A method and apparatus for target tracking of a multistatic passive coherent location radar

Abstract

The application discloses a target tracking method and device of a multi-base external radiation source radar, mainly applied to the field of the multi-base external radiation source radar, and aims to effectively fuse multi-base measurement information and improve target positioning and tracking precision. The method firstly obtains an effective measurement data set of a target, and establishes a multi-base passive detection tracking model based on Bernoulli random finite sets; the tracking model comprises a motion model created based on a target state space and a measurement model created based on an observation space; secondly, Bernoulli prediction is performed on a target state probability density according to the tracking model; then, a target posterior probability density is obtained through a Bernoulli updating algorithm improved based on multi-source information fusion; the Bernoulli updating algorithm improved based on the multi-source information fusion involves complex multiple set integral calculation, and then a Gaussian mixture method is adopted to realize; finally, an accurate estimation of the target state is obtained through track pruning and edge multi-target estimators, and a target track output is formed.

Description

Technical Field

[0001] The present invention relates to the technical field of target tracking, and in particular to a target tracking method and device for a multi-base external radiation source radar. Background Art

[0002] Exo-radiation radar is a special form of passive radar. It uses signals from non-cooperative external sources to opportunistically illuminate targets, receive and process the target's scattered waves, extract relevant measurement information, and ultimately complete target detection, positioning, and tracking. Exo-radiation radar offers numerous advantages, including electromagnetic stealth and strong anti-interference capabilities. Furthermore, the system is capable of operating in all weather conditions and countering stealth targets. However, due to the non-cooperative nature of the exo-radiation source and the complexity of the radio wave propagation environment, radars configured with a single exo-radiation source and a single receiving station suffer from low target detection rates and high false alarm rates, making it difficult to accurately locate and track targets. While collaborative detection using a multi-station network can expand detection range and improve target positioning and tracking performance, it also increases the difficulty and uncertainty of measurement and target sorting. Therefore, developing target tracking methods that effectively integrate multi-source information is crucial for improving the accuracy of target state and position estimation.

[0003] At the same time, because traditional data association-based target tracking algorithms require sorting and pairing measurements and targets, they are prone to problems such as large computational load and combinatorial explosion in complex scenarios, resulting in reduced tracking performance. Target tracking methods based on random finite sets can effectively avoid the complex data association process and achieve simultaneous estimation of target quantity and state. Among them, the Bernoulli filter algorithm based on random finite sets is a Bayesian optimal method for single-target detection and tracking that can be applied in arbitrary clutter backgrounds. However, its standard form is only applicable to target tracking scenarios with single-source observations and cannot handle multi-source measurement updates. Summary of the Invention

[0004] The purpose of the present invention is to solve the problem that the current Bernoulli filtering algorithm based on random finite sets is only applicable to target tracking scenarios with single-source observations and cannot handle multi-source measurement updates. The present invention provides a target tracking method and device for a multi-base exo-radiation source radar.

[0005] The technical solution of the embodiment of the present application is implemented as follows:

[0006] A first aspect of an embodiment of the present application provides a target tracking method for a multi-base exo-radiation radar, comprising:

[0007] Step 1: Collect and process multi-source measurement data of the target at different time points; establish a multi-base passive detection and tracking model based on Bernoulli random finite sets; the tracking model includes a motion model created based on the target state space and a measurement model created based on the observation space;

[0008] Step 2: Perform Bernoulli prediction on the target state probability density at time k according to the tracking model to obtain the prediction result;

[0009] Step 3: Based on the prediction results and the multi-source measurement data, obtain the target posterior probability at time k and the target state Bernoulli prediction rate density at time k by using the Bernoulli update algorithm improved by multi-source information fusion; the Bernoulli update algorithm improved by multi-source information fusion involves complex multi-set integral calculations, which are then implemented using a Gaussian mixture method;

[0010] Step 4: Based on the target posterior probability density, an accurate estimate of the target state at time k is obtained by combining track pruning and edge multi-target estimator.

