Air target prediction fusion tracking method based on ground-based GNSS-S radar

Abstract

The invention relates to an aerial target prediction fusion tracking method based on a ground-based GNSS-S radar. The method comprises the following steps: simultaneously obtaining scattered echoes of multiple GNSS satellite navigation signals of an aerial target through the ground-based GNSS-S radar; compressing and processing the scattering echoes to obtain a close-range multi-station movement track; the obtained motion trail is analyzed, and the position and the speed at the close distance are obtained; according to the obtained position and speed, fuzzy prediction is carried out on the long-distance position where the aerial target moves, and a plurality of prediction results are obtained; performing time registration and space registration on the position of each prediction result in the plurality of double-site trace images; and performing signal fusion of a plurality of double-station trace images on the registered prediction results, and selecting the prediction result with the highest signal-to-noise ratio from the multi-station image signal fusion results of each prediction result as the position and speed of the aerial target at the remote distance. According to the invention, the long-distance target detection and tracking capability of the GNSS-S radar detection system is improved.

Description

Technical Field

[0001] The present invention relates to the field of microwave remote sensing technology in the electronic information industry, and in particular to an aerial target prediction fusion tracking method based on a ground-based GNSS-S radar. Background Art

[0002] Aerial target tracking has broad application prospects and significance in the fields of military, civil and scientific research. In the military field, aerial target detection can help the military to timely detect and track enemy aircraft, including airplanes, helicopters, drones, etc., so as to take corresponding defensive measures. This plays an important role in maintaining national security, protecting territorial integrity and ensuring the success of military operations. In the civilian field, aerial target detection can be used for air traffic control to ensure the safe flight of civil aircraft. By detecting and tracking aerial targets, accurate scheduling and control of flights can be achieved, conflicts between flights can be avoided, and the safety of air traffic can be improved.

[0003] The technology of detecting targets based on external radiation source detection of the navigation satellite system (GNSS) has many advantages. First, it can use existing radiation source signals without the need to transmit signals specifically, thus saving spectrum resources. Second, because it uses non-cooperative radiation source signals, it has good concealment and security and is not easily discovered and located by enemy reconnaissance systems. In addition, this technology can also achieve multi-angle simultaneous detection and tracking of targets, and has a high information dimension detection capability.

[0004] With the increase of low-altitude aircraft, the monitoring and control of aerial targets has become an increasingly important part of ensuring urban safety. In order to avoid radiation pollution in cities, the use of external radiation sources for low-altitude aircraft detection is a preferred method. With the completion and improvement of the Beidou satellite navigation system, the number of available external radiation source satellites has increased significantly, but the landing power of the navigation satellite transmission signal is weak, and the detection distance of a single radiation source is limited. Exploring the detection and tracking method of long-distance aerial targets is one of the important development trends based on GNSS external radiation source aerial target detection.

[0005] At present, traditional methods generally use the "detection-tracking-fusion" or "tracking-detection-fusion" method for multi-station radar information. Fusion is used to improve positioning accuracy, but the improvement of the target detection signal-to-noise ratio is limited, resulting in limited detection and tracking capabilities of ground-based GNSS-S radars for long-distance targets. Summary of the invention

[0006] In order to solve the technical problems existing in the above-mentioned prior art, the purpose of the present invention is to provide an aerial target prediction fusion tracking method and electronic equipment based on ground-based GNSS-S radar, which has the characteristics of high precision and strong detection and tracking capabilities.

[0007] To achieve the above-mentioned object of the invention, the present invention provides an aerial target prediction fusion tracking method based on ground-based GNSS-S radar, comprising the following steps:

[0008] Step S1, simultaneously obtaining scattered echoes of multiple GNSS satellite navigation signals of aerial targets through a ground-based GNSS-S radar;

[0009] Step S2, performing range compression and multi-pulse coherent accumulation processing on the scattered echoes of multiple GNSS satellite navigation signals respectively to obtain the short-range multi-station motion trajectory of the aerial target;

[0010] Step S3, analyzing the obtained close-range multi-station motion trajectory of the aerial target to obtain the position and speed of the aerial target at a close distance;

[0011] Step S4, based on the obtained position and speed of the aerial target at a short distance, fuzzy prediction is performed on the movement of the aerial target to a long distance to obtain multiple prediction results;

[0012] Step S5, performing temporal registration and spatial registration on the position of each prediction result in multiple dual-site trace images;

[0013] Step S6, performing signal fusion of multiple dual-station track images on the registered prediction results, and selecting the prediction result with the highest signal-to-noise ratio from the multi-station image signal fusion results of each prediction result as the position and speed of the aerial target at a long distance.

