Moving target positioning state estimation method based on double passive sensor detection

Abstract

The invention particularly relates to a moving target positioning state estimation method based on double passive sensor detection, and the method comprises the steps: constructing a discrete motion model corresponding to a target based on a speed feature, an acceleration feature and a position feature; constructing an observation model for the target position according to the positions of the two sensor platforms and the detection angle of the target; obtaining an observation result corresponding to the target based on the observation model; based on the discrete motion model, performing one-step state estimation according to the target state at the previous moment, and obtaining a first position estimation result of the target at the current moment; based on the observation model, performing one-step estimation according to the target measurement result at the previous moment, and obtaining a second position estimation result of the target at the current moment; and determining the optimal position estimation of the target at the current moment in combination with the first position estimation result and the second position estimation result. According to the method, the precision and the anti-interference capability of dynamic target positioning are remarkably improved.

Description

Technical Field

[0001] This invention relates to the field of radar technology, and specifically to a method for estimating the localization status of a moving target based on dual passive sensor detection. Background Technology

[0002] In related technologies, target localization technology has become a key component in modern information warfare and aerospace security, serving as a crucial element in command and control, precision strikes, and situational awareness. Compared to active radar systems, passive radar systems have received increasing attention due to their superior stealth and electromagnetic compatibility. Especially in complex electromagnetic environments, the technology of relying on multiple passive radar sensors for coordinated detection has become an important direction for the future development of sensing systems. Passive radar typically observes targets by receiving signals radiated or reflected by the target (such as angles, frequencies, or time differences). Because it does not actively emit signals, targets are difficult to detect, thus greatly improving the system's survivability. However, compared to active radar, passive radar systems have an inherent disadvantage in target localization accuracy, particularly when facing challenges such as dynamic targets, high-noise environments, and observation uncertainties, where traditional filtering methods struggle to achieve ideal state estimation performance.

[0003] Currently, various filtering algorithms have been proposed for the state estimation problem, such as using the Extended Kalman Filter (EKF) for object tracking, the Unscented Kalman Filter (UKF) for state estimation of continuous-time nonlinear systems, and the Particle Filter (PF) algorithm for state estimation of nonlinear systems, particularly for radar passive positioning. These methods alleviate the problems of nonlinear modeling and some observation constraints to a certain extent. However, in practical applications, the observation data acquired by sensors are often affected by multiplicative noise, leading to a severe degradation in observation accuracy and signal quality. Traditional additive noise assumption models are unable to accurately characterize this type of uncertainty, thus affecting the estimation accuracy and stability of the filters.

[0004] It should be noted that the information disclosed in the background section above is only used to enhance the understanding of the background of the present invention, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0005] This invention provides a method for estimating the positioning state of a moving target based on dual passive sensor detection, which can effectively overcome the defects existing in the prior art.

[0006] Other features and advantages of the invention will become apparent from the following detailed description, or may be learned in part by practice of the invention.

[0007] According to a first aspect of the present invention, a method for estimating the localization state of a moving target based on dual passive sensor detection is provided, the method comprising: Based on velocity characteristics, acceleration characteristics, and position characteristics, a discrete motion model corresponding to the target is constructed. Based on the positions of the two sensor platforms and the detection angles towards the target, an observation model for the target position is constructed; and based on the observation model, the observation results corresponding to the target are obtained. Based on the discrete motion model, a one-step state estimation is performed according to the target state at the previous moment to obtain the first position estimation result of the target at the current moment. Based on the observation model, a one-step estimation is performed based on the target measurement results of the previous moment to obtain the second position estimation result of the target at the current moment; By combining the first position estimation result and the second position estimation result, the optimal position estimate of the target at the current moment is determined.

[0008] In some exemplary embodiments, a discrete motion model corresponding to the target is constructed based on velocity characteristics, acceleration characteristics, and position characteristics, including: Based on velocity, acceleration, and position characteristics, a continuous-time motion model of the target is constructed, including:

[0009] in, For speed, For acceleration; For location; The continuous-time to discrete-time transformation matrix is ​​used to discretize the continuous-time motion model to obtain the discrete motion model, including:

[0010] Where G and H are the continuous-time to discrete-time transformation matrices, respectively; .