[0011] Step 5: Repeat steps 2 to 4 until the tracking is completed, and finally output the target track.

[0012] Optionally, step 1 includes:

[0013] The measurement data collected and processed by multiple external radiation sources at different time points are modeled as a random finite set of measurements, where the k-time measurement finite set Expressed as , is the target observation space, s is the number of external radiation sources;

[0014] The target state is modeled as a random finite set of states, and the finite set of target states at time k is expressed as ,in is the target state space, is the target number;

[0015] Based on the physical characteristics and motion laws of the target to be measured, the motion model is determined in combination with the effective state variables in the target state space; the measurement model is determined based on the observation space and observation equations; the observation equations characterize the mapping relationship between the observation variables and the effective state variables.

[0016] Optionally, step 2 includes:

[0017] The probability density of the target state at time k is given by the Bernoulli distribution parameter Expressed as:

[0018]

[0019] in, 、 They are k The probability of target existence at each moment and the probability density distribution when the target exists;

[0020] described Probability density of target state at time Satisfies Bernoulli random finite set distribution , use the target state transfer equation to predict the Bernoulli distribution of the target state at the current k moment Expressed as:

[0021]

[0022]

[0023] in, is the target existence probability, is the reentry probability, is the state distribution of the reentry target.

[0024] Optionally, step 3 includes: using multi-source measurement information fusion technology to update the current k-time target state Bernoulli distribution ;

[0025]

[0026]

[0027] in:

[0028]

[0029]

[0030]

[0031]

[0032] is the detection probability of the target under the i-th external radiation source, is a single measurement value under the i-th external radiation source Generate target state The likelihood function of is the clutter distribution in the i-th external radiation source, is a functional transformation.

[0033] Optionally, the Bernoulli distribution of the target prediction probability density at time k satisfies the Gaussian mixture distribution form:

[0034]

[0035] but k The Bernoulli form of the target posterior probability density after the fusion of multi-source measurement information is used at all times It can be expressed as , the functional transformation in the Bernoulli distribution calculation formula of the target posterior probability at time k can be expressed as:

[0036]

[0037]

[0038] in represents the composite operator product operator, and has:

[0039] .

[0040] Optionally, step 4 includes:

[0041] The track pruning first discards the components whose weights are lower than the preset threshold, and then sorts the remaining Gaussian components by weight, calculates the weighted distances in turn, and adds the weighted distance values ​​within the merging threshold. All Gaussian terms within are merged.

[0042] The probability of target existence When the value is greater than a preset threshold, it is determined that the target to be detected exists;

[0043] In the case that the target to be measured exists, an estimated state of the target to be measured is estimated using an edge multi-target algorithm.

[0044] A second aspect of the embodiment of the present application provides a target tracking device based on an external radiation source radar, comprising: a creation module, a prediction module, an update module, a state estimation module and an iteration module, wherein:

[0045] The creation module is configured to collect and process multi-source measurement data of the target at different time points; establish a multi-base passive detection and tracking model based on a Bernoulli random finite set; the tracking model includes a motion model created based on the target state space and a measurement model created based on the observation space;

[0046] The prediction module is configured to perform Bernoulli prediction on the target state probability density at time k according to the tracking model to obtain a prediction result;

[0047] The update module is configured to obtain the target posterior probability density at time k based on the prediction result and the multi-source measurement data by using an improved Bernoulli update algorithm based on multi-source information fusion; the improved Bernoulli update algorithm based on multi-source information fusion includes a multiple set integral implemented by a Gaussian mixture method;

[0048] The state estimation module is configured to obtain an accurate estimate of the target state at time k based on the target posterior probability density in combination with track pruning and edge multi-target estimator;

[0049] The iteration module is configured to repeatedly execute steps 2 to 4 until the tracking is completed, and finally output the target track.