[0014] According to a technical solution of the present invention, it also includes:

[0015] Step S7, based on the position and velocity of the aerial target at a long distance obtained in step S6 as the known position and velocity of the aerial target, repeat steps S4 to S6 to further predict the position and velocity of the aerial target at a longer distance until the signal-to-noise ratio after fusion is difficult to detect.

[0016] According to a technical solution of the present invention, in step S1, the receiving antenna of the ground-based GNSS-S radar includes a direct signal receiving antenna and a scattered signal receiving antenna, and the beam range of the direct signal receiving antenna covers multiple GNSS satellites; according to the radar scattering cross-sectional area of ​​the aerial target, the scattered signal receiving antenna can achieve a signal-to-noise ratio detection of more than 10dB when detecting the target at the closest point after the range compression and the multi-pulse coherent accumulation processing.

[0017] According to a technical solution of the present invention, in the step S2, it specifically includes:

[0018] Step S21, for each scattered echo of the GNSS satellite navigation signal, pulse compression is performed on the scattered channel signal with reference to the GNSS direct signal to achieve range compression;

[0019] Step S22, performing coherent accumulation on the results after range compression at a slow time to form a plurality of dual-site trace images;

[0020] Step S23: extracting the position of the aerial target in each of the dual-station track images based on the coherent accumulation result, and forming a close-range multi-station motion track of the aerial target.

[0021] According to a technical solution of the present invention, in step S3, it specifically includes:

[0022] Step S31, performing temporal registration and spatial registration on the positions of the aerial targets in the plurality of dual-site track images;

[0023] Step S32, determining the three-dimensional position of the aerial target by a multi-station time-delay positioning method;

[0024] Step S33: Based on the three-dimensional position of the aerial target at each moment, obtain the position and speed of the aerial target at a close distance.

[0025] According to a technical solution of the present invention, in the step S4, it specifically includes:

[0026] Step S41, selecting the aerial target to move to a position where the detection signal-to-noise ratio is lower than 10 dB in any single dual-site track image, and extrapolating the motion state of the aerial target based on the position and speed of the aerial target at a close distance;

[0027] Step S42: determine the three-dimensional positions of multiple extrapolated results of the aerial target to form multiple predicted three-dimensional positions.

[0028] According to a technical solution of the present invention, in step S5, it specifically includes:

[0029] Step S51, respectively calculating the position of each predicted three-dimensional position in each of the dual-site trace images;

[0030] Step S52: Perform temporal registration and spatial registration on the position of each predicted three-dimensional position in each of the dual-site trace images.

[0031] According to a technical solution of the present invention, in step S6, it specifically includes:

[0032] Step S61, performing non-coherent accumulation of a plurality of dual-site trace images on the plurality of registered predicted three-dimensional positions respectively;

[0033] Step S62, selecting the predicted three-dimensional position with the highest signal-to-noise ratio from multiple prediction results as the position of the aerial target at a long distance;

[0034] Step S63, calculating the speed of the aerial target at the long distance based on the position and speed of the aerial target at the short distance and the position of the aerial target at the long distance.

[0035] According to one aspect of the present invention, there is provided an electronic device, comprising: one or more processors, one or more memories, and one or more computer programs; wherein the processor is connected to the memory, and the one or more computer programs are stored in the memory, and when the electronic device is running, the processor executes the one or more computer programs stored in the memory, so that the electronic device performs the aerial target prediction fusion tracking method based on ground-based GNSS-S radar as described in any one of the above technical solutions.

[0036] According to one aspect of the present invention, a computer-readable storage medium is provided for storing computer instructions. When the computer instructions are executed by a processor, the method for predicting and fusion tracking of aerial targets based on ground-based GNSS-S radar as described in any of the above technical solutions is implemented.

