed target in the current observation period.

[0108] In an embodiment of the present invention, the observation matrices of multiple sensors are vertically spliced ​​and the observation error covariance matrices are diagonally spliced ​​before linear Kalman filtering, so that the Kalman filter can simultaneously process the observation results of multiple target sensors and fuse the observation results of multiple sensors, thereby obtaining a more accurate filtered estimate of the true state of the observed target.

[0109] In an optional implementation manner of the present invention, for a target sensor whose observation direction is a specified positive direction, step S201 includes:

[0110] According to the distance between the target sensor and the observed target, a predetermined sensor observation error basic covariance matrix is ​​converted to obtain the observation error covariance matrix of the target sensor.

[0111] In the embodiment of the present invention, the specified positive direction may be any direction preset in advance by a technician, for example, due east.

[0112] In the embodiment of the present invention, for a target sensor whose observation direction is a specified positive direction, it is not necessary to perform direction conversion, but only distance conversion.

[0113] In the embodiment of the present invention, the principle of distance conversion is: when the observed target is closer to the target sensor, the observation error is smaller, and when the observed target is farther from the target sensor, the observation error is larger. Based on this, a linear function can be used to convert the predetermined sensor observation error basic covariance matrix, or a nonlinear function can be used to convert the predetermined sensor observation error basic covariance matrix. In practical applications, a technician can determine the actual distance conversion function based on experience, and the present invention does not impose specific restrictions on this.

[0114] Optionally, in an embodiment of the present invention, a specific implementation method for converting a predetermined sensor observation error basic covariance matrix using a linear function is provided:

[0115] In this embodiment, step S201 specifically includes:

[0116] According to the distance between the target sensor and the observed target, and a linear function, a predetermined sensor observation error basic covariance matrix is ​​converted;

[0117] Wherein, the linear function is: R′=f′(R, d);

[0118] Wherein, R′ represents the observation error covariance matrix of the target sensor; R represents the sensor observation error covariance matrix; d represents the distance between the target sensor and the observed target; a, b, k are preset constants.

[0119] In the embodiment of the present invention, a, b, and k are all constants preset in advance by technicians according to actual needs.

[0120] In an optional implementation manner of the present invention, for a target sensor whose observation direction and the specified positive direction have an angle θ, step S201 includes:

[0121] Step S2011, according to the distance between the target sensor and the observed target, a predetermined sensor observation error basic covariance matrix is ​​converted to obtain a distance-converted observation error covariance matrix of the target sensor.

[0122] Step S2012: Rotate the distance-converted observation error covariance matrix of the target sensor according to the angle between the observation direction of the target sensor and the specified positive direction to obtain the observation error covariance matrix of the target sensor.

[0123] In the embodiment of the present invention, for a target sensor whose observation direction is not the specified positive direction, it is necessary to first perform distance conversion on the sensor observation error basic covariance matrix, and then perform direction conversion on the sensor to obtain the observation error covariance matrix of the target sensor.

[0124] The distance conversion process is expressed as: R′=f′(R, d).

[0125] Wherein, step S2012 specifically includes:

[0126] Determine a coordinate rotation matrix according to the angle θ between the target sensor observation direction and the specified positive direction;

[0127] Performing diagonal concatenation on the coordinate rotation matrix to obtain a rotation matrix;

[0128] Rotating the distance-reduced observation error covariance matrix of the target sensor according to the rotation matrix;

[0129] Among them, the coordinate rotation matrix is

[0130] The rotation matrix is:

[0131] At this time, the rotation conversion process can be expressed as: R″=HR′H T .

[0132] In the embodiment of the present invention, the predetermined sensor observation error basic covariance matrix is ​​converted according to the distance between the target sensor position and the observed target, and the observation direction of the target sensor, and the observation error covariance matrix of any target sensor can be determined. Thus, the observation matrices of any multiple target sensors and the observation error covariance matrix can be fused, so that the Kalman filter can process the observation results of multiple target sensors at the same time and obtain the fused observation result filter value of the observed target.

[0133] refer to Figure 3 , shows an observation result determination device, the device comprising:

[0134] A first conversion module 301 is used to convert a predetermined sensor observation error basic covariance matrix according to the distance between the target sensor position and the observed target and the observation direction of the target sensor to determine the observation error covariance matrix of the target sensor;

[0135] The first determination module 302 is used to input the observation error covariance matrix of the target sensor, the observation matrix of the target sensor for the observed target in the current observation period, and the observation result filter value of the observed target in the previous observation period into the Kalman filter to determine the observation result filter value of the target sensor for the observed target in the current observation period.

