Target positioning method based on multi-station long-time passive angle measurement

Abstract

The application provides a target positioning method based on multi-station long-time passive angle measurement, which is applied to an information collection node and comprises the following steps: obtaining angle information of a target collected by a passive sensor in a preset time period, and a sampling position and a sampling time of the passive sensor; estimating a first sampling position and a first motion speed corresponding to the passive sensor at a first time according to the sampling position and the sampling time of the passive sensor; estimating first angle information of the target corresponding to the passive sensor at the first time according to the angle information of the target collected by the passive sensor and the sampling time; and sending the first sampling position, the first motion speed and the first angle information corresponding to the passive sensor to an information fusion center, so that the information fusion center estimates the position and the speed of the target at the first time according to the first sampling position, the first motion speed and the first angle information corresponding to the passive sensor, and the communication amount can be reduced and the positioning precision can be improved.

Description

Technical Field

[0001] This invention belongs to the field of signal processing technology, specifically relating to a target localization method based on multi-station long-term passive angle measurement. Background Technology

[0002] Target localization is the process of estimating the target's position, velocity, and other motion parameters using sensor measurements. Depending on the type of sensor, target localization can be divided into two types: active measurement-based and passive measurement-based. Passive sensors, which only need to receive signals, offer advantages such as good concealment and strong anti-interference capabilities.

[0003] Depending on the type of measurement, common passive sensor measurements include time of arrival (TOA), time difference of arrival (TDA), frequency domain information, and angle. Target localization methods based on TOA or TDA utilize the measurement of TOA or TDA by passive sensors to obtain the distance or distance difference between the sensor and the target, thereby estimating the target's motion state. However, this requires high sensitivity of the passive sensor, and the target needs to emit a radiation signal or a transmitter is required to assist in measuring the TOA or TDA. Target localization methods based on frequency domain information mainly utilize the frequency difference information of multiple passive sensors at the same radiation source to estimate the target's motion state, but it depends on the frequency of the radiation source itself and has poor adaptability to signals. Compared to target localization methods based on TOA, TDA, and frequency domain information, target localization methods based on angle measurement do not require a target radiation signal, and passive sensors with angle measurement capabilities are typically smaller, requiring less load capacity from the mounting platform. With the rapid development of passive sensors with angle measurement capabilities, such as optoelectronic platforms, infrared angle sensors, and passive radar, target localization methods based on angle measurement have also been widely applied.

[0004] When locating a target, passive sensors are mounted on platforms distributed at different locations in space. After each passive sensor acquires the target's angle measurement, traditional positioning methods, such as the patent application CN109991572A entitled "A Dual-Machine Passive Positioning Method Based on Azimuth and Pitch Angle Information," require transmitting the measurement time, angle measurement, and corresponding sensor position coordinates at each moment to a fusion center. Therefore, this places high demands on communication bandwidth. Furthermore, for moving targets, positioning requires synchronized angle measurements from multiple passive sensors, which is difficult to achieve in practice. Summary of the Invention

[0005] To address the aforementioned problems in the existing technology, this invention provides a target localization method based on multi-station long-term passive angle measurement. This invention is achieved through the following technical solution:

[0006] In a first aspect, the present invention provides a target positioning method based on multi-station long-term passive angle measurement, applied to an information acquisition node, wherein a passive sensor is deployed in the information acquisition node, and the method includes:

[0007] Acquire the angle information of the target collected by the passive sensor within a preset time period, as well as the sampling position and sampling time of the passive sensor;

[0008] Based on the sampling position and sampling time of the passive sensor, estimate the first sampling position and first motion velocity of the passive sensor at the first moment;

[0009] Based on the target's angle information and sampling time collected by the passive sensor, estimate the first angle information of the target corresponding to the passive sensor at the first moment;

[0010] The first sampling position, first motion speed, and first angle information corresponding to the passive sensor are sent to the information fusion center so that the information fusion center can estimate the position and speed of the target at the first moment based on the first sampling position, first motion speed, and first angle information corresponding to the passive sensor.

[0011] Secondly, the present invention also provides a target positioning method based on multi-station long-term passive angle measurement, applied to an information fusion center, the method comprising:

[0012] The system acquires the first sampling position, first motion speed, and first angle information corresponding to the passive sensor at the first moment, which are sent by multiple information acquisition nodes. The first sampling position and first motion speed are determined by each information acquisition node based on the sampling position and sampling time of the corresponding passive sensor within the acquired preset time period. The first angle information is determined by each information acquisition node based on the angle of the target acquired by the corresponding passive sensor and the sampling time within the acquired preset time period.

[0013] Based on the first sampling position, first motion velocity, and first angle information corresponding to each passive sensor, the position and velocity of the target at the first moment are estimated.

[0014] Thirdly, the present invention also provides a target positioning system based on multi-station long-term passive angle measurement, including multiple information acquisition nodes and an information fusion center, wherein passive sensors are deployed in the information acquisition nodes.

[0015] Each information collection node is connected to the information fusion center via communication.

[0016] Each information acquisition node is used to obtain the angle information of the target collected by the passive sensor within a preset time period, as well as the sampling position and sampling time of the passive sensor;

[0017] Each information acquisition node is also used to estimate the first sampling position and the first motion velocity of the passive sensor at the first moment, based on the sampling position and sampling time of the passive sensor.

[0018] Each information acquisition node is also used to estimate the first angle information of the target corresponding to the passive sensor at the first moment, based on the angle information of the target acquired by the passive sensor and the sampling time.

[0019] Each information acquisition node is also used to send the first sampling position, first motion speed and first angle information corresponding to the passive sensor to the information fusion center;

[0020] The information fusion center is used to estimate the target's position and velocity at the first moment based on the first sampling position, first motion velocity, and first angle information corresponding to the passive sensor.