[0050] A third aspect of an embodiment of the present application provides an electronic device, including a processor and a memory; the memory stores a computer program, wherein the computer program, when executed by the processor, implements the target tracking method of the multi-base external radiation source radar described in the first aspect.

[0051] A fourth aspect of an embodiment of the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method described in the first aspect.

[0052] Compared with the existing technology, the technical solution provided by this application has the following beneficial effects:

[0053] The present invention provides a target tracking method and device for a multi-base exoplanetary radiation source radar. First, a valid measurement data set of the target is obtained, and a multi-base passive detection and tracking model is established based on a Bernoulli random finite set. The tracking model includes a motion model created based on a target state space and a measurement model created based on an observation space. Secondly, according to the tracking model, a Bernoulli prediction is performed on the target state probability density at time k. Then, a Bernoulli update algorithm improved based on multi-source information fusion is used to obtain the target posterior probability density. The Bernoulli update algorithm improved based on multi-source information fusion involves complex multi-set integral calculations, which are then implemented using a Gaussian mixture method. Finally, an accurate estimate of the target state is obtained through track pruning and an edge multi-target estimator, which effectively reduces the convergence time of the tracking algorithm and significantly improves the target tracking accuracy and system tracking performance. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 A schematic diagram of a flow chart of a target tracking method based on an external radiation source radar provided in an embodiment of the present application;

[0055] Figure 2 A schematic diagram of the structure of a target tracking device based on an external radiation source radar provided in an embodiment of the present application;

[0056] Figure 3 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0057] Hereinafter, embodiments of the present application will be described with reference to the accompanying drawings. However, it should be understood that these descriptions are exemplary only and are not intended to limit the scope of the present application. In the detailed description below, for ease of explanation, many specific details are set forth to provide a comprehensive understanding of the embodiments of the present application. However, it is apparent that one or more embodiments may also be implemented without these specific details. In addition, in the following description, descriptions of known structures and technologies are omitted to avoid unnecessarily confusing the concepts of the present application.

[0058] The terms used herein are only for describing specific embodiments and are not intended to limit this application. The terms "comprise," "include," etc. used herein indicate the presence of the features, steps, operations, and / or components, but do not exclude the presence or addition of one or more other features, steps, operations, or components.

[0059] All terms used herein (including technical and scientific terms) have the meanings commonly understood by those skilled in the art unless otherwise defined. It should be noted that the terms used herein should be interpreted as having a meaning consistent with the context of this specification and should not be interpreted in an idealized or overly rigid manner.

[0060] The accompanying drawings show some block diagrams and / or flow charts. It should be understood that some blocks in the block diagrams and / or flow charts, or combinations thereof, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when these instructions are executed by the processor, they can create a device for implementing the functions / operations described in the block diagrams and / or flow charts.

[0061] In some embodiments, see Figure 1 , Figure 1 A flow chart of a target tracking method for a multi-base exo-radiation radar provided in an embodiment of the present application; the target tracking method for a multi-base exo-radiation radar provided in an embodiment of the present application includes:

[0062] Step 1: Collect and process multi-source measurement data of the target at different time points; establish a multi-base passive detection and tracking model based on Bernoulli random finite sets; the tracking model includes a motion model created based on the target state space and a measurement model created based on the observation space.

[0063] In some embodiments, step 1, collecting and processing multi-source measurement data of the target at different time points; establishing a multi-base passive detection and tracking model based on a Bernoulli random finite set, further includes:

[0064] The measurement data collected and processed by multiple external radiation sources at different time points are modeled as a random finite set of measurements, where the k-time measurement finite set Expressed as , is the target observation space, and s is the number of external radiation sources.

[0065] The target state is modeled as a random finite set of states, and the finite set of target states at time k is expressed as ,in is the target state space, is the target number.