[0136] Optionally, for a plurality of target sensors that observe the observation target, the device further includes:

[0137] A second conversion module is used to convert the predetermined sensor observation error basic covariance matrix according to the distance between the respective positions of the multiple target sensors and the observed targets and the respective observation directions of the multiple target sensors, so as to determine the observation error covariance matrix of each of the multiple target sensors;

[0138] A first splicing module, used for vertically splicing the observation matrices of the observation target observed by the multiple target sensors in the same observation period to obtain a fused observation matrix;

[0139] A second splicing module is used to perform diagonal splicing on the observation error covariance matrices of the multiple target sensors to obtain a fused observation error covariance matrix;

[0140] The second determination module is used to input the fused observation matrix and the fused observation error covariance matrix, as well as the filtered value of the fused observation result of the observed target in the previous observation period, into the Kalman filter to determine the filtered value of the fused observation result of the multiple target sensors on the observed target in the current observation period.

[0141] Optionally, the first conversion module 301 includes:

[0142] For target sensors whose observation direction is the specified positive direction:

[0143] The conversion submodule is used to convert a predetermined sensor observation error basic covariance matrix according to the distance between the target sensor and the observed target to obtain the observation error covariance matrix of the target sensor.

[0144] Optionally, the first conversion module 301 includes:

[0145] For a target sensor whose observation direction and the specified positive direction have an angle of θ:

[0146] A distance conversion submodule, used to convert a predetermined sensor observation error basic covariance matrix according to the distance between the target sensor and the observed target, to obtain a distance conversion observation error covariance matrix of the target sensor;

[0147] The rotation submodule is used to rotate the distance-converted observation error covariance matrix of the target sensor according to the angle between the observation direction of the target sensor and the specified positive direction to obtain the observation error covariance matrix of the target sensor.

[0148] Optionally, the distance conversion submodule is specifically used to:

[0149] According to the distance between the target sensor and the observed target, and a linear function, a predetermined sensor observation error basic covariance matrix is ​​converted;

[0150] Wherein, the linear function is: R′=f′(R, d);

[0151] Wherein, R′ represents the observation error covariance matrix of the target sensor; R represents the sensor observation error covariance matrix; d represents the distance between the target sensor and the observed target; a, b, k are preset constants.

[0152] Optionally, the rotating submodule is specifically used for:

[0153] Determine a coordinate rotation matrix according to the angle θ between the target sensor observation direction and the specified positive direction;

[0154] Performing diagonal concatenation on the coordinate rotation matrix to obtain a rotation matrix;

[0155] Rotating the distance-reduced observation error covariance matrix of the target sensor according to the rotation matrix;

[0156] Among them, the coordinate rotation matrix is

[0157] The rotation matrix is:

[0158] It should be noted that the device embodiment is similar to the method embodiment, so the description is relatively simple, and the relevant parts can be referred to the method embodiment.

[0159] An embodiment of the present invention further provides an electronic device, including:

[0160] A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor is configured to: execute the observation result determination method as described in any of the above embodiments.

[0161] An embodiment of the present invention also provides a computer-readable storage medium, which stores a computer program that enables a processor to execute the observation result determination method described in any of the above embodiments.

[0162] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referenced to each other.

[0163] It will be appreciated by those skilled in the art that the embodiments of the present invention may be provided as methods, devices, or computer program products. Therefore, the embodiments of the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the embodiments of the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0164] The embodiments of the present invention are described with reference to the flowcharts and / or block diagrams of the methods, terminal devices (systems), and computer program products according to the embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of the processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing terminal device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing terminal device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0165] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing terminal device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce a manufactured product including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0166] These computer program instructions can also be loaded onto a computer or other programmable data processing terminal device so that a series of operating steps are executed on the computer or other programmable terminal device to produce a computer-implemented process, thereby providing instructions for executing on the computer or other programmable terminal device to implement the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0167] Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the embodiments of the present invention.

[0168] Finally, it should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or terminal device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or terminal device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or terminal device including the elements.

[0169] The above is a detailed introduction to the observation result determination method, device, equipment and storage medium provided by the present invention. Specific examples are used in this article to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea; at the same time, for general technical personnel in this field, according to the idea of ​​the present invention, there will be changes in the specific implementation method and application scope. In summary, the content of this specification should not be understood as a limitation on the present invention.