[0011] In some exemplary embodiments, the method further includes: pre-constructing a continuous-time to discrete-time transformation matrix, including: ;

[0012] Where A and B are coefficient matrices, and T is the sampling period.

[0013] In some exemplary embodiments, an observation model for the target location is constructed based on the positions of the two sensor platforms and the detection angle towards the target, including: Based on the sensor platform's position, detection angle, and target position, configure the measurement equations for the target using the two sensors, including:

[0014] Wherein, the first position corresponding to the first sensor platform is represented as The first detection angle table is The second position corresponding to the second sensor platform indicates The second detection angle is expressed as The target location is represented as ; These are Gaussian white noise; Based on the measurement equations, the observation models corresponding to the two sensor platforms are configured, including:

[0015] Where V represents additive noise; m is a diagonal matrix; D represents multiplicative noise; and C is based on the probe angle configuration. .

[0016] In some exemplary embodiments, based on a discrete motion model, a one-step state estimation is performed according to the target state at the previous moment to obtain the first position estimation result of the target at the current moment, including:

[0017] in, This indicates the result of the first position estimation.

[0018] In some exemplary embodiments, based on the observation model, a second position estimation result of the target at the current time is obtained by performing a one-step estimation based on the target measurement results of the previous time step, including:

[0019] in, This indicates the result of the second position estimation.

[0020] In some exemplary embodiments, determining the optimal position estimate of the target at the current moment by combining the first position estimation result and the second position estimation result includes: Based on the discrete motion model, the covariance matrix is ​​calculated according to the target's state at the previous moment. ,include: ; Based on the discrete motion model, the state comatrix is ​​determined according to the target's state at the previous moment and the corresponding estimate. ,include: ; Based on state comatrix Determine the cross-covariance of the corresponding state prediction error. ,include: ; Based on the observation results from the observation model, and combined with the target measurement results estimated in the next step, the corresponding covariance is calculated. ,include: ; According to covariance State Coma Matrix Determine the gain of the Kalman filter ,include: ; Gain based on Kalman filtering Determine the optimal position estimate of the target at the current moment.

[0021] In some exemplary implementations, the first position result of the one-step state estimation and the gain of the Kalman filter are combined. The second position result of the first-step estimation determines the optimal position estimate of the target at the current moment, including: .

[0022] In some exemplary embodiments, the method further includes: Calculate the error covariance corresponding to the optimal position estimate of the target at the current time. ,include: ; Based on error covariance to state coma Update.

[0023] According to a second aspect of the present invention, a computer program product is provided, on which a computer program is stored, wherein when the computer program is executed by a processor, the above-described method for estimating the positioning state of a moving target based on dual passive sensor detection is implemented.

[0024] According to a third aspect of the present invention, an electronic device is provided, comprising: Processor; and Memory for storing the executable instructions of the processor; The processor is configured to implement the above-described moving target localization state estimation method based on dual passive sensor detection when executing the executable instructions.

[0025] According to a fourth aspect of the present invention, a storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the above-described method for estimating the positioning state of a moving target based on dual passive sensor detection.

[0026] The moving target localization state estimation method based on dual passive sensor detection provided by the embodiments of the present invention constructs a cooperative detection observation model based on dual passive sensors, and implements a modeling method considering multiplicative noise observation errors. Addressing the problem of strong observation uncertainty in practical passive radar systems, an improved state estimation algorithm is implemented, which can significantly improve the accuracy and anti-interference capability of dynamic target localization. By performing a one-step state estimation based on the target state at the previous moment using a discrete motion model to obtain the first position estimation result of the target at the current moment, and performing a one-step estimation based on the target measurement results at the previous moment using an observation model to obtain the second position estimation result of the target at the current moment, the optimal position estimation of the target at the current moment can be determined based on the first and second position estimation results. It can effectively overcome the problems of poor convergence and poor stability of traditional Kalman filtering methods under non-ideal observation conditions by using the minimum mean square error criterion and fusing multiplicative and additive noise information, thus exhibiting stronger practicality and reliability.