[0066] Based on the physical characteristics and motion laws of the target to be measured, the motion model is determined in combination with the effective state variables in the target state space; the measurement model is determined based on the observation space and observation equations; the observation equations represent the mapping relationship between the observation variables and the effective state variables.

[0067] Step 2: Perform Bernoulli prediction on the target state probability density at time k according to the tracking model to obtain a prediction result.

[0068] In some embodiments, step 2, performing a Bernoulli prediction on the target state probability density at time k according to the tracking model to obtain a prediction result, includes:

[0069] The probability density of the target state at time k is given by the Bernoulli distribution parameter express:

[0070]

[0071] in, 、 They are k The probability of target existence at each moment and the probability density distribution when the target exists;

[0072] described Probability density of target state at time Satisfies Bernoulli random finite set distribution , use the target state transfer equation to predict the Bernoulli distribution of the target state at the current k moment Expressed as:

[0073]

[0074]

[0075] in, is the target existence probability, is the reentry probability, is the state distribution of the reentry target.

[0076] Step 3: Based on the prediction results and multi-source measurement data, the target posterior probability density at time k is obtained through the Bernoulli update algorithm improved based on multi-source information fusion; the Bernoulli update algorithm improved based on multi-source information fusion includes multiple set integration implemented by the Gaussian mixture method.

[0077] In some embodiments, step 3 is to obtain the target posterior probability density at time k by using the Bernoulli update algorithm improved by multi-source information fusion; the Bernoulli update algorithm improved by multi-source information fusion involves multiple set integrals that are difficult to calculate, and is then implemented using a Gaussian mixture method, including: using multi-source measurement information fusion to update the Bernoulli distribution of the target state at time k ;

[0078]

[0079]

[0080] in:

[0081]

[0082]

[0083]

[0084]

[0085] is the detection probability of the target under the i-th external radiation source, is a single measurement value under the i-th external radiation source Generate target state The likelihood function of is the clutter distribution in the i-th external radiation source.

[0086] In some embodiments, S130, the target posterior probability density at time k is obtained by an improved Bernoulli update algorithm based on multi-source information fusion; the improved Bernoulli update algorithm based on multi-source information fusion involves multiple set integrals that are difficult to calculate, and is then implemented using a Gaussian mixture method. The calculation formula involves complex multiple set integral operations, which are implemented using the following Gaussian mixture method.

[0087] The Bernoulli distribution of the target prediction probability density at time k satisfies the Gaussian mixture distribution form:

[0088]

[0089] but k Bernoulli form of target posterior probability density for multi-source measurement information fusion at each moment It can be expressed as , the functional transformation in the Bernoulli distribution calculation formula of the target posterior probability at time k can be expressed as:

[0090]

[0091]

[0092] in represents the composite operator product operator, and has:

[0093] .

[0094] Step 4: Obtain an accurate estimate of the target state at time k through track pruning and edge multi-target estimator.

[0095] In some embodiments, track pruning first discards components with weights lower than a preset threshold, then sorts the remaining Gaussian components by weight, calculates weighted distances in sequence, and combines the weighted distance values ​​within the merging threshold. All Gaussian terms within are merged.

[0096] The probability of target existence When the value is greater than the preset threshold, it is determined that the target to be detected exists;

[0097] When the target to be measured exists, the edge multi-target algorithm is used to estimate the estimated state of the target to be measured.

[0098] Step 5: Repeat steps 2 to 4 until the tracking is completed, and finally output the target track.

[0099] The embodiment of the present application obtains a valid measurement data set of the target and establishes a multi-base passive detection and tracking model based on a Bernoulli random finite set. According to the tracking model, a Bernoulli prediction is performed on the target state probability density. Then, the target posterior probability density is obtained through an improved Bernoulli update algorithm based on multi-source information fusion. The improved Bernoulli update algorithm based on multi-source information fusion involves multiple set integrals that are difficult to calculate, and is then implemented using a Gaussian mixture method. Finally, an accurate estimate of the target state is obtained through track pruning and an edge multi-target estimator.

