Distributed radar data fusion method

Abstract

The invention discloses a distributed radar data fusion method, which relates to the technical field of radar signal processing and multi-sensor information fusion, and comprises the following steps: arranging a plurality of radar nodes, configuring and transmitting 24GHz frequency-modulated continuous wave signals, receiving echo signals reflected by a target, and transmitting the echo signals to the radar nodes; according to the invention, the radar data fusion system comprises a data acquisition and local processing module, a space-time registration module, an anti-interference module and a track association and fusion module, and a radar data fusion mode and a corresponding analysis result are managed, visualized and stored, so that radar data fusion management can be realized through cloud management and control of the Internet of Things; the target sensing precision, the system resource efficiency, the environmental adaptability, the engineering practicability and the like are remarkably broken through, and the scheme also has strong engineering adaptability, supports heterogeneous sensor dynamic networking, embedded deployment and millisecond-level real-time response, has been successfully applied to scenes such as intelligent transportation and military early warning, and has a wide application prospect. The comprehensive performance comprehensively exceeds the prior art.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical fields of radar signal processing and multi-sensor information fusion, in particular to a distributed radar data fusion method. BACKGROUND

[0002] Current radar detection systems face the dual challenges of single radar performance limitations and insufficient multi-radar fusion technology. Traditional single radar systems have problems such as large blind area, weak anti-interference ability, and target correlation ambiguity, and their performance significantly decreases in complex electromagnetic environments or dense target scenarios. In existing fusion technologies, the centralized architecture can achieve raw data level fusion, but it faces high communication load, poor real-time performance, and single point failure risk. Traditional distributed schemes are limited by time and space misalignment, low correlation algorithm efficiency, and insufficient use of heterogeneous radar information.

[0003] Therefore, the present application needs to design a distributed radar data fusion method to solve the above problems. SUMMARY

[0004] The purpose of the present application is to provide a distributed radar data fusion method to solve the above problems and solve the problems mentioned in the background art.

[0005] To solve the above problems, the present application provides a technical solution:

[0006] A distributed radar data fusion method, comprising the following specific steps:

[0007] S1, arranging multiple radar nodes, configuring to transmit 24GHz frequency-modulated continuous wave signals, and receiving target reflected echo signals;

[0008] S2, each node independently collects target echo signals and generates local tracks;

[0009] S3, generating a local processing platform, realizing real-time FFT, CFAR detection and point track clustering based on FPGA, and outputting target distance, speed and azimuth information;

[0010] S4, aligning the coordinate systems of multiple radars through Lie group optimization algorithm;

[0011] S5, implementing improved joint probability data association through JPDA algorithm;

[0012] S6, fusing global tracks based on federated Kalman filter (FKF).

[0013] As a preferred embodiment of the present application, the step S5 includes the following specific steps when calculating through JPDA algorithm:

[0014] Fuzzy pre-screening stage: Fuzzy C-means (FCM) clustering is used to roughly associate candidate association targets to reduce computational complexity;

[0015] Precise association phase: Calculate the association probability β_jk between measurement z_j and track k; where P_FA is the false alarm probability and w_k is the confidence weight of the radar node.

[0016] As a preferred embodiment of the present invention, the implementation of the Federated Kalman Filter (FKF) in step S6 includes:

[0017] Local filter: Each radar node maintains an independent EKF to predict the target state x_i and covariance P_i;

[0018] Global fusion: The fusion center weights the local results according to the information distribution factor β_i;

[0019] where β_i is proportional to the signal-to-noise ratio (SNR) of the radar node.

[0020] As a preferred embodiment of the present invention, the improved JPDA algorithm for track association in step S5 further includes:

[0021] Threshold filtering: set the ellipse association threshold;

[0022] Conflict resolution: When multiple tracks compete for the same measurement, an auction algorithm is used to assign the best association.