[0027] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit the invention. Attached Figure Description

[0028] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention. It is obvious that the drawings described below are merely some embodiments of the invention, and those skilled in the art can obtain other drawings based on these drawings without any inventive effort.

[0029] Figure 1 The diagram illustrates an exemplary embodiment of the present invention: a method for estimating the localization state of a moving target based on dual passive sensor detection. Figure 2 This diagram illustrates a dual-machine passive detection and positioning orientation relationship according to an exemplary embodiment of the present invention. Figure 3 This diagram schematically illustrates a single-step prediction error according to an exemplary embodiment of the present invention. Figure 4 This diagram schematically illustrates a trajectory prediction method according to an exemplary embodiment of the present invention. Figure 5 This schematic diagram illustrates the composition of an electronic device according to an exemplary embodiment of the present invention. Detailed Implementation

[0030] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, they are provided so that the invention will be more comprehensive and complete, and will fully convey the concept of the exemplary embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

[0031] Furthermore, the accompanying drawings are merely illustrative of the invention and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted. Some block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor devices and / or microcontroller devices.

[0032] To address the shortcomings and deficiencies of existing technologies, this example embodiment provides a method for estimating the localization state of a moving target based on dual passive sensor detection, which can be applied to application scenarios involving dual-platform passive detection. (Reference) Figure 1 As shown, the method includes: Step S11: Based on velocity characteristics, acceleration characteristics, and position characteristics, construct a discrete motion model corresponding to the target; Step S12: Based on the positions of the two sensor platforms and the detection angle for the target, construct an observation model for the target position; and obtain the observation results corresponding to the target based on the observation model. Step S13: Based on the discrete motion model, perform a one-step state estimation according to the target state at the previous moment to obtain the first position estimation result of the target at the current moment; Step S14: Based on the observation model, perform a one-step estimation based on the target measurement results of the previous moment to obtain the second position estimation result of the target at the current moment; Step S15: Combine the first position estimation result and the second position estimation result to determine the optimal position estimate of the target at the current moment.

[0033] The following will describe in more detail each step of the moving target localization state estimation method based on dual passive sensor detection in this exemplary embodiment, with reference to the accompanying drawings and embodiments.

[0034] In step S11, a discrete motion model corresponding to the target is constructed based on velocity characteristics, acceleration characteristics, and position characteristics.

[0035] For example, step S11 described above may specifically include: Step S21: Construct a continuous-time motion model of the target based on its velocity characteristics, acceleration characteristics, and position characteristics; Step S22: Discretize the continuous-time motion model using the continuous-time to discrete-time transformation matrix to obtain the discrete motion model.

[0036] Specifically, in practical applications, considering two passive radars with known position coordinates, the angle of the target relative to each radar is obtained. Using the two angles and the two radar coordinates, an estimated value of the target's position is calculated, followed by optimal state estimation. The target can be an aircraft, such as a drone, airplane, etc.

[0037] When constructing a motion model of a target, one can first consider a continuous-time motion model of the target, including: (1) in, For the target speed, Acceleration with an unknown target; The location of the target.

[0038] Discretize the above model, and denote... Then we have: (2) set up , ; Using the continuous-time to discrete-time transformation matrix: , (3) in, is the coefficient matrix; T is the sampling period.

[0039] The continuous-time model is transformed into a discrete-time model, and the uncertainty and noise in the target modeling are considered. The discrete-time motion model of the target is obtained as follows: (4) Among them, considering if If the equations of motion are approximated over a continuous time period, the results will be different and the accuracy will be lower.

[0040] In step S12, an observation model for the target position is constructed based on the positions of the two sensor platforms and the detection angle for the target; and the observation results corresponding to the target are obtained based on the observation model.

[0041] For example, in step S12 above, constructing the observation model may include: Step S31: Configure the measurement equations of the two sensors for the target based on the position of the sensor platform, the detection angle, and the target position; Step S32: Configure the observation models corresponding to the two sensor platforms according to the measurement equations.