[0100] In some embodiments, see Figure 2 , Figure 2 The schematic diagram of the structure of a target tracking device based on an external radiation source radar provided in an embodiment of the present application. The target tracking device 200 based on an external radiation source radar provided in an embodiment of the present application includes: a creation module 210, a prediction module 220, an update module 230, a state estimation module 240 and an iteration module 250, wherein:

[0101] A creation module 210 is configured to collect and process multi-source measurement data of a target at different time points; establish a multi-base passive detection and tracking model based on a Bernoulli random finite set; the tracking model includes a motion model established based on the target state space and a measurement model established based on the observation space;

[0102] The prediction module 220 is configured to perform a Bernoulli prediction on the target state probability density at time k according to the tracking model to obtain a prediction result;

[0103] An updating module 230 is configured to obtain a target posterior probability density at time k based on the prediction results and multi-source measurement data using an improved Bernoulli update algorithm based on multi-source information fusion; the improved Bernoulli update algorithm based on multi-source information fusion includes a multi-set integral implemented using a Gaussian mixture method;

[0104] a state estimation module 240 configured to obtain an accurate estimate of the target state at time k through track pruning and edge multi-target estimator;

[0105] The iteration module 250 is configured to repeatedly execute steps 2 to 4 until the tracking is completed, and finally output the target navigation.

[0106] In some embodiments, the target state and measurement are modeled as random finite sets, and the target finite state set and measurement finite set at time k are expressed as and ,in is the target state space, is the target observation space, is the target number, s is the number of external radiation sources.

[0107] The creation module 210 is specifically configured to: determine a motion model based on the physical characteristics and motion laws of the target to be measured and in combination with the effective state variables of the target state space;

[0108] The measurement model is determined based on the observation space and the observation equation; the observation equation represents the mapping relationship between the observed variables and the effective state variables.

[0109] In some embodiments, the target state k-time probability density is given by the Bernoulli distribution parameter express:

[0110]

[0111] in, 、 They are k The probability of target existence at each moment and the probability density distribution when the target exists;

[0112] Probability density of target state at time Satisfies Bernoulli random finite set distribution , use the target state transfer equation to predict the Bernoulli distribution of the target state at the current k moment Expressed as:

[0113]

[0114]

[0115] in, is the target existence probability, is the reentry probability, is the state distribution of the reentry target.

[0116] In some embodiments, the target state prediction Bernoulli distribution at time k is updated by fusion of multi-source measurement information. ;

[0117]

[0118]

[0119] in:

[0120]

[0121]

[0122]

[0123]

[0124] For the goal i The detection probability under external radiation sources is For the i A single measurement value under an external radiation source Generate target state The likelihood function of For the i The clutter distribution in the external radiation source.

[0125] In some embodiments, the calculation involves complex multiple set integral operations, which are implemented using the following Gaussian mixture method. The Bernoulli distribution of the target prediction probability density at time k satisfies the Gaussian mixture distribution form:

[0126]

[0127] but k Bernoulli form of target posterior probability density for multi-source measurement information fusion at each moment It can be expressed as , the functional transformation in the Bernoulli distribution calculation formula of the target posterior probability at time k can be expressed as:

[0128]

[0129]

[0130] in represents the composite operator product operator, and has:

[0131] .

[0132] In some embodiments, track pruning first discards components with weights lower than a preset threshold, then sorts the remaining Gaussian components by weight, calculates weighted distances in sequence, and combines the weighted distance values ​​within the merging threshold. All Gaussian terms within are merged.

[0133] The probability of target existence When the value is greater than the preset threshold, it is determined that the target to be detected exists;

[0134] When the target to be measured exists, the edge multi-target algorithm is used to estimate the estimated state of the target to be measured.