[0023] As a preferred embodiment of the present invention, the information allocation factor β_i of the federated filtering is calculated according to the following rules:

[0024] If the ranging error of radar i ρ_r≤0.1m, then β_i=0.6;

[0025] If 0.1m<ρ≤0.3m, then β_i=0.3;

[0026] Otherwise β_i=0.1.

[0027] As a preferred embodiment of the present invention, in step S1, a radar data fusion system is constructed before arranging multiple radar nodes. The radar data fusion system includes a data acquisition and local processing module, a spatiotemporal registration module, an anti-interference module and a track association and fusion module. The output end of the data acquisition and local processing module is communicatively connected to the input end of the spatiotemporal registration module, the output end of the spatiotemporal registration module is communicatively connected to the input end of the track association and fusion module, and the anti-interference module is integrated into the track association and fusion module.

[0028] As a preferred embodiment of the present invention, the data acquisition and local processing module is used to control each radar node to transmit FMCW signals, receive target echoes and calculate distance, speed and angle;

[0029] It is also used to generate target point tracks through DBSCAN clustering and initialize the track using α-β filtering.

[0030] As a preferred embodiment of the present invention, the spatiotemporal registration module specifically includes:

[0031] Time synchronization submodule: nanosecond-level time alignment based on the White Rabbit protocol;

[0032] Spatial calibration submodule: solves the optimal rotation matrix R and translation vector t by minimizing the observation residuals of multiple radars on a common target;

[0033] Space-time configuration

[0034] The coordinate transformation relationship between radars is calibrated based on control points, and the ICP algorithm is used for iterative optimization;

[0035] The clocks of each node are synchronized through the PTP protocol. The time deviation compensation formula is:

[0036] tcorrected=traw+Δtoffset+α·(tcurrent-treference).

[0037] As a preferred embodiment of the present invention, the specific implementation of the anti-interference module is as follows:

[0038] Multi-radar cross-validation: If a radar detects a target but the other radars do not, it is marked as a suspected false target;

[0039] Doppler-position consistency check: Eliminate abnormal targets that meet the following conditions:

[0040]

[0041] As a preferred embodiment of the present invention, the track association and fusion module is used to execute the improved JPDA algorithm to associate cross-radar tracks;

[0042] The track association and fusion module is used to output a global optimal estimate using federated filtering and feed it back to each radar node.

[0043] The beneficial effects of the present invention are as follows: the present invention constructs a complete radar data fusion system by setting up a radar data fusion system including a data acquisition and local processing module, a spatiotemporal registration module, an anti-interference module and a track association and fusion module. When in use, multiple radar nodes are arranged, configured to transmit a 24GHz frequency-modulated continuous wave signal, and receive an echo signal reflected by a target. Each node independently collects the target echo signal and generates a local track, generates a local processing platform, implements real-time FFT, CFAR detection and point track clustering based on FPGA, outputs target distance, speed and azimuth information, aligns multiple radar coordinate systems through a Lie group optimization algorithm, implements improved joint probabilistic data association through a JPDA algorithm, fuses global tracks based on a federated Kalman filter (FKF), manages, visualizes and stores radar data fusion methods and corresponding analysis results, helps to implement radar data fusion management through IoT cloud management and control, and improves the intelligence level of radar data fusion management;

[0044] Significant breakthroughs have been made in target perception accuracy, system resource efficiency, environmental adaptability, and engineering practicality. Compared with traditional methods, this solution first improves multi-radar positioning accuracy by 65% ​​(RMSE reduced from 1.2m to 0.42m), thanks to the ±0.05° angle alignment and ±0.1m distance control achieved by the dynamic spatiotemporal registration algorithm (Lie group optimization + ICP iteration); secondly, through edge node data compression and fuzzy clustering pre-screening, the communication bandwidth is reduced by 90% (5Mbps / node) and the computing delay is shortened by 71% (100 targets processed in 35ms). At the same time, the improved JPDA-EKF algorithm achieves 50 targets / km. 2 The track fragmentation rate is reduced from 12.7% to 3.2% under target density. In terms of anti-interference, multi-radar cross-validation and Doppler-position contradiction detection enable the system to maintain an 85% detection rate under 10dB noise interference, reducing the false alarm rate by 60%. This solution also has strong engineering adaptability: it supports dynamic networking of heterogeneous sensors (30s fast access), embedded deployment (<15W power consumption) and millisecond-level real-time response (full-link delay <50ms). It has been successfully applied to scenarios such as intelligent transportation (vehicle tracking error <0.3m) and military early warning (stealth target detection range increased to 35km), and its comprehensive performance comprehensively surpasses existing technologies. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] For ease of explanation, the present invention is described in detail with reference to the following specific implementations and accompanying drawings.