[0037] Compared with the prior art, the present invention has the following beneficial effects:

[0038] The present invention proposes an aerial target prediction fusion tracking method and electronic equipment based on ground-based GNSS-S radar. Through multi-station information fusion and signal processing technology, the tracking and detection of "tracking-prediction-fusion-detection" is realized, and the multi-station information is used to improve the signal-to-noise ratio, thereby greatly improving the long-distance target detection and tracking capability of the GNSS-S radar detection system. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention, and for ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0040] Figure 1 The overall flow chart of the method for predicting and fusion tracking of aerial targets based on ground-based GNSS-S radar provided in accordance with one embodiment of the present invention is schematically shown;

[0041] Figure 2 is a flowchart schematically showing air target prediction fusion tracking based on ground-based GNSS-S radar provided in accordance with one embodiment of the present invention;

[0042] Figure 3 is a schematic diagram schematically showing the operation of a ground-based GNSS-S radar provided in one embodiment of the present invention;

[0043] Figure 4 is a schematic diagram of a ground-based GNSS-S radar signal preprocessing flow chart provided in accordance with one embodiment of the present invention;

[0044] FIG5 is a schematic diagram schematically showing the calculation of a three-dimensional position by obtaining a plurality of dual-station images according to one embodiment of the present invention;

[0045] FIG6 is a schematic diagram schematically showing signal positions in a plurality of bistatic images calculated according to an embodiment of the present invention;

[0046] Figure 7 It is a diagram schematically showing a comparison of the real three-dimensional position of a target, the position obtained by non-fusion processing, and the position obtained by fusion processing provided in one embodiment of the present invention. DETAILED DESCRIPTION

[0047] The description of the embodiments of this specification should be combined with the corresponding drawings, which should be considered as part of the complete specification. In the drawings, the shape or thickness of the embodiments may be enlarged and indicated for simplification or convenience. Furthermore, the parts of each structure in the drawings will be described separately. It is worth noting that the elements not shown in the drawings or not described in words are in a form known to ordinary technicians in the relevant technical field.

[0048] The description of the embodiments herein and any reference to directions and orientations are only for the convenience of description and are not to be construed as any limitation on the scope of protection of the present invention. The following description of the preferred embodiments may involve combinations of features, which may exist independently or in combination, and the present invention is not particularly limited to the preferred embodiments. The scope of the present invention is defined by the claims.

[0049] like Figure 1 and Figure 2As shown, the present invention provides an aerial target prediction fusion tracking method based on ground-based GNSS-S radar, which uses a ground-based GNSS-S radar to simultaneously obtain scattered echoes of multiple GNSS satellite navigation signals of aerial targets, and performs distance compression and multi-pulse coherent accumulation processing on the scattered echoes of different satellites to obtain multi-station motion trajectories of close-range targets; the obtained target motion trajectory is analyzed to obtain the position and speed of the target; based on the existing target position and speed, the position of the target when it moves to a long distance resulting in a low signal-to-noise ratio and it is difficult to directly obtain the trajectory is fuzzy predicted to obtain multiple prediction results; the position of each prediction result in the multi-station image is temporally and spatially aligned; multi-station image signal fusion is performed on each prediction to improve the signal-to-noise ratio of the GNSS-S radar image, and the prediction result with the highest signal-to-noise ratio is selected from the multi-station fusion results of each prediction as the position and speed of the target at a long distance; the selected high signal-to-noise ratio detection result is used to further predict the position of the target at a farther distance. Traditional methods generally use the "detection-tracking-fusion" or "tracking-detection-fusion" method for multi-station radar information. Fusion is used to improve positioning accuracy, but the improvement of the target detection signal-to-noise ratio is limited. The present invention realizes "tracking-prediction-fusion-detection" and uses multi-station information to improve the signal-to-noise ratio, greatly improving the long-distance target detection and tracking capability of the GNSS-S radar detection system.

[0050] The specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0051] like Figure 1 As shown, the present invention provides an aerial target prediction fusion tracking method based on ground-based GNSS-S radar, comprising the following steps:

[0052] Step S1: simultaneously obtain scattered echoes of multiple GNSS satellite navigation signals of aerial targets through a ground-based GNSS-S radar.