[0021] Fourthly, the present invention provides an electronic device, including a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus;

[0022] Memory, used to store computer programs;

[0023] A processor, when executing a program stored in memory, implements any of the method steps provided by the first, second, or third aspect.

[0024] Fifthly, the present invention provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements any of the method steps provided in the first, second, or third aspects.

[0025] The beneficial effects of this invention are:

[0026] This invention provides a target localization method based on multi-station long-term passive angle measurement, applied to an information acquisition node equipped with passive sensors. The method acquires the target's angle information, sampling position, and sampling time collected by the passive sensors within a preset time period. Based on the sampling position and sampling time, it estimates the first sampling position and first velocity corresponding to the passive sensors at a first moment. Furthermore, based on the target's angle information and sampling time collected by the passive sensors, it estimates the first angle information of the target corresponding to the passive sensors at the first moment. This achieves linear fitting of both the sensor position information and the sensor acquisition results. During the linear model fitting process, it can reduce measurement errors to a certain extent. To reduce the impact of measurement errors on positioning accuracy and improve the accuracy of positioning results, further, by sending the first sampling position, first motion velocity, and first angle information corresponding to the passive sensor to the information fusion center, the information fusion center can estimate the target's position and velocity at the first moment based on the first sampling position, first motion velocity, and first angle information corresponding to the passive sensor. This achieves the goal of only transmitting the linearly fitted parameters to the information fusion center during data transmission, thereby greatly reducing the communication volume and ensuring that the communication volume does not increase with the number of sensor samplings over a period of time, thus improving communication efficiency and quality. Transmitting data after linear fitting enables multiple sensors to work in asynchronous sampling scenarios, increasing the application scope and improving the user experience.

[0027] The present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0028] Figure 1 A flowchart illustrating a target positioning method based on multi-station long-term passive angle measurement provided by the present invention;

[0029] Figure 2 A flowchart illustrating another target positioning method based on multi-station long-term passive angle measurement provided by the present invention;

[0030] Figure 3 A schematic diagram of the structure of a target positioning system based on multi-station long-term passive angle measurement provided by the present invention;

[0031] Figure 4 A set of simulation results comparison charts provided for this invention;

[0032] Figure 5 This is a comparison chart of another set of simulation results provided by the present invention. Detailed Implementation

[0033] The present invention will be further described in detail below with reference to specific embodiments, but the implementation of the present invention is not limited thereto.

[0034] Since photoelectric platforms and infrared angle measurement systems are mostly mounted on mobile platforms such as aircraft, passive sensors will take multiple measurements of the target within a certain period of time. Existing target positioning technologies based on angle measurement need to transmit multiple angle measurements, sensor position measurements, and measurement times to the information processing center. Therefore, the amount of communication will increase linearly with the increase in the number of measurements.

[0035] If the measurement time, angle measurement, and corresponding sensor position coordinates at various moments can be processed and then transmitted in a certain way, the communication volume between the acquisition nodes and the fusion center can be reduced, thereby effectively improving the positioning efficiency. Based on this, the present invention provides a target positioning method based on multi-station long-term passive angle measurement. Figure 1 This is a flowchart illustrating a target positioning method based on long-term passive angle measurement at multiple stations, provided by the present invention. The method is applied to an information acquisition node, in which passive sensors are deployed.

[0036] like Figure 1 As shown, the method includes:

[0037] S101. Obtain the angle information of the target collected by the passive sensor within a preset time period, as well as the sampling position and sampling time of the passive sensor.

[0038] The passive sensor is a passive angle sensor.

[0039] For example, angle information may include azimuth and pitch angle.

[0040] The passive sensor can sample the target's angle information multiple times within a preset time period, and the sampling time can be any moment within the preset time period. Within the preset time period, the passive sensor can move and change position to sample the target's angle information at different positions.

[0041] Information acquisition nodes may include angle information acquisition modules, location information acquisition modules, time information acquisition modules, and information processors.

[0042] The angle information acquisition module includes a passive sensor for sampling the angle information of the target.

[0043] The location information acquisition module is used to obtain the sampling location of the passive sensor.

[0044] The time information acquisition module is used to obtain the sampling time of the passive sensor.

[0045] Optionally, the location information acquisition module can obtain the sampling position of the passive sensor based on a pre-built spatial rectangular coordinate system.

[0046] For example, passive sensor z n The i-th sampling position is represented as:

[0047] p n,i =[x n,i ,y n,i ,z n,i ] T

[0048] Where, x n,i y n,i z n,i These represent the passive sensor z. n The components of the i-th sampling position on the X, Y, and Z axes of the Cartesian coordinate system, 1 ≤ i ≤ M n M n This indicates a passive sensor z. n Total number of observations, M n ≥2, [·] T This indicates the transpose operation.

[0049] S102. Based on the sampling position and sampling time of the passive sensor, determine the first sampling position and the first motion speed corresponding to the passive sensor at the first moment.

[0050] Optionally, based on the sampling position and sampling time of the passive sensor, the first sampling position and first motion velocity corresponding to the passive sensor at the first moment are determined, including:

[0051] For passive sensor z n According to the passive sensor z n The sampling location and sampling time are used to determine the first moment t0, and the passive sensor z n The corresponding first sampling position and first velocity Represented as:

[0052]

[0053]

[0054]

[0055] Where I3 represents a 3×3 identity matrix, A s,n The dimension is 3M n A 6 × 10⁶ matrix, M n This indicates a passive sensor z. n Total number of samplings, p n,1 This indicates a passive sensor z. n The sampling position during the first sampling, p n For the passive sensor zn M n A vector consisting of sampling positions, p n The dimension is 3M n ×1,t n,1 This indicates a passive sensor z. n The sampling time during the first sampling, (·) T Indicates transpose. For the passive sensor z n The corresponding first sampling position and first velocity The vector formed.