[0135] The target tracking device based on external radiation source radar provided in the embodiment of the present application can implement each process in the embodiment corresponding to the above-mentioned target tracking method based on external radiation source radar. To avoid repetition, it will not be repeated here.

[0136] It should be noted that the target tracking device based on external radiation source radar provided in the embodiment of the present application and the target tracking method based on external radiation source radar provided in the embodiment of the present application are based on the same application concept. Therefore, the specific implementation of this embodiment can refer to the implementation of the aforementioned target tracking method based on external radiation source radar, and the repeated parts will not be repeated.

[0137] In some embodiments, see Figure 3 , Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. The electronic device 300 provided in an embodiment of the present application includes a processor 310 and a memory 320; the memory 320 stores a computer program, wherein the computer program, when executed by the processor, implements the above-mentioned target tracking method based on an external radiation source radar.

[0138] Specifically, the processor 310 may include, for example, a general-purpose microprocessor, an instruction set processor and / or a related chipset and / or a dedicated microprocessor (e.g., an application-specific integrated circuit (ASIC)), etc. The processor 310 may also include onboard memory for caching purposes. The processor 310 may be a single processing unit or multiple processing units for executing different actions of the method flow according to the embodiments of the present application.

[0139] Memory 320 can be, for example, any medium capable of containing, storing, conveying, disseminating, or transmitting instructions. For example, memory 320 can include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, components, or communication media. Specific examples of memory 320 include: magnetic storage devices, such as magnetic tape or hard disk drives (HDDs); optical storage devices, such as compact discs (CD-ROMs); random access memory (RAM) or flash memory; and / or wired or wireless communication links.

[0140] This application also provides a computer-readable medium storing a computer program that, when executed by a processor, implements the aforementioned exo-radiator radar-based target tracking method. This computer-readable medium may be included in the device / apparatus / system described in the aforementioned embodiments, or it may exist independently and not incorporated into the device / apparatus / system. The computer-readable medium carries one or more programs that, when executed, implement the method according to the embodiments of this application.

[0141] According to an embodiment of the present application, a computer-readable medium may be a computer-readable signal medium or a computer-readable storage medium or any combination thereof. A computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or component, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present application, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, device, or component. In the present application, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, which carries computer-readable program code. Such a propagated data signal may take a variety of forms, including but not limited to an electromagnetic signal, an optical signal, or any suitable combination thereof. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium that can transmit, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer-readable medium may be transmitted using any suitable medium, including but not limited to wireless, wired, optical cable, radio frequency signals, or any suitable combination thereof.

[0142] Those skilled in the art will appreciate that the features described in the various embodiments and / or claims of the present application may be combined and / or coupled in a variety of ways, even if such combinations or couplings are not explicitly described in the present application. In particular, without departing from the spirit and teachings of the present application, the features described in the various embodiments and / or claims of the present application may be combined and / or coupled in a variety of ways. All of these combinations and / or couplings fall within the scope of the present application. Therefore, the scope of the present application should not be limited to the above-described embodiments, but should be determined not only by the appended claims, but also by the equivalents of the appended claims.

Claims

1. A target tracking method for a multi-base exoplanetary radar, characterized in that: include: Step 1: Collect and process multi-source measurement data of the target at different time points; Establishing a multi-base passive detection and tracking model based on Bernoulli random finite sets; the tracking model includes a motion model created based on the target state space and a measurement model created based on the observation space; Step 2: Perform Bernoulli prediction on the target state probability density at time k according to the tracking model to obtain the prediction result; Step 3: Based on the prediction result and the multi-source measurement data, obtain the target posterior probability density at time k by using an improved Bernoulli update algorithm based on multi-source information fusion; the improved Bernoulli update algorithm based on multi-source information fusion includes a multi-set integral implemented by a Gaussian mixture method; Step 4: Based on the target posterior probability density, track pruning and edge multi-target estimator are combined to obtain an accurate estimate of the target state at time k; Step 5: Repeat steps 2 to 4 until the tracking is completed, and finally output the target track.