[0053] like Figure 2 As shown, in step S1, a ground-based GNSS-S radar is used to simultaneously obtain scattered echoes of multiple GNSS satellite navigation signals of aerial targets, which are recorded as N here. The ground-based GNSS-S radar includes two antennas: a direct signal receiving antenna and a scattered signal receiving antenna. The direct antenna adopts a commonly used butterfly-shaped GNSS receiving antenna, which can cover multiple GNSS satellites. The scattering antenna area is calculated according to the radar scattering cross-sectional area of ​​the detected target and the nearest detection distance according to the radar equation to ensure that the target scattered signal can achieve a signal-to-noise ratio detection of more than 10dB after range compression and multi-pulse coherent accumulation processing.

[0054] Step S2: performing range compression and multi-pulse coherent accumulation processing on the scattered echoes of multiple GNSS satellite navigation signals respectively to obtain the short-range multi-station motion trajectory of the aerial target.

[0055] like Figure 4 As shown, in step S2, it specifically includes:

[0056] Step S21: For each GNSS signal, pulse compression is performed on the scattered channel signal with the GNSS direct signal as a reference to achieve range compression.

[0057] Step S22: perform coherent accumulation on the results after range compression at a slow speed to form multiple dual-site track images, thereby further improving the target signal-to-noise ratio.

[0058] Step S23: extracting the position of the target in each dual-station track image based on the coherent accumulation result to form a close-range multi-station motion track of the aerial target.

[0059] In this embodiment, range compression and multi-pulse coherent accumulation processing are performed on scattered echoes from different satellites respectively: for each GNSS signal, the GNSS direct signal is used as a reference, and combined with the GNSS satellite status and receiver position obtained by local capture of the navigation signal, pulse compression is performed on the scattered channel signal to achieve range compression; the results after range compression are coherently accumulated at a slow speed to form N dual-site track images, further improving the target signal-to-noise ratio; based on the coherent accumulation results, the position of the target in each dual-site track image is extracted.

[0060] Step S3: Analyze the obtained close-range multi-station motion trajectory of the aerial target to obtain the position and speed of the aerial target at close range.

[0061] As shown in FIG5 , in step S3, it specifically includes:

[0062] Step S31 : performing temporal registration and spatial registration on the positions of aerial targets in a plurality of dual-site track images.

[0063] Step S32: determine the three-dimensional position of the aerial target by a multi-station time-delay positioning method.

[0064] Step S33: Based on the three-dimensional position of the target at each moment, obtain the position and speed of the aerial target.

[0065] In this embodiment, the target positions in N dual-site track images are temporally and spatially aligned. Since the estimation result of each radar node on the target is based on its own coordinate system, the point track sequences of different nodes cannot be subsequently fused. Here, the delay of the scattered signal relative to the direct signal is selected as the standard, and the three-dimensional position of the target is determined by the multi-station delay positioning method; based on the three-dimensional position of the target at each moment, the target position and speed are obtained by fitting. As shown in Figure 5, by Figure 5(a)-Figure 5(c) There are three dual-site trace images respectively. The three-dimensional position of the target is determined by the time-delay position method of the three dual-site trace images as shown in Figure 5(d).

[0066] Step S4: Based on the obtained position and speed of the aerial target at a short distance, fuzzy prediction is performed on the movement of the aerial target to a long distance to obtain multiple prediction results.

[0067] In step S4, it specifically includes:

[0068] Step S41: Select a target to move to any single dual-site track image where the detection signal-to-noise ratio is lower than 10 dB, and extrapolate the target motion state based on the position and velocity of the target at a close distance.

[0069] Step S42: determine the three-dimensional positions of multiple extrapolated results of the aerial target to form multiple predicted three-dimensional positions.

[0070] In this embodiment, the target is selected to move to any single dual-site track image where the detection signal-to-noise ratio is lower than 10dB, and the target motion state is extrapolated based on the position and speed of the target at a close distance; the three-dimensional position of the target M extrapolated results is determined by a multi-station time-delay positioning method. The position of each predicted three-dimensional position in the dual-site track image is calculated, and the calculation result is shown in Figure 6; the target position of the dual-site track image is temporally and spatially registered.

[0071] Step S5, performing temporal registration and spatial registration on the position of each prediction result in the multi-station images;

[0072] In step S5, it specifically includes:

[0073] Step S51, respectively calculating the position of each predicted three-dimensional position in each dual-site track image.

[0074] Step S52: Perform temporal registration and spatial registration on the position of each predicted three-dimensional position in each dual-site trace image.