[0056] Using passive sensor z n Multiple position measurement estimation of passive sensor z n First sampling position and first velocity It can reduce the impact of measurement noise on sensor position measurement to a certain extent, improve the estimation accuracy of the position of passive sensors, and at the same time, during data transmission, only the first sampling position and the first motion speed of the passive sensor need to be transmitted to the fusion center, reducing the communication requirements.

[0057] S103. Based on the target angle information and sampling time collected by the passive sensor, determine the first angle information of the target corresponding to the passive sensor at the first moment.

[0058] Optionally, the target's first angle information includes: the target's first azimuth angle, first azimuth angle rate of change, first pitch angle, and first pitch angle rate of change.

[0059] Optionally, based on the target's angle information and sampling time collected by the passive sensor, the first azimuth angle and the rate of change of the first azimuth angle of the target corresponding to the passive sensor are determined at the first moment, including:

[0060] For passive sensor z n According to the passive sensor z n The angle of the target and the sampling time are collected to determine the first moment t0, and the passive sensor z n The first azimuth of the corresponding target and the rate of change of the first azimuth angle Represented as:

[0061]

[0062]

[0063]

[0064]

[0065]

[0066]

[0067] Among them, A a,n This indicates that the passive sensor z n M n The matrix formed by the difference between the measurement time of each measurement and the first time point t0. Indicates a length of M n A vector of all 1s, where ∑ represents the summation operation, and M n This indicates a passive sensor z. n Total number of samplings, θ n This indicates that the passive sensor z n M n The target azimuth angle θ collected in the second acquisition n,i The vector formed, θ n,i This indicates a passive sensor z. n The azimuth angle of the target in the i-th acquisition, t n,i This indicates a passive sensor z. n The sampling time t during the i-th sampling n This indicates that the passive sensor z n M n The sampling time t of the next sample n,i The vector formed, T d This indicates a passive sensor z. n M n The sampling time t of the next sample n,i The mean of the time difference with the first sampling time t0, T D This indicates a passive sensor z. n M n The sampling time t of the next sample n,i The variance with respect to the first sampling time t0.

[0068] Optionally, based on the target angle information and sampling time collected by the passive sensor, the first pitch angle and the rate of change of the first pitch angle of the target corresponding to the passive sensor at the first moment are determined, including:

[0069] For passive sensor z n According to the passive sensor z n The angle of the target and the sampling time are collected to determine the first moment t0, and the passive sensor z n The first pitch angle of the corresponding target and the first pitch angle change rate Represented as:

[0070]

[0071]

[0072]

[0073]

[0074]

[0075]

[0076] Among them, A a,n This indicates that the passive sensor z n M n The matrix formed by the difference between the measurement time of each measurement and the first time point t0. Indicates a length of M n A vector of all 1s, where ∑ represents the summation operation, and M n This indicates a passive sensor z. n Total number of samplings, This indicates that the passive sensor z n M n The target elevation angle for the second acquisition The vector formed This indicates a passive sensor z. n The pitch angle of the target acquired in the i-th acquisition, t n,i This indicates a passive sensor z. n The sampling time t during the i-th sampling n This indicates that the passive sensor z n M n The sampling time t of the next sample n,i The vector formed, T d This indicates a passive sensor z. n M n The sampling time t of the next sample n,i The mean of the time difference with the first sampling time t0, T D This indicates a passive sensor z. n M n The sampling time t of the next sample n,i The variance with respect to the first sampling time t0.

[0077] This method uses linear fitting to the collected angle information, reducing the amount of data, thereby reducing the communication volume between the information collection node and the information fusion center, improving communication efficiency, increasing processing speed, and improving positioning efficiency.

[0078] Optionally, before sending the first sampling position, first motion velocity, and first angle information corresponding to the passive sensor to the information fusion center, the method further includes:

[0079] Determine whether the total number of samplings corresponding to the passive sensor is greater than or equal to the first threshold. If so, determine the average azimuth angle estimation error corresponding to the passive sensor based on the first azimuth angle and the first azimuth angle change rate corresponding to the passive sensor at the first moment, and / or determine the average pitch angle estimation error corresponding to the passive sensor based on the first pitch angle and the first pitch angle change rate corresponding to the passive sensor at the first moment.

[0080] If the average azimuth estimation error is greater than the second threshold, and / or if the average pitch estimation error is greater than the third threshold, then the first angle information of the target corresponding to the passive sensor at the first moment is re-determined based on the target angle information and sampling time collected by the passive sensor.

[0081] Correspondingly, if it is determined that the total number of samplings corresponding to the passive sensor is less than the first threshold, the first sampling position, first motion speed and first angle information corresponding to the passive sensor are directly sent to the information fusion center.

[0082] Accordingly, if it is determined that the total number of samplings corresponding to the passive sensor is greater than or equal to the first threshold, and the average azimuth angle estimation error is less than or equal to the second threshold and / or the average pitch angle estimation error is less than or equal to the third threshold, then the first sampling position, first motion speed and first angle information corresponding to the passive sensor are directly sent to the information fusion center.