[0042] Specifically, refer to Figure 2 The dual-machine passive detection and positioning relationship shown is defined as follows: the first position corresponding to the first sensor platform is denoted as... The first detection angle table is The second position corresponding to the second sensor platform indicates The second detection angle is expressed as The target location is represented as Based on the constructed observation model, it can be used to observe moving targets and obtain initial observation results.

[0043] For two sensors, the measurement equations for detecting a target can include: (5) in, These are Gaussian white noise, respectively. , .

[0044] remember , .

[0045] Simplifying the above formula, we get: (6) in, , .

[0046] For the first sensor platform, Mathematical expectation Expanding at the point and retaining the first term, we get: (7) (8) Substituting formulas (7) and (8) into formula (6), we get: (9) remember: ; ; ; The observation equations for the first sensor platform are written as follows: (10) Therefore, based on formulas (9) and (10), the measurement equation corresponding to the first sensor platform can be obtained as follows: (11) in, , .

[0047] Similarly, the measurement equation corresponding to the second sensor platform can be obtained as follows: (12) Based on the measurement equations corresponding to the first and second sensor platforms, an observation model for the two passive sensors is constructed, including: (13) Where V represents additive noise; m is a diagonal matrix; D represents multiplicative noise; and C is based on the probe angle configuration. .

[0048] For example, consider Since it is a random matrix, it is difficult to directly calculate the state estimate. The estimated value, denoted as Perform an equivalent transformation on it: (14) in, yes A dimensional matrix, where the ij-th element is 0.

[0049] Perform transformation processing: using Go take (Extract each of them) At this point, the observation model of the system can be expressed as: (15) in, .

[0050] Additionally, the filtering process can be defined to include the following parameters: Optimal state estimation : (16) One-step state estimation : (17) estimation error : (18) Mean of state x: (19) State correlation matrix : (20) One-step prediction Measurement estimation: (twenty one) State Coma Matrix : (twenty two) Cross-covariance of state prediction error : (twenty three) Covariance of Predicted Measurements : (twenty four) Error covariance : (25) Kalman gain : (26) In step S13, based on the discrete motion model, a one-step state estimation is performed according to the target state at the previous moment to obtain the first position estimation result of the target at the current moment.

[0051] For example, based on a constructed discrete motion model, a one-step state estimation can be performed based on the target's state at the previous moment to obtain the target's state estimation result at the current moment. This state estimation result includes estimates of the target's position, velocity, and acceleration, with the position result configured as the first position estimation result. The formula may include:

[0052]

[0053] (27) in, This indicates the result of the first position estimation.

[0054] In step S14, based on the observation model, a one-step estimation is performed according to the target measurement results of the previous moment to obtain the second position estimation result of the target at the current moment.

[0055] For example, based on the established observation model, a one-step predictive measurement estimate can be performed based on the target measurement results at the previous time step, and the position estimate in the estimation result can be configured as the second position estimate. The formula is expressed as:

[0056]

[0057]

[0058] (28) in, This indicates the result of the second position estimation; This represents the value at position i,j in the multiplicative noise matrix; This represents the additive noise at step K.

[0059] In step S15, the optimal position estimate of the target at the current moment is determined by combining the first position estimation result and the second position estimation result.

[0060] For example, step S15 described above may specifically include: Step S41: Based on the discrete motion model, calculate the corresponding covariance matrix according to the target's state at the previous moment. ; Step S42: Based on the discrete motion model, determine the state comatrix according to the target's state at the previous moment and the corresponding estimate. ; Step S43, based on the state comatrix Determine the cross-covariance of the corresponding state prediction error. ; Step S44: Based on the observation results of the observation model and combined with the target measurement results estimated in the previous step, calculate the corresponding covariance. ; Step S45, based on covariance State Coma Matrix Determine the gain of the Kalman filter ; Step S46, gain based on Kalman filter Determine the optimal position estimate of the target at the current moment.