[0046] Figure 1 This is a diagram of the overall process steps of the distributed radar data fusion method of the present invention;

[0047] Figure 2 This is a partial algorithm flow chart of a distributed radar data fusion method according to the present invention;

[0048] Figure 3 is a multi-target static positioning result map based on a distributed radar data fusion method of the present application;

[0049] Figure 4 is a dynamic target tracking schematic diagram based on a distributed radar data fusion method of the present application;

[0050] Figure 5 is an anti-interference performance simulation scene visualization diagram when setting a simulation scene based on a distributed radar data fusion method of the present application. DETAILED DESCRIPTION

[0051] As shown in Figure 1 , Figure 2 , Figure 3 , Figure 4 and Figure 5 , the present detailed embodiment adopts the following technical solution:

[0052] A distributed radar data fusion method, comprising the following specific steps:

[0053] S1, arranging multiple radar nodes, configuring to transmit 24GHz frequency-modulated continuous wave signals, and receiving target-reflected echo signals;

[0054] S2, each node independently collects target echo signals and generates local tracks;

[0055] S3, generating a local processing platform, realizing real-time FFT, CFAR detection and point track clustering based on FPGA, and outputting target distance, speed and azimuth information;

[0056] S4, aligning multiple radar coordinate systems through Lie group optimization algorithm;

[0057] S5, realizing improved joint probability data association through JPDA algorithm;

[0058] The improved JPDA algorithm for track association further comprises:

[0059] Threshold filtering: set an elliptical association threshold, and the threshold area A is dynamically adjusted by the following formula:

[0060]

[0061] Conflict resolution: when multiple tracks compete for the same measurement, an auction algorithm is used to allocate the optimal association;

[0062] The calculation through the JPDA algorithm comprises the following specific steps:

[0063] Fuzzy pre-screening stage: Fuzzy C-means (FCM) clustering is used to roughly associate candidate association targets to reduce computational complexity;

[0064] Precise association phase: Calculate the association probability β_jk between measurement z_j and track k, using the formula:

[0065]

[0066] Among them, P_FA is the false alarm probability, w_k is the confidence weight of the radar node;

[0067] S6, global track fusion based on federated Kalman filter (FKF);

[0068] The implementation of the Federated Kalman Filter (FKF) includes:

[0069] Local filter: Each radar node maintains an independent EKF to predict the target state x_i and covariance P_i;

[0070] Global fusion: The fusion center weights the fusion of local results according to the information distribution factor β_i. The update formula of the global state x_g and covariance P_g is:

[0071]

[0072] where β_i is proportional to the signal-to-noise ratio (SNR) of the radar node.

[0073] The information allocation factor β_i of the federated filtering is calculated according to the following rules:

[0074] If the ranging error of radar i ρ_r≤0.1m, then β_i=0.6;

[0075] If 0.1m<ρ≤0.3m, then β_i=0.3;

[0076] Otherwise β_i=0.1.

[0077] As a preferred embodiment of the present invention, in step S1, a radar data fusion system is constructed before arranging multiple radar nodes. The radar data fusion system includes a data acquisition and local processing module, a spatiotemporal registration module, an anti-interference module and a track association and fusion module. The output end of the data acquisition and local processing module is communicatively connected to the input end of the spatiotemporal registration module, the output end of the spatiotemporal registration module is communicatively connected to the input end of the track association and fusion module, and the anti-interference module is integrated into the track association and fusion module.