[0083] For example, if it is determined that the passive sensor z n The corresponding total number of samplings M n Is it greater than or equal to the first threshold γ? If M n If ≥γ, then based on the first time t0, the first azimuth angle corresponding to the passive sensor is... and the rate of change of the first azimuth angle Determine the average azimuth estimation error ε corresponding to the passive sensor. n,θ And / or, based on the first time t0, the first pitch angle corresponding to the passive sensor. and the first pitch angle change rate Determine the average pitch angle estimation error corresponding to the passive sensor. If the average azimuth estimation error ε n,θ If the average pitch angle estimation error is greater than the second threshold, and / or if ... If the value is greater than the third threshold, then step S103 is executed again.

[0084] The second and third thresholds can be the same or different.

[0085] Optionally, based on the first time t0, the first azimuth angle corresponding to the passive sensor. and the rate of change of the first azimuth angle Determine the average azimuth estimation error ε corresponding to the passive sensor. n,θ ,include:

[0086] 1) Based on the first time t0, the first azimuth angle corresponding to the passive sensor and the rate of change of the first azimuth angle Estimate at time t n,i The azimuth angle corresponding to the passive sensor Represented as:

[0087]

[0088] Where i = 1, 2, ..., M n , t n,i M represents the i-th actual sampling time of the passive sensor. n This indicates a passive sensor z. n The corresponding total number of samplings.

[0089] 2) Based on the estimated time t n,i The azimuth angle corresponding to the passive sensor and at time t n,i The azimuth angle θ actually collected by the passive sensor n,i Determine the average azimuth estimation error ε corresponding to the passive sensor. n,θ , is represented as:

[0090]

[0091] Optionally, based on the first time t0, the first pitch angle corresponding to the passive sensor. and the first pitch angle change rate Determine the average pitch angle estimation error corresponding to the passive sensor. include:

[0092] 1) The first pitch angle corresponding to the passive sensor based on the first time t0. and the first pitch angle change rate Estimate at time t n,i The azimuth angle corresponding to the passive sensor Represented as:

[0093]

[0094] Where i = 1, 2, ..., M n , t n,i M represents the i-th actual sampling time of the passive sensor. n This indicates a passive sensor z. n The corresponding total number of samplings.

[0095] 2) Based on the estimated time t n,i The azimuth angle corresponding to the passive sensor and at time t n,i The azimuth angle actually collected by the passive sensor Determine the average azimuth estimation error corresponding to the passive sensor. Represented as:

[0096]

[0097] This method can reduce the error caused by linear fitting and improve the accuracy of the positioning results.

[0098] S104. The first sampling position, first motion speed and first angle information corresponding to the passive sensor are sent to the information fusion center so that the information fusion center can determine the position and speed of the target at the first moment based on the first sampling position, first motion speed and first angle information corresponding to the passive sensor.

[0099] Optionally, any information acquisition node can send the first sampling position, first motion speed, and first angle information corresponding to the passive sensor to the information fusion center in a time-division manner, or simultaneously. Different information acquisition nodes can send the first sampling position, first motion speed, and first angle information corresponding to their respective passive sensors to the information fusion center in a time-division manner, or simultaneously.

[0100] This invention provides a target localization method based on multi-station long-term passive angle measurement, applied to an information acquisition node equipped with passive sensors. The method acquires the target's angle information, sampling position, and sampling time collected by the passive sensors within a preset time period. Based on the sampling position and sampling time, it estimates the first sampling position and first velocity corresponding to the passive sensors at a first moment. Furthermore, based on the target's angle information and sampling time collected by the passive sensors, it estimates the first angle information of the target corresponding to the passive sensors at the first moment. This achieves linear fitting of both the sensor position information and the sensor acquisition results. During the linear model fitting process, it can reduce measurement errors to a certain extent. To reduce the impact of measurement errors on positioning accuracy and improve the accuracy of positioning results, further, by sending the first sampling position, first motion velocity, and first angle information corresponding to the passive sensor to the information fusion center, the information fusion center can estimate the target's position and velocity at the first moment based on the first sampling position, first motion velocity, and first angle information corresponding to the passive sensor. This achieves the goal of only transmitting the linearly fitted parameters to the information fusion center during data transmission, thereby greatly reducing the communication volume and ensuring that the communication volume does not increase with the number of sensor samplings over a period of time, thus improving communication efficiency and quality. Transmitting data after linear fitting enables multiple sensors to work in asynchronous sampling scenarios, increasing the application scope and improving the user experience.

[0101] Figure 2 This is a flowchart illustrating another target localization method based on multi-station long-term passive angle measurement provided by the present invention. This method is applied in an information fusion center, such as... Figure 2 As shown, the method includes:

[0102] S201. Obtain the first sampling position, first motion speed and first angle information corresponding to the passive sensor at the first moment sent by multiple information acquisition nodes.

[0103] The first sampling position and the first movement speed are determined by each information acquisition node based on the sampling position and sampling time of the corresponding passive sensor within the preset time period.

[0104] The first angle information is determined by each information acquisition node based on the angle of the target and the sampling time acquired by the corresponding passive sensor within the preset time period.

[0105] Optionally, the sampling times of multiple information collection nodes within the same preset time period can be the same or different, and the number of samples can be the same or different.

[0106] Optionally, the information fusion center can acquire data sent by multiple information collection nodes simultaneously, or it can acquire data sent by different information collection nodes at different times.

[0107] Optionally, the information fusion center can simultaneously acquire multiple types of data sent by the same information collection node, or it can acquire multiple types of data sent by the same information collection node at different times.

[0108] It should be noted that the information fusion center can passively receive data sent by the information collection nodes, or it can actively acquire relevant data corresponding to each information collection node. When the information fusion center actively acquires data, the information collection nodes do not need to actively send data. This solution also falls within the protection scope of this invention, therefore, for the sake of brevity, it will not be elaborated upon here.