[0061] Specifically, we can first calculate the state correlation matrix, i.e., the covariance matrix. The formula includes: (29) Then, based on the discrete equations of motion, the state comatrix can be calculated. The formula includes:

[0062] (30) in, Indicates system state noise Additive noise in the observation model The covariance of the two is generally considered to be uncorrelated. ; This represents the reciprocal of the new information covariance from the previous step, as shown in equation (31); Let represent the error covariance of the previous step, as shown in equation (35); The coefficients represent the coefficients when the continuous state space is transformed into a discrete space, as shown in equation (3); Let represent the Kalman filter gain from the previous step, as shown in equation (33).

[0063] Then, the cross-covariance of the state prediction error can be calculated. The formula is expressed as:

[0064]

[0065]

[0066]

[0067]

[0068] (31) Then, the measurement model can be used to calculate the covariance of the predicted measurements. The formula is expressed as:

[0069]

[0070]

[0071]

[0072]

[0073] (32) Then, the gain of the Kalman filter can be calculated. The formula includes: (33) Then, calculate the optimal state estimate. The formula includes:

[0074]

[0075]

[0076] (34) The above steps are used to estimate the optimal location for dual-machine passive cooperative detection.

[0077] For example, the method further includes: Step S15: Calculate the error covariance corresponding to the optimal position estimate of the target at the current time. Based on error covariance to the state comatrix Update.

[0078] Specifically, after estimating the target's optimal position at the current moment, the corresponding error covariance can also be calculated. And the state comatrix This information is then updated so that it can be used to estimate the optimal position for the next time step.

[0079] Specifically, calculate the error covariance. The formula includes:

[0080]

[0081]

[0082]

[0083]

[0084]

[0085]

[0086] (35) Where I represents the identity matrix.

[0087] , in, It is a symmetric matrix. Factor the common factor The following was obtained by submitting the request: ).

[0088] For example, the recursive process for the optimal estimation of dual-machine passive cooperative detection and localization may include: S501, one-step state prediction estimation, the formula includes:

[0089] S502, one-step prediction observation estimation, the formula includes:

[0090] S503, calculate the state correlation matrix, the formula includes:

[0091] S504 calculates the filter gain. The formula includes:

[0092]

[0093] S505, Calculate the state estimate, the formula includes:

[0094] S506, Calculate the error covariance, the formula includes:

[0095] Specifically, the moving target localization state estimation method based on dual passive radar sensors provided by this invention can more realistically characterize the characteristics of actual observation errors by introducing a multiplicative noise term in the system modeling. Furthermore, by designing an improved filtering framework, it can simultaneously handle multiplicative and additive noise during the observation process, thereby improving the accuracy and robustness of target state estimation.

[0096] For example, based on the above model and formula, simulation verification is performed in Matlab to obtain the mean square error of the estimated error and the predicted trajectory plot. Specifically, this includes: Step 1: Initialize the random number seed to ensure the experiment is reproducible.

[0097] Step 2: Set the initial positions of the two detectors and the target position, and set the motion equation of the target according to formula (4).

[0098] Step 3: Set up the observation model of the two detectors according to equation (13).

[0099] Step 4: Predict and estimate the target state using the algorithm flow described in the above embodiment, and compare it with the extended Kalman filter. The mean square error of the estimation is shown in Table 1, and the single-step prediction error is as follows: Figure 3 As shown, the predicted trajectory diagram is as follows: Figure 4 As shown.

[0100] Table 1

[0101] From Table 1, Figure 3 and Figure 4 Simulation results show that the estimation algorithm proposed in this invention has a smaller root mean square error and can better estimate the target state.

[0102] The effectiveness of the proposed method in practical application scenarios was verified by constructing a dual-radar cooperative positioning system and conducting numerical simulations.

[0103] The method provided in this embodiment of the invention has the following beneficial effects: 1) By introducing a multiplicative noise observation error modeling method, an improved state estimation algorithm was designed to address the problem of strong observation uncertainty in practical passive radar systems, which significantly improves the accuracy and anti-interference capability of dynamic target positioning.

[0104] 2) The state estimation method proposed in this invention adopts the minimum mean square error criterion, integrates multiplicative and additive noise information, and establishes a recursive estimation formula, which effectively overcomes the problems of poor convergence and poor stability of the traditional Kalman filter method under non-ideal observation conditions, and has stronger practicality and reliability.