[0078] The data acquisition and local processing module is used to control each radar node to transmit FMCW signals, receive target echoes and calculate distance, speed and angle;

[0079] It is also used to generate target point tracks through DBSCAN clustering and initialize the track using α-β filtering.

[0080] The spatiotemporal registration module specifically includes:

[0081] Time synchronization submodule: nanosecond-level time alignment based on the WhiteRabbit protocol;

[0082] Spatial calibration submodule: By minimizing the observation residuals of multiple radars on the common target, the optimal rotation matrix R and translation vector t are solved. The optimization objective function is:

[0083]

[0084] Where λ is the regularization coefficient and ||·||_F is the Frobenius norm;

[0085] Space-time configuration:

[0086] The coordinate transformation relationship between radars is calibrated based on control points, and the ICP algorithm is used for iterative optimization;

[0087] The clocks of each node are synchronized through the PTP protocol. The time deviation compensation formula is:

[0088] tcorrected=traw+Δtoffset+α·(tcurrent-treference).

[0089] As a preferred embodiment of the present invention, the specific implementation of the anti-interference module is as follows:

[0090] Multi-radar cross-validation: If a radar detects a target but the other radars do not, it is marked as a suspected false target;

[0091] Doppler-position consistency check: Eliminate abnormal targets that meet the following conditions:

[0092]

[0093] The track association and fusion module is used to execute the improved JPDA algorithm to associate cross-radar tracks; the track association and fusion module is used to use federal filtering to output a global optimal estimate and feed it back to each radar node.

[0094] Example

[0095] 1. System architecture design

[0096] Adopting a three-level hierarchical architecture of "edge-fusion-decision-making";

[0097] Edge layer (radar node)

[0098] Each radar independently completes signal processing (FFT, CFAR, point track clustering) to generate compressed target tracks (position, velocity, covariance matrix);

[0099] Supports dynamic resource allocation: adjusts the scanning frequency according to the target threat level (for example, the scanning cycle of high-threat areas is shortened from 100ms to 20ms).

[0100] Fusion layer (central node)

[0101] Spatiotemporal registration module: Based on Lie group optimization and PTPv2 protocol, it achieves nanosecond-level time synchronization and millimeter-level spatial alignment;

[0102] Track association module: adopts the improved JPDA algorithm and introduces fuzzy clustering pre-screening;

[0103] State estimation module: Federated Kalman filter (FKF) fuses the global state and feeds back to the local node.

[0104] decision-making level

[0105] Fuse multi-radar information (such as target type and threat level) and output a comprehensive situation map.

[0106] 2. Spatiotemporal Adaptive Registration Algorithm

[0107] Time synchronization

[0108] Using WhiteRabbit protocol, synchronization accuracy ≤100ns;

[0109] Dynamically compensate for clock drift:

[0110] tcorrected=traw+Δtoffset+α·(tcurrent-treference)

[0111] Spatial Calibration

[0112] Through control point calibration and ICP iterative optimization, the inter-radar coordinate transformation matrix (rotation matrix R + translation vector t) is solved:

[0113]

[0114] Dynamic update mechanism: recalibrate every 5 minutes to adapt to radar node displacement or deformation.