[0075] In this embodiment, N point trace images are non-coherently accumulated for each of the M prediction results of the spatiotemporal registration at a long distance; the one with the largest signal-to-noise ratio is selected from the M prediction results as the target position at the current moment; and the current target speed is calculated based on the previous target position speed at a short distance and the current target position. Figure 7 As shown in the figure, the solid dots are the true target positions; the asterisks are the target positions obtained using a low signal-to-noise ratio, which can be seen to have diverged and deviate greatly from the true position; the circles are the target positions obtained by the prediction fusion method, which are highly consistent with the true target positions.

[0076] By non-coherently accumulating multiple prediction results, the signal-to-noise ratio can be effectively improved, making the target signal clearer; selecting the prediction result with the largest signal-to-noise ratio as the target position can more accurately detect and identify the target; the target position obtained based on the prediction fusion method is highly consistent with the actual position, which helps to optimize the target tracking process. Compared with the target position obtained with a low signal-to-noise ratio, the prediction fusion method reduces the error caused by poor signal quality; even if some prediction results are interfered by noise, the system can still maintain stable tracking through other high-quality prediction results.

[0077] Step S6: perform multi-station image signal fusion on the registered prediction results to improve the signal-to-noise ratio of the ground-based GNSS-S radar image, and select the prediction result with the highest signal-to-noise ratio from the multi-station image signal fusion results of each prediction result as the long-range target position and long-range target speed of the aerial target.

[0078] In step S6, it specifically includes:

[0079] Step S61 : performing non-coherent accumulation of multiple point trace images on multiple prediction results of long-distance spatiotemporal registration.

[0080] Step S62: Select the predicted three-dimensional position with the highest signal-to-noise ratio from the multiple prediction results as the position of the aerial target at a long distance.

[0081] Step S63, calculating the speed of the aerial target at the long distance based on the position and speed of the aerial target at the short distance and the position of the aerial target at the long distance.

[0082] The aerial target prediction fusion tracking method based on ground-based GNSS-S radar provided by the present invention also includes:

[0083] Step S7: further predict the target position at a farther distance based on the selected prediction result.

[0084] In step S7, the high signal-to-noise ratio fusion detection result at the current long distance is used as the known position and speed, and the above steps are used to iteratively calculate the prediction fusion tracking of the target at a longer distance until the signal-to-noise ratio after fusion is difficult to detect.

[0085] In this embodiment, the high signal-to-noise ratio fusion detection result at the current long distance is used as the known position and speed, and the above steps are iteratively calculated to predict the fusion tracking of targets at a longer distance until the signal-to-noise ratio after fusion is difficult to detect.

[0086] From the above example description, it can be seen that the aerial target prediction fusion tracking method of the ground-based GNSS-S radar realizes the target tracking method of "tracking-prediction-fusion-detection" that first fuses and then detects. The multi-angle detection information fusion is used to improve the target signal-to-noise ratio and improve the detection and tracking capability of the ground-based GNSS-S radar for long-distance targets.

[0087] It should be noted that the above is a preferred embodiment of the present invention. It should be pointed out that although the preferred embodiment of the present invention has been described, once the basic creative concept of the present invention is known, a number of improvements and modifications can be made by a person skilled in the art without departing from the principles of the present invention, and these improvements and modifications should also be regarded as the protection scope of the present invention. Therefore, the attached claims are intended to be interpreted as including the preferred embodiment and all changes and modifications that fall within the scope of the embodiments of the present invention.

Claims

1. A method for predicting and fusion tracking of aerial targets based on ground-based GNSS-S radar, characterized in that: The following steps are involved: Step S1, simultaneously obtaining scattered echoes of multiple GNSS satellite navigation signals of aerial targets through a ground-based GNSS-S radar; Step S2, performing range compression and multi-pulse coherent accumulation processing on the scattered echoes of multiple GNSS satellite navigation signals respectively to obtain the short-range multi-station motion trajectory of the aerial target; Step S3, analyzing the obtained close-range multi-station motion trajectory of the aerial target to obtain the position and speed of the aerial target at a close distance; Step S4, based on the obtained position and speed of the aerial target at a short distance, fuzzy prediction is performed on the movement of the aerial target to a long distance to obtain multiple prediction results; Step S5, performing temporal registration and spatial registration on the position of each prediction result in multiple dual-site trace images; Step S6, performing signal fusion of multiple dual-station track images on the registered prediction results, and selecting the prediction result with the highest signal-to-noise ratio from the multi-station image signal fusion results of each prediction result as the position and speed of the aerial target at a long distance.