2. The target tracking method of the multi-base extraterrestrial radiation source radar according to claim 1, characterized in that: The step 1 comprises: The measurement data collected and processed by multiple external radiation sources at different time points are modeled as a random finite set of measurements, where the k-time measurement finite set Expressed as , is the target observation space, s is the number of external radiation sources; The target state is modeled as a random finite set of states, and the finite set of target states at time k is expressed as ,in is the target state space, is the target number; Based on the physical characteristics and motion laws of the target to be measured, the motion model is determined in combination with the effective state variables in the target state space; the measurement model is determined based on the observation space and observation equations; the observation equations represent the mapping relationship between the observation variables and the effective state variables.

3. The target tracking method of the multi-base extraterrestrial radiation source radar according to claim 2, characterized in that: The step 2 comprises: The probability density of the target state at time k is given by the Bernoulli distribution parameter express: in, 、 They are k The probability of target existence at each moment and the probability density distribution when the target exists; described Probability density of target state at time Satisfies Bernoulli random finite set distribution , use the target state transfer equation to predict the Bernoulli distribution of the target state at the current k moment Expressed as: in, is the target existence probability, is the reentry probability, is the state distribution of the reentry target.

4. The target tracking method of the multi-base extraterrestrial radiation source radar according to claim 3, characterized in that: Step 3 includes: using multi-source measurement information fusion technology to update the current k-time target state Bernoulli distribution ; in: is the detection probability of the target under the i-th external radiation source, is a single measurement value under the i-th external radiation source Generate target state The likelihood function of is the clutter distribution in the i-th external radiation source, is a functional transformation.

5. The target tracking method of the multi-base extraterrestrial radiation source radar according to claim 4, characterized in that: The Bernoulli distribution of the target prediction probability density at time k satisfies the Gaussian mixture distribution form: but k The Bernoulli form of the target posterior probability density after the fusion of multi-source measurement information is used at all times It can be expressed as , the functional transformation in the Bernoulli distribution calculation formula of the target posterior probability at time k can be expressed as: in represents the composite operator product operator, and has: 。 6. The target tracking method of the multi-base extraterrestrial radiation source radar according to claim 1, characterized in that: The step 4 comprises: The track pruning first discards the components whose weights are lower than the preset threshold, and then sorts the remaining Gaussian components by weight, calculates the weighted distances in turn, and adds the weighted distance values ​​within the merging threshold. All Gaussian terms within are merged; The probability of target existence When the value is greater than the preset threshold, it is determined that the target to be detected exists; In the case that the target to be measured exists, an estimated state of the target to be measured is estimated using an edge multi-target algorithm.

7. A target tracking device for a multi-base extraterrestrial radiation source radar, characterized in that: include: Create module, prediction module, update module, state estimation module and iteration module, among which, The creation module is configured to collect and process measurement data of the target at different time points; establish a multi-base passive detection and tracking model based on a Bernoulli random finite set; the tracking model includes a motion model created based on the target state space and a measurement model created based on the observation space; The prediction module is configured to perform Bernoulli prediction on the target state probability density at time k according to the tracking model to obtain a prediction result; The update module is configured to obtain the target posterior probability density at time k based on the prediction result and multi-source measurement data by using an improved Bernoulli update algorithm based on multi-source information fusion; the improved Bernoulli update algorithm based on multi-source information fusion includes a multiple set integral implemented by a Gaussian mixture method; The state estimation module is configured to obtain an accurate estimate of the target state at time k through track pruning and edge multi-target estimator; The iteration module is configured to repeatedly execute steps 2 to 4 until the tracking is completed, and finally output the target track.

8. An electronic device comprising a processor and a memory; the memory stores a computer program, wherein: When the computer program is executed by the processor, the computer program implements the target tracking method of the multistatic exoplanetary emitter radar according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, wherein when the program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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