[0109] S202. Based on the first sampling position, first motion speed and first angle information corresponding to each passive sensor, determine the position and speed of the target at the first moment.

[0110] Optionally, the target's first angle information includes: the target's first azimuth angle, first azimuth angle rate of change, first pitch angle, and first pitch angle rate of change.

[0111] Based on the first sampling position, first motion velocity, and first angle information corresponding to each passive sensor, the position and velocity of the target at the first moment are determined, including:

[0112] Based on the first sampling position, first velocity, first azimuth angle, first azimuth angle change rate, first pitch angle, and first pitch angle change rate corresponding to each passive sensor, the target's position and velocity at the first moment are determined, expressed as follows:

[0113]

[0114]

[0115] E0 = [e 0,1 ,e 0,2 ,…,e 0,N ]

[0116]

[0117] D0 = blkdiag(e 0,1 ,e 0,2 ,…,e 0,N )

[0118]

[0119]

[0120]

[0121]

[0122]

[0123]

[0124]

[0125]

[0126]

[0127]

[0128] Where N represents the number of passive sensors, 1 N I represents a vector of length N consisting entirely of 1s, I3 represents an identity matrix of dimension 3×3, and e 0,1 Let Ez1 represent the first direction vector formed by the first azimuth angle and the first pitch angle of the first passive sensor z1, and let E0 represent the matrix formed by the first direction vectors of N passive sensors, with a dimension of 3×N. This represents the vector formed by the distance estimates between the target and N passive sensors. The dimension of P0 is N×1, and P0 represents a matrix composed of the first sampling positions of N passive sensors. The dimension of P0 is 3×N. Let D0 represent the first sampling position of the first passive sensor, D0 represent the matrix composed of the first direction vectors of N passive sensors, blkdiag(·) represents the operation of generating a block diagonal matrix, and the dimension of D0 is 3N×N. Let p0 represent the vector composed of the first sampling positions of N passive sensors, and the dimension of p0 is 3N×1. a v represents the estimate of the rate of change of distance between N passive sensors and the target at the first moment. e This represents a vector composed of the first direction vector and the first motion velocity of N passive sensors. This represents the vector formed by the first azimuth angle and the first pitch angle of the first passive sensor. χ represents the partial derivative matrix of the first direction vector of the first passive sensor with respect to the first azimuth and first elevation angles of the first passive sensor, and χ represents the matrix of N passive sensors. sum vector The resulting matrix, χ, has a dimension of 3×N. V represents the first velocity of the nth passive sensor. p V represents a matrix consisting of the first motion velocities of N passive sensors. pThe dimension is 3×N. Indicates the Kronecker product. This represents the estimated position of the target at the first moment. This represents the velocity estimate of the target at the first moment.

[0129] For example, based on the first sampling position, first velocity, first azimuth angle, first azimuth angle change rate, first pitch angle, and first pitch angle change rate corresponding to N passive sensors, the position and velocity of the target at the first moment are determined, including:

[0130] 1) Estimate the target's position at time t0 based on the first sampling position, first velocity, first azimuth angle, first azimuth angle change rate, first pitch angle, and first pitch angle change rate corresponding to N passive sensors. and parameter vector Represented as:

[0131]

[0132]

[0133] E0 = [e 0,1 ,e 0,2 ,…,e 0,N ]

[0134]

[0135] D0 = blkdiag(e 0,1 ,e 0,2 ,…,e 0,N )

[0136]

[0137]

[0138]

[0139] Among them, 1 N Represents a vector of length N consisting entirely of 1s, matrix E0 has dimensions 3×N, and parameter vectors. The dimension of the matrix is ​​N×1, the dimension of matrix P0 is 3×N, the dimension of matrix D0 is 3N×N, and blkdiag(·) represents the operation of generating a block diagonal matrix. The dimension of vector p0 is 3N×1. It represents the Kronecker product.

[0140] 2) Based on the passive sensor z n The corresponding first sampling position First velocity First azimuth angle Rate of change of first azimuth angle First pitch angle and the first pitch angle change rate The position equation of the target at time t is constructed as follows:

[0141]

[0142] Where g0 represents the position of the target at time t0, v g This represents the velocity of the target at time t0. α 0,n rate of change, α 0,n This indicates the relationship between time t0 and the passive sensor z. n Distance estimation.

[0143] 3) Based on the error of the target position estimation results at time t corresponding to each passive sensor, determine the target error term, expressed as:

[0144]

[0145]

[0146]

[0147] in, Let represent the error in the estimation of the target's position at time t by N passive sensors. This indicates a passive sensor z. n The error in estimating the target's position at time t.

[0148] 4) Let ε a Mid-position error part Given a value of 0, establish an equation regarding the target position g0 and the parameter vector α0, expressed as:

[0149]

[0150] 5) Solve equation U to obtain the target's position estimate at time t0. The estimation results of the parameter vector α0 Represented as:

[0151]

[0152]

[0153] Figure 3 A schematic diagram of a target positioning system based on multi-station long-term passive angle measurement provided by the present invention is shown below. Figure 3 As shown, the system includes multiple information collection nodes 31 and an information fusion center 32.

[0154] Among them, passive sensors are deployed in information collection node 31.

[0155] Each information collection node 31 is connected to the information fusion center 32 via communication.

[0156] Each information acquisition node 31 is used to acquire the angle information of the target collected by the passive sensor within a preset time period, as well as the sampling position and sampling time of the passive sensor.

[0157] Each information acquisition node 31 is also used to estimate the first sampling position and the first motion velocity of the passive sensor at the first moment based on the sampling position and sampling time of the passive sensor.

[0158] Each information acquisition node 31 is also used to estimate the first angle information of the target corresponding to the passive sensor at the first moment, based on the angle information of the target acquired by the passive sensor and the sampling time.