Claims

1. A method for determining an observation result, characterized in that: The method comprises: According to the distance between the target sensor position and the observed target, and the observation direction of the target sensor, a predetermined sensor observation error basic covariance matrix is ​​converted to determine the observation error covariance matrix of the target sensor; Inputting the observation error covariance matrix of the target sensor, the observation matrix of the target sensor for the observed target in the current observation period, and the observation result filter value of the observed target in the previous observation period into the Kalman filter to determine the observation result filter value of the target sensor for the observed target in the current observation period; For a plurality of target sensors that observe the observation target, the method further includes: According to the distances between the respective positions of the plurality of target sensors and the observed targets and the respective observation directions of the plurality of target sensors, respectively converting the predetermined sensor observation error basic covariance matrix to determine the respective observation error covariance matrix of the plurality of target sensors; Vertically splicing the observation matrices of the observation target observed by the multiple target sensors in the same observation period to obtain a fused observation matrix; Diagonally concatenating the observation error covariance matrices of the multiple target sensors to obtain a fused observation error covariance matrix; Inputting the fused observation matrix and the fused observation error covariance matrix, as well as the fused observation result filter value of the observed target in the previous observation period, into the Kalman filter to determine the fused observation result filter value of the observed target by the multiple target sensors in the current observation period; The step of converting a predetermined sensor observation error basic covariance matrix according to the distance between each position of the plurality of target sensors and the observed target and each observation direction of the plurality of target sensors to determine each observation error covariance matrix of the plurality of target sensors includes: According to the distance between the target sensor and the observed target, and a linear function, a predetermined sensor observation error basic covariance matrix is ​​converted; Wherein, the linear function is: R′=f′(R, d); Wherein, R′ represents the observation error covariance matrix of the target sensor; R represents the sensor observation error basic covariance matrix; d represents the distance between the target sensor and the observed target; a, b, k are preset constants.

2. The method according to claim 1, characterized in that The step of converting a predetermined sensor observation error basic covariance matrix according to the distance between the target sensor position and the observed target and the observation direction of the target sensor to determine the observation error covariance matrix of the target sensor includes: For target sensors whose observation direction is the specified positive direction: According to the distance between the target sensor and the observed target, a predetermined sensor observation error basic covariance matrix is ​​converted to obtain the observation error covariance matrix of the target sensor.

3. The method according to claim 1, characterized in that The step of converting a predetermined sensor observation error basic covariance matrix according to the distance between the target sensor position and the observed target and the observation direction of the target sensor to determine the observation error covariance matrix of the target sensor includes: For a target sensor whose observation direction has an angle θ with the specified positive direction: According to the distance between the target sensor and the observed target, a predetermined sensor observation error basic covariance matrix is ​​converted to obtain a distance-converted observation error covariance matrix of the target sensor; Then, according to the angle between the observation direction of the target sensor and the specified positive direction, the distance-converted observation error covariance matrix of the target sensor is rotated to obtain the observation error covariance matrix of the target sensor.

4. The method according to claim 3, characterized in that According to the angle between the observation direction of the target sensor and the specified positive direction, the distance-reduced observation error covariance matrix of the target sensor is rotated, including: Determine a coordinate rotation matrix according to the angle θ between the target sensor observation direction and the specified positive direction; Performing diagonal concatenation on the coordinate rotation matrix to obtain a rotation matrix; Rotating the distance-reduced observation error covariance matrix of the target sensor according to the rotation matrix; Among them, the coordinate rotation matrix is The rotation matrix is:

5. An observation result determination device, characterized in that: The device comprises: A first conversion module is used to convert a predetermined sensor observation error basic covariance matrix according to the distance between the target sensor position and the observed target and the observation direction of the target sensor to determine the observation error covariance matrix of the target sensor; A first determination module is used to input the observation error covariance matrix of the target sensor, the observation matrix of the target sensor for the observed target in the current observation period, and the observation result filter value of the observed target in the previous observation period into a Kalman filter to determine the observation result filter value of the target sensor for the observed target in the current observation period; For a plurality of target sensors that observe the observation target, the device further includes: A second conversion module is used to convert the predetermined sensor observation error basic covariance matrix according to the distance between the respective positions of the multiple target sensors and the observed targets and the respective observation directions of the multiple target sensors, so as to determine the observation error covariance matrix of each of the multiple target sensors; A first splicing module, used for vertically splicing the observation matrices of the observation target observed by the multiple target sensors in the same observation period to obtain a fused observation matrix; A second splicing module is used to perform diagonal splicing on the observation error covariance matrices of the multiple target sensors to obtain a fused observation error covariance matrix; The second determination module is used to input the fused observation matrix and the fused observation error covariance matrix, as well as the fused observation result filter value of the observed target in the previous observation period, into the Kalman filter to determine the fused observation result filter value of the observed target by the multiple target sensors in the current observation period; The step of converting a predetermined sensor observation error basic covariance matrix according to the distance between each position of the plurality of target sensors and the observed target and each observation direction of the plurality of target sensors to determine each observation error covariance matrix of the plurality of target sensors includes: According to the distance between the target sensor and the observed target, and a linear function, a predetermined sensor observation error basic covariance matrix is ​​converted; Wherein, the linear function is: R′=f′(R, d); Wherein, R′ represents the observation error covariance matrix of the target sensor; R represents the sensor observation error basic covariance matrix; d represents the distance between the target sensor and the observed target; a, b, k are preset constants.

6. An electronic device, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor is configured to: execute the observation result determination method as described in any one of claims 1 to 4.

7. A computer-readable storage medium, characterized in that: The computer program stored therein enables the processor to execute the observation result determination method as described in any one of claims 1 to 4.

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