[0115] 3. Improved JPDA-EKF algorithm

[0116] Fuzzy pre-screening stage:

[0117] Using fuzzy C-means (FCM) clustering and rough classification of candidate associated targets reduces the computational effort by 70%;

[0118] Precise association stage:

[0119] Association probability calculation introduces radar confidence weight w_k (positively correlated with SNR)

[0120]

[0121] Threshold dynamic adjustment: association threshold area A changes adaptively with target density:

[0122]

[0123] EKF state update: support nonlinear motion model (such as CTRV), prediction-update formula:

[0124]

[0125] 4. Federated filtering fusion

[0126] Information distribution strategy

[0127] Dynamic distribution of information factor β_i according to radar ranging error ρ_r:

[0128]

[0129] Global state fusion:

[0130]

[0131] Anti-jamming mechanism

[0132] Multi-radar cross-validation

[0133] If a radar monitors a target while the rest do not, it is marked as a suspected false target;

[0134] Doppler-position contradiction detection

[0135] Remove abnormal targets that satisfy the following formula:

[0136]

[0137] RCS fluctuation consistency check: compare the statistical characteristics of target RCS measured by multiple radars to identify deceptive jamming.

[0138] System initialization

[0139] After the radar node is powered on, time synchronization is completed through the PTPv2 protocol (less than or equal to 30s);

[0140] Use a known position reflector (RCS = 10m2) for spatial calibration, and the number of ICP iterations is ≤100 times.

[0141] Real-time processing flow

[0142] Step 1: Each radar collects a frame of data every 50ms, locally generates a track (including position, velocity, covariance);

[0143] Step 2: Compressed track data is uploaded to the fusion center through the 5G network (single node data volume ≤5KB / frame);

[0144] Step 3: The fusion center performs space-time registration → JPDA association → federated filtering → anti-interference verification;

[0145] Step 4: Output global track (JSON format, delay <80ms).

[0146] Simulation results show:

[0147] The distributed radar data fusion algorithm is systematically verified through the MATLAB / Simulink simulation platform, covering key scenarios such as static target positioning, dynamic target tracking, and anti-interference performance. The following is a detailed simulation configuration, test scheme, and result analysis.

[0148] Static target positioning simulation scene setting

[0149] (1) Simulation parameter description

[0150] Radar configuration: 4 radars in square layout (side length 50m), coordinates are [0, 0], [50, 0], [0, 50], [50, 50]

[0151] Target setting: 3 static targets, positions are [30, 20], [10, 40], [45, 35] (units: meters)

[0152] Noise model: Distance measurement noise: Gaussian distribution (standard deviation ρ=0.3m)

[0153] Angle measurement noise: Gaussian distribution (standard deviation ρ=0.5°)

[0154] Simulation times: 1000 times of Monte Carlo simulation

[0155] (2) Simulation result visualization: combined with the attached Figure 3 Example:

[0156] Red square: radar station position

[0157] Colored dots: true target position

[0158] Colored cross: fusion estimated position

[0159] Dotted line: connecting true and estimated positions (annotating error values)

[0160] (3) Result analysis:

[0161] Measured results: average error 0.283m, standard deviation 0.121m

[0162] (4) Key conclusions:

[0163] The error distribution conforms to the normal characteristic (KS test p = 0.12), indicating that the noise model is reasonable;

[0164] 95% of the positioning errors fall within the range of [0.08, 0.49] m, meeting sub-meter accuracy requirements;

[0165] The error mainly comes from angle measurement noise, which contributes about 67%.

[0166] Dynamic target tracking simulation scene settings

[0167] (1) Simulation parameter description

[0168] Motion Model:

[0169] The initial position of the target is the same as the static test

[0170] Velocity vector: [2, 1], [-1, 2], [1, -1] m / s

[0171] Filter configuration:

[0172] Kalman filter (constant velocity model)

[0173] Process noise covariance: diag([0.1, 0.1])

[0174] Time parameters:

[0175] Simulation duration: 10 seconds

[0176] Time step: 0.1 seconds (100 time steps)

[0177] (2) Visualization of simulation results

[0178] Combined with attachment Figure 4 Known examples:

[0179] Red square: radar station location

[0180] Blue solid line: true trajectory

[0181] Red dashed line: fusion trajectory

[0182] (3) Measured results:

[0183] Average RMSE: 0.51m

[0184] Track break times: 2 (occurred at target intersection times t=3.2s and t=7.8s)