[0105] 3) This invention establishes a scalable dual-platform passive detection simulation environment, verifies the superior performance of the proposed algorithm in dynamic and complex scenarios, provides theoretical support and engineering implementation path for multi-sensor collaborative sensing systems, and has good application prospects.

[0106] It should be noted that the above figures are merely illustrative of the processes included in the method according to exemplary embodiments of the present invention, and are not intended to be limiting. It is readily understood that the processes shown in the above figures do not indicate or limit the temporal order of these processes. Furthermore, it is readily understood that these processes may, for example, be executed synchronously or asynchronously in multiple modules.

[0107] It should be noted that although several modules or units of the device for performing actions have been mentioned in the detailed description above, this division is not mandatory. In fact, according to embodiments of the present invention, the features and functions of two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided and embodied by multiple modules or units.

[0108] Figure 5 A schematic diagram of an electronic device suitable for implementing embodiments of the present invention is shown.

[0109] It should be noted that, Figure 5 The electronic device 1000 shown is merely an example and should not be construed as limiting the functionality and scope of use of the embodiments of the present invention.

[0110] like Figure 5As shown, the electronic device 1000 includes a Central Processing Unit (CPU) 1001, which can perform various appropriate actions and processes based on programs stored in Read-Only Memory (ROM) 1002 or programs loaded from storage section 1008 into Random Access Memory (RAM) 1003. The RAM 1003 also stores various programs and data required for system operation. The CPU 1001, ROM 1002, and RAM 1003 are interconnected via a bus 1004. An Input / Output (I / O) interface 1005 is also connected to the bus 1004. Furthermore, the electronic device 1000 also includes an FPGA device and a System-on-a-Chip (SoC) device.

[0111] The following components are connected to I / O interface 1005: an input section 1006 including a keyboard, mouse, etc.; an output section 1007 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and speakers, etc.; a storage section 1008 including a hard disk, etc.; and a communication section 1009 including a network interface card such as a LAN (Local Area Network) card, modem, etc. The communication section 1009 performs communication processing via a network such as the Internet. A drive 1010 is also connected to I / O interface 1005 as needed. Removable media 1011, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., are installed on drive 1010 as needed so that computer programs read from them can be installed into storage section 1008 as needed.

[0112] In particular, according to embodiments of the present invention, the processes described below with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program carried on a storage medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication section 1009, and / or installed from removable medium 1011. When the computer program is executed by central processing unit (CPU) 1001, it performs various functions defined in the system of this application.

[0113] Specifically, the aforementioned electronic devices can be intelligent electronic devices, such as computers, tablets, etc. These electronic devices can connect to and communicate with the sensor platform, execute the methods described above based on the received data, and output an estimate of the target's position.

[0114] It should be noted that the storage medium shown in the embodiments of the present invention can be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), flash memory, optical fiber, portable compact disc read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In the present invention, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In the present invention, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, wherein computer-readable program code is carried. Such transmitted data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. The computer-readable signal medium can also be any storage medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the storage medium can be transmitted using any suitable medium, including but not limited to wireless, wired, etc., or any suitable combination thereof.

[0115] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0116] The units described in the embodiments of the present invention can be implemented in software or hardware, and the described units can also be located in a processor. The names of these units do not necessarily limit the specific unit itself.

[0117] It should be noted that, as another aspect, this application also provides a storage medium, which may be included in an electronic device or may exist independently without being assembled into the electronic device. The aforementioned storage medium carries one or more programs, which, when executed by an electronic device, cause the electronic device to perform the methods described in the following embodiments. For example, the electronic device may perform... Figure 1 The steps of the method shown.

[0118] In one embodiment, this application provides a computer program product including a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0119] Furthermore, the above figures are merely illustrative of the processes included in the method according to exemplary embodiments of the present invention, and are not intended to be limiting. It is readily understood that the processes shown in the above figures do not indicate or limit the temporal order of these processes. Additionally, it is readily understood that these processes may be executed synchronously or asynchronously, for example, in multiple modules.