[0159] Each information acquisition node 31 is also used to send the first sampling position, first motion speed and first angle information corresponding to the passive sensor to the information fusion center 32.

[0160] The information fusion center 32 is used to estimate the position and velocity of the target at the first moment based on the first sampling position, first motion velocity and first angle information corresponding to the passive sensor.

[0161] Optionally, each information acquisition node 31 is also used to determine whether the total number of samplings corresponding to the corresponding passive sensor is greater than or equal to a first threshold. If so, the average azimuth angle estimation error corresponding to the corresponding passive sensor is determined based on the first azimuth angle and the first azimuth angle change rate corresponding to the corresponding passive sensor at the first moment, and / or the average pitch angle estimation error corresponding to the corresponding passive sensor is determined based on the first pitch angle and the first pitch angle change rate corresponding to the corresponding passive sensor at the first moment. If the average azimuth angle estimation error is greater than a second threshold, and / or if the average pitch angle estimation error is greater than a third threshold, the first angle information of the target corresponding to the corresponding passive sensor at the first moment is re-estimated based on the target angle information and sampling time acquired by the corresponding passive sensor.

[0162] Optionally, the information acquisition node 31 includes an angle information acquisition module 311, a position information acquisition module 312, a time information acquisition module 313, and an information processor 314.

[0163] The angle information acquisition module 311 includes a passive sensor 3111.

[0164] The angle information acquisition module 311 is used to sample the angle information of the target within a preset time period using a passive sensor 3111.

[0165] The location information acquisition module 312 is used to acquire the sampling position of the passive sensor 3111.

[0166] The time information acquisition module 313 is used to acquire the sampling time of the passive sensor 3111.

[0167] The information processor 314 is used to estimate the first sampling position and first motion velocity corresponding to the passive sensor 3111 at a first moment based on the sampling position and sampling time of the passive sensor 3111; to estimate the first angle information of the target corresponding to the passive sensor 3111 at a first moment based on the angle information of the target collected by the passive sensor 3111 and the sampling time; and to send the first sampling position, first motion velocity and first angle information corresponding to the passive sensor 3111 to the information fusion center 32.

[0168] The beneficial effects of different embodiments of the present invention can be used as a reference for each other, and will not be elaborated here.

[0169] To further demonstrate the beneficial effects of the present invention, a set of simulation data is also provided. The simulation results are described in detail below.

[0170] 1. Simulation conditions and content

[0171] The simulation uses an Intel Core i7 6500U CPU with a clock speed of 2.50GHz, 8.0GB of memory, a 64-bit operating system, Microsoft Windows 10 Professional Edition, and MATLAB 2020a simulation software.

[0172] Set the target's position coordinates at time t0 as g0 = (0, 100, 1000) meters and its velocity as v. g = (200, 100, 0) meters per second. There are N = 4 passive sensors, and their position coordinates at time t0 are p... 0,1 = (1000, 1000, 0) meters, p 0,2 = (1000, 2000, 0) meters, p 0,3 = (2000, 1000, 0) meters, p 0,4 = (1500, 1500, 0) meters, the speeds of the sensors are v p,1 = (-100,0,0) meters per second, v p,2 = (-100, -80, 0) meters per second, v p,3 = (-100, -50, 0) meters per second, v p,4 = (-100, -60, 0) meters per second. Assume the positioning error of the passive sensor follows a zero-mean Gaussian distribution, and the error covariance matrix R... s,n =diag(102 10 2 10 2 ), n=1,2,3,4; assume that the azimuth and elevation measurement errors both follow a zero-mean Gaussian distribution, collectively referred to as angle measurement errors, with a standard deviation of 1°. Set the sampling interval for each sensor to 20ms, and increase the number of samples from 2 to 100 (i.e., increase the sampling time from 40ms to 2s). Perform 20 Monte Carlo experiments after each change in the number of samples. Define the root mean square error of the target position estimation to measure the target's positioning accuracy; its expression is:

[0173]

[0174] in, This represents the estimation of the target location coordinates g0 by the positioning algorithm, M represents the Monte Carlo iteration, and ||·|| represents the 2-norm operation. Assuming that each data point transmitted from the information acquisition node to the information fusion center uses a 64-bit double-precision floating-point format, the communication volume required by this invention and existing technologies is compared under different sampling numbers.

[0175] The positioning accuracy and corresponding communication requirements of this invention are compared with those of existing dual-machine passive positioning methods based on azimuth and elevation angle information through simulation. For example, the communication requirements are... Figure 4 As shown, the target positioning accuracy is compared to... Figure 5 As shown.

[0176] 2. Simulation Result Analysis

[0177] Reference Figure 4 The horizontal axis represents the number of samples, in seconds; the vertical axis represents the data transmission volume required by a single data acquisition node, in bytes. Figure 4 It can be seen that in existing technologies, the average communication volume of a single sensor increases linearly with the number of samplings. This is because the communication volume of a single measurement in existing technologies is fixed, and the communication volume increases linearly with the number of samplings. In this invention, the average communication volume of a single sensor remains constant at 80 bytes when the number of samplings is between 2 and 60. When the number of samplings exceeds 60, due to the increased nonlinearity of angle measurement, the error in estimating the angle measurement parameters using linear regression exceeds the threshold. Therefore, linear regression is performed on segmented data to improve accuracy, and the corresponding communication volume doubles. After the number of samplings exceeds 60, the average communication volume of a single sensor approaches 160 bytes.