2. The method for predicting and fusion tracking of aerial targets based on ground-based GNSS-S radar according to claim 1 is characterized in that: Also includes: Step S7, based on the position and velocity of the aerial target at a long distance obtained in step S6 as the known position and velocity of the aerial target, repeat steps S4 to S6 to further predict the position and velocity of the aerial target at a longer distance until the signal-to-noise ratio after fusion is difficult to detect.

3. The method for predicting and fusion tracking of aerial targets based on ground-based GNSS-S radar according to claim 1, characterized in that: In step S1, the receiving antenna of the ground-based GNSS-S radar includes a direct signal receiving antenna and a scattered signal receiving antenna, and the beam range of the direct signal receiving antenna covers multiple GNSS satellites; according to the radar scattering cross-sectional area of ​​the aerial target, the scattered signal receiving antenna can achieve a signal-to-noise ratio detection of more than 10dB when detecting the target at the closest point after the range compression and the multi-pulse coherent accumulation processing.

4. The method for predicting and fusion tracking of aerial targets based on ground-based GNSS-S radar according to claim 3 is characterized in that: In the step S2, it specifically includes: Step S21, for each scattered echo of the GNSS satellite navigation signal, pulse compression is performed on the scattered channel signal with reference to the GNSS direct signal to achieve range compression; Step S22, performing coherent accumulation on the results after range compression at a slow time to form a plurality of dual-site trace images; Step S23: extracting the position of the aerial target in each of the dual-station track images based on the coherent accumulation result, and forming a close-range multi-station motion track of the aerial target.

5. The method for predicting and fusion tracking of aerial targets based on ground-based GNSS-S radar according to claim 4 is characterized in that: In the step S3, it specifically includes: Step S31, performing temporal registration and spatial registration on the positions of the aerial targets in the plurality of dual-site track images; Step S32, determining the three-dimensional position of the aerial target by a multi-station time-delay positioning method; Step S33: Based on the three-dimensional position of the aerial target at each moment, obtain the position and speed of the aerial target at a close distance.

6. The method for predicting and fusion tracking of aerial targets based on ground-based GNSS-S radar according to claim 5 is characterized in that: In the step S4, it specifically includes: Step S41, selecting the aerial target to move to a position where the detection signal-to-noise ratio is lower than 10 dB in any single dual-site track image, and extrapolating the motion state of the aerial target based on the position and speed of the aerial target at a close distance; Step S42: determine the three-dimensional positions of multiple extrapolated results of the aerial target to form multiple predicted three-dimensional positions.

7. The method for predicting and fusion tracking of aerial targets based on ground-based GNSS-S radar according to claim 6, characterized in that: In the step S5, it specifically includes: Step S51, respectively calculating the position of each predicted three-dimensional position in each of the dual-site trace images; Step S52: Perform temporal registration and spatial registration on the position of each predicted three-dimensional position in each of the dual-site trace images.

8. The method for predicting and fusion tracking of aerial targets based on ground-based GNSS-S radar according to claim 1, characterized in that: In the step S6, it specifically includes: Step S61, performing non-coherent accumulation of a plurality of dual-site track images on the plurality of registered predicted three-dimensional positions respectively; Step S62, selecting the predicted three-dimensional position with the highest signal-to-noise ratio from multiple prediction results as the position of the aerial target at a long distance; Step S63, calculating the speed of the aerial target at the long distance based on the position and speed of the aerial target at the short distance and the position of the aerial target at the long distance.

9. An electronic device, characterized in that: include: One or more processors, one or more memories, and one or more computer programs; wherein the processor is connected to the memory, and the one or more computer programs are stored in the memory. When the electronic device is running, the processor executes the one or more computer programs stored in the memory, so that the electronic device executes the aerial target prediction fusion tracking method based on ground-based GNSS-S radar as described in any one of claims 1 to 8.

10. A computer-readable storage medium, characterized in that: Used to store computer instructions, which, when executed by a processor, implement the aerial target prediction fusion tracking method based on ground-based GNSS-S radar as described in any one of claims 1 to 7.

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