[0120] Other embodiments of the invention will readily occur to those skilled in the art upon consideration of the specification and practice of the invention herein. This application is intended to cover any variations, uses, or adaptations of the invention that follow the general principles of the invention and include common knowledge or customary techniques in the art not disclosed herein. The specification and embodiments are to be considered exemplary only, and the true scope and spirit of the invention are indicated by the claims.

[0121] It should be understood that the present invention is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of the invention is limited only by the appended claims.

Claims

1. A method for estimating the localization state of a moving target based on dual passive sensor detection, characterized in that, The method includes: Based on velocity characteristics, acceleration characteristics, and position characteristics, a discrete motion model corresponding to the target is constructed. Based on the positions of the two sensor platforms and the detection angles towards the target, an observation model for the target position is constructed; and based on the observation model, the observation results corresponding to the target are obtained. Based on the discrete motion model, a one-step state estimation is performed according to the target state at the previous moment to obtain the first position estimation result of the target at the current moment. Based on the observation model, a one-step estimation is performed based on the target measurement results of the previous moment to obtain the second position estimation result of the target at the current moment; By combining the first position estimation result and the second position estimation result, the optimal position estimate of the target at the current moment is determined.

2. The method according to claim 1, characterized in that, Based on velocity characteristics, acceleration characteristics, and position characteristics, a discrete motion model corresponding to the target is constructed, including: Based on velocity, acceleration, and position characteristics, a continuous-time motion model of the target is constructed, including: in, For speed, For acceleration; For location; The continuous-time to discrete-time transformation matrix is ​​used to discretize the continuous-time motion model to obtain the discrete motion model, including: Where G and H are the continuous-time to discrete-time transformation matrices, respectively; .

3. The method according to claim 2, characterized in that, The method further includes: pre-constructing a continuous-time to discrete-time transformation matrix, including: ； Where A and B are coefficient matrices, and T is the sampling period.

4. The method according to claim 1, characterized in that, Based on the positions of the two sensor platforms and the detection angle towards the target, an observation model for the target's position is constructed, including: Based on the sensor platform's position, detection angle, and target position, configure the measurement equations for the target using the two sensors, including: Wherein, the first position corresponding to the first sensor platform is represented as The first detection angle table is The second position corresponding to the second sensor platform indicates The second detection angle is expressed as The target location is represented as ; These are Gaussian white noise; Based on the measurement equations, the observation models corresponding to the two sensor platforms are configured, including: Where V represents additive noise; m is a diagonal matrix; D represents multiplicative noise; and C is based on the probe angle configuration. .

5. The method according to claim 1, characterized in that, Based on the discrete motion model, a one-step state estimation is performed according to the target state at the previous moment to obtain the first position estimation result of the target at the current moment, including: in, This indicates the result of the first position estimation.

6. The method according to claim 1, characterized in that, Based on the observation model, a second-step estimation is performed using the target measurement results from the previous moment to obtain the target's second position estimate at the current moment, including: in, This indicates the result of the second position estimation.

7. The method according to claim 1, characterized in that, Combining the first and second position estimation results, the optimal position estimate of the target at the current moment is determined, including: Based on the discrete motion model, the covariance matrix is ​​calculated according to the target's state at the previous moment. ,include: ; Based on the discrete motion model, the state comatrix is ​​determined according to the target's state at the previous moment and the corresponding estimate. ,include: ； Based on state comatrix Determine the cross-covariance of the corresponding state prediction error. ,include: ; Based on the observation results from the observation model, and combined with the target measurement results estimated in the next step, the corresponding covariance is calculated. ,include: ; According to covariance State Coma Matrix Determine the gain of the Kalman filter ,include: ; Gain based on Kalman filtering Determine the optimal position estimate of the target at the current moment.

8. The method according to claim 7, characterized in that, Combining the first position result of the one-step state estimation and the gain of the Kalman filter The second position result of the first-step estimation determines the optimal position estimate of the target at the current moment, including: 。 9. The method according to claim 8, characterized in that, The method further includes: Calculate the error covariance corresponding to the optimal position estimate of the target at the current time. ,include: ; Based on error covariance to state coma Update.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the moving target positioning state estimation method based on dual passive sensor detection as described in any one of claims 1 to 9.

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