[0178] Reference Figure 5 The horizontal axis represents the number of samples, in units of times, and the vertical axis represents the RMSE of the target location estimate, in units of meters. From Figure 5It can be seen that when the number of samples is between 2 and 20, the accuracy of target position estimation by the existing technology and the present invention is very close. As the number of samples increases, when the number of samples is between 20 and 60, the accuracy of target position estimation by the present invention is better than that by the existing technology. When the number of samples is 60, the RMSE of target position estimation by the present invention is about 20m, while the RMSE of target position estimation by the existing technology is about 35m. When the number of samples exceeds 60, due to the segmentation of the angle measurement parameters, the RMSE of target position estimation by the present invention increases slightly, but it is still less than that of target position estimation by the existing technology. Within the range of 20 to 100 samples, the accuracy of target position estimation by the present invention is improved compared to the existing technology.

[0179] In summary, simulations have demonstrated that the method of this invention can locate the target, while improving the target location accuracy and reducing communication volume compared to existing technologies.

[0180] This invention also provides an electronic device, including a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other via the communication bus.

[0181] Memory, used to store computer programs;

[0182] When a processor executes a program stored in memory, it implements the steps provided in the above method embodiments.

[0183] The communication interface is used for communication between the aforementioned electronic devices and other devices.

[0184] The present invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps provided in the above-described method embodiments.

[0185] For the embodiments of the device / electronic device / storage medium, since they are basically similar to the method embodiments, the description is relatively simple. For specific details and beneficial effects, please refer to the description of the method embodiments.

[0186] The terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0187] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. In addition, those skilled in the art can combine and integrate the different embodiments or examples described in this specification.

[0188] The above description, in conjunction with specific preferred embodiments, provides a further detailed explanation of the present invention. It should not be construed that the specific implementation of the present invention is limited to these descriptions. For those skilled in the art, various simple deductions or substitutions can be made without departing from the concept of the present invention, and all such modifications and substitutions should be considered within the scope of protection of the present invention.

Claims

1. A target localization method based on multi-station long-term passive angle measurement, characterized in that, This technology is applied to information acquisition nodes, which are equipped with passive sensors. The method includes: The passive sensor acquires the angle information of the target within a preset time period, as well as the sampling position and sampling time of the passive sensor. Based on the sampling position and sampling time of the passive sensor, estimate the first sampling position and first motion velocity corresponding to the passive sensor at the first moment; Based on the target angle information and sampling time collected by the passive sensor, the first angle information of the target corresponding to the passive sensor at the first moment is estimated. The first sampling position, first motion speed, and first angle information corresponding to the passive sensor are sent to the information fusion center, so that the information fusion center can estimate the position and speed of the target at the first moment based on the first sampling position, first motion speed, and first angle information corresponding to the passive sensor. Before sending the first sampling position, first motion speed, and first angle information corresponding to the passive sensor to the information fusion center, the method further includes: Determine whether the total number of samplings corresponding to the passive sensor is greater than or equal to a first threshold. If so, determine the average azimuth angle estimation error corresponding to the passive sensor based on the first azimuth angle and the first azimuth angle change rate corresponding to the passive sensor at the first moment, and / or determine the average pitch angle estimation error corresponding to the passive sensor based on the first pitch angle and the first pitch angle change rate corresponding to the passive sensor at the first moment. If the average azimuth angle estimation error is greater than the second threshold, and / or if the average pitch angle estimation error is greater than the third threshold, then the first angle information of the target corresponding to the passive sensor at the first moment is re-estimated based on the target angle information and sampling time collected by the passive sensor. Specifically, based on the target angle information and sampling time collected by the passive sensor, estimating the first azimuth angle and the rate of change of the first azimuth angle of the target corresponding to the passive sensor at the first moment includes: For passive sensors According to passive sensors The angle of the target and the sampling time are used to estimate the first moment. passive sensor The first azimuth angle corresponding to the target and the rate of change of the first azimuth angle , is represented as: in, Indicates that it is made of passive sensor of The measurement time of this measurement and the first moment The matrix formed by the differences, Indicates length is A vector of all 1s This represents the summation operation. Indicates passive sensor Total number of samplings, Indicates that it is made of passive sensor of The target azimuth angle collected in the second time The vector formed Indicates passive sensor The The azimuth angle of the target collected in this second acquisition. Indicates passive sensor The Sampling time during the next sampling Indicates that it is made of passive sensor of Sampling time of the next sample The vector formed Indicates passive sensor of Sampling time of the next sample With the first sampling time The mean of the time difference, Indicates passive sensor of Sampling time of the next sample With the first sampling time The variance.

2. The method according to claim 1, characterized in that, The step of estimating the first sampling position and first motion velocity corresponding to the passive sensor at the first moment based on the sampling position and sampling time of the passive sensor includes: For passive sensors According to passive sensors The sampling location and sampling time are used to estimate the first moment. passive sensor The corresponding first sampling position and first velocity , is represented as: in, The dimension is The identity matrix, The dimension is The matrix, Indicates passive sensor Total number of samplings, Indicates passive sensor The sampling location during the first sampling. For passive sensors of A vector consisting of sampling positions. The dimension is , Indicates passive sensor The sampling time during the first sampling. Indicates transpose. For passive sensors The corresponding first sampling position and first velocity The vector formed.

3. The method according to any one of claims 1-2, characterized in that, The first angle information of the target includes: the first azimuth angle, the first azimuth angle change rate, the first pitch angle, and the first pitch angle change rate of the target.

4. The method according to claim 3, characterized in that, Based on the target angle information and sampling time collected by the passive sensor, the first pitch angle and the rate of change of the first pitch angle of the target corresponding to the passive sensor at the first moment are estimated, including: For passive sensors According to passive sensors The angle of the target and the sampling time are used to estimate the first moment. passive sensor The corresponding first pitch angle of the target and the first pitch angle change rate , represented as: in, Indicates that it is made of passive sensor of The measurement time of this measurement and the first moment A matrix formed by the differences, Indicates length is A vector of all 1s This represents the summation operation. Indicates passive sensor Total number of samplings, Indicates that it is made of passive sensor of The target elevation angle for the second acquisition The vector formed Indicates passive sensor The The pitch angle of the target being collected next. Indicates passive sensor The Sampling time during the next sampling Indicates that it is made of passive sensor of Sampling time of the next sample The vector formed Indicates passive sensor of Sampling time of the next sample With the first sampling time The mean of the time difference, Indicates passive sensor of Sampling time of the next sample With the first sampling time The variance.

5. A target localization method based on multi-station long-term passive angle measurement, characterized in that, Applied to an information fusion center, the method includes: The system receives first sampling position, first movement speed, and first angle information corresponding to a passive sensor at a first moment from multiple information acquisition nodes. The first sampling position and first movement speed are determined by each information acquisition node based on the sampling position and sampling time of the corresponding passive sensor within a preset time period. The first angle information is determined by each information acquisition node based on the angle of the target collected by the corresponding passive sensor within the preset time period and the sampling time. Based on the first sampling position, first motion speed, and first angle information corresponding to each of the passive sensors, the position and velocity of the target at the first moment are estimated; The information acquisition node is processed using the target positioning method based on multi-station long-term passive angle measurement as described in any one of claims 1-4.

6. The method according to claim 5, characterized in that, The first angle information of the target includes: the target's first azimuth angle, first azimuth angle change rate, first pitch angle, and first pitch angle change rate. The step of estimating the position and velocity of the target at the first moment based on the first sampling position, first motion velocity, and first angle information corresponding to each of the passive sensors includes: Based on the first sampling position, first velocity, first azimuth angle, first azimuth angle change rate, first pitch angle, and first pitch angle change rate corresponding to each passive sensor, the position and velocity of the target at the first moment are estimated, and expressed as follows: in, Indicates the number of passive sensors. Indicates length is A vector consisting entirely of 1s The dimension is The identity matrix, This indicates that the first passive sensor The first direction vector formed by the first azimuth angle and the first elevation angle Indicates by A matrix composed of the first direction vectors of the passive sensors. The dimension is , Indicates the relationship between the target and A vector composed of distance estimates from each passive sensor. The dimension is , Indicates by A matrix consisting of the first sampling positions of each passive sensor. The dimension is , This indicates the first sampling position of the first passive sensor. Indicates by A matrix composed of the first direction vectors of the passive sensors. This represents the operation of generating a block diagonal matrix. The dimension is , Indicates by A vector formed by the first sampling positions of each passive sensor. The dimension is , Indicates to Estimation of the rate of change of distance between the passive sensor and the target at the first moment. Indicates by The vector formed by the first direction vector and the first motion velocity of each passive sensor This represents the vector formed by the first azimuth angle and the first pitch angle of the first passive sensor. Let represent the partial derivative matrix of the first direction vector of the first passive sensor with respect to the first azimuth angle and the first elevation angle of the first passive sensor. Indicates by A matrix of passive sensors sum vector The matrix formed The dimension is , Indicates the first The first motion speed of the passive sensor Indicates by A matrix composed of the first motion velocities of the passive sensors. The dimension is , Indicates the Kronecker product. This represents the estimated position of the target at the first moment. This represents the velocity estimate of the target at the first moment.

7. A target positioning system based on multi-station long-term passive angle measurement, characterized in that, It includes multiple information acquisition nodes and an information fusion center, wherein passive sensors are deployed in the information acquisition nodes. Each of the aforementioned information collection nodes is communicatively connected to the information fusion center; Each of the aforementioned information acquisition nodes is used to acquire the angle information of the target collected by the corresponding passive sensor within a preset time period, as well as the sampling position and sampling time of the corresponding passive sensor; Each of the aforementioned information acquisition nodes is further configured to estimate, based on the sampling position and sampling time of the corresponding passive sensor, the first sampling position and the first motion velocity corresponding to the corresponding passive sensor at the first moment; Each of the aforementioned information acquisition nodes is further configured to estimate the first angle information of the target corresponding to the corresponding passive sensor at the first moment based on the target angle information and sampling time acquired by the corresponding passive sensor; Each of the aforementioned information acquisition nodes is further configured to send the first sampling position, first motion speed, and first angle information corresponding to the respective passive sensor to the information fusion center; The information fusion center is used to estimate the position and velocity of the target at the first moment based on the first sampling position, first motion velocity and first angle information corresponding to the corresponding passive sensor; The information acquisition node is processed using the target positioning method based on multi-station long-term passive angle measurement as described in any one of claims 1-4.

8. The system according to claim 7, characterized in that, include: Each of the aforementioned information acquisition nodes is further configured to determine whether the total number of samplings corresponding to the corresponding passive sensor is greater than or equal to a first threshold. If so, the average azimuth angle estimation error corresponding to the corresponding passive sensor is determined based on the first azimuth angle and the first azimuth angle change rate corresponding to the corresponding passive sensor at the first moment, and / or the average pitch angle estimation error corresponding to the corresponding passive sensor is determined based on the first pitch angle and the first pitch angle change rate corresponding to the corresponding passive sensor at the first moment. If the average azimuth angle estimation error is greater than the second threshold, and / or if the average pitch angle estimation error is greater than the third threshold, then the first angle information of the target corresponding to the corresponding passive sensor at the first moment is re-estimated based on the target angle information and sampling time collected by the corresponding passive sensor.

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