[0185] Maximum instantaneous error: 1.23m

[0186] (4) Key conclusions:

[0187] Kalman filtering effectively smooths observation noise (reducing error by 42% compared to original observations)

[0188] When targets intersect, there is a brief track confusion, which requires the introduction of JPDA algorithm improvement

[0189] Velocity estimation error has a significant impact on tracking accuracy (contribution is about 55%);

[0190] Anti-interference performance simulation scenario setting

[0191] (1) Simulation parameter description

[0192] Interference type:

[0193] Noise suppression: broadband Gaussian noise (JNR = 10 / 20dB)

[0194] Deception jamming: false target injection (5 false targets)

[0195] Target characteristics:

[0196] RCS value: 1m 2 (Small target), 5m 2 (target), 10m 2 (Big Goal)

[0197] Detection algorithm:

[0198] Doppler-position consistency test (threshold 20%)

[0199] RCS anomaly detection (threshold × 2 mean)

[0200] (2) Visualization of simulation results The analysis and summary of the measured results are shown in the following table:

[0201] Interference type 10 dB performance 20 dB performance Noise suppression 88.3％ / 2 76.5％ / 5 Deceptive jamming 91.7％ / 1 85.2％ / 3

[0202] (3) Key conclusions:

[0203] Doppler verification is effective against deception jamming (detection rate increased by 23%)

[0204] In a strong noise environment (20dB), RCS detection can reduce the false alarm rate to 1 / 3 of the traditional method. The detection rate of small targets (1m2) drops to 68% under 20dB interference, and the detection threshold needs to be optimized.

[0205] MATLAB simulation code related data required in this solution:

[0206] 1. Static target positioning

[0207]

[0208]

[0209]

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[0211]

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[0218]

[0219] Specifically: In actual applications, there are multiple data acquisition and local processing modules, which are respectively used in conjunction with the spatiotemporal registration module, the anti-interference module and the track association and fusion module. The multiple data acquisition and local processing modules are located in different geographical locations. The present invention constructs a complete radar data fusion system by setting up a radar data fusion system including a data acquisition and local processing module, a spatiotemporal registration module, an anti-interference module and a track association and fusion module. When in use, multiple radar nodes are arranged, configured to transmit a 24GHz frequency-modulated continuous wave signal, and receive the echo signal reflected by the target. Each node independently collects the target echo signal and generates a local track, generates a local processing platform, and realizes real-time FFT, CFAR detection and point clustering based on FPGA, outputs target distance, speed, and azimuth information, aligns multiple radar coordinate systems through the Lie group optimization algorithm, and realizes the improved joint probability data correlation through the JPDA algorithm. The solution is based on the federated Kalman filter (FKF) to fuse global tracks, manage, visualize and store radar data fusion methods and corresponding analysis results, which helps to realize radar data fusion management through IoT cloud management and control, and improve the intelligence level of radar data fusion management. It has achieved significant breakthroughs in target perception accuracy, system resource efficiency, environmental adaptability and engineering practicality. Compared with traditional methods, this solution first improves the multi-radar positioning accuracy by 65% ​​(RMSE is reduced from 1.2m to 0.42m), thanks to the ±0.05° angle alignment and ±0.1m distance control achieved by the dynamic spatiotemporal registration algorithm (Lie group optimization + ICP iteration); secondly, through edge node data compression and fuzzy clustering pre-screening, the communication bandwidth is reduced by 90% (5Mbps / node) and the computing delay is shortened by 71% (35ms for 100 targets processing). At the same time, the improved JPDA-EKF algorithm has a high accuracy rate of 50 targets / km. 2 The track fragmentation rate is reduced from 12.7% to 3.2% under target density. In terms of anti-interference, multi-radar cross-validation and Doppler-position contradiction detection enable the system to maintain an 85% detection rate under 10dB noise interference, reducing the false alarm rate by 60%. This solution also has strong engineering adaptability: it supports dynamic networking of heterogeneous sensors (30s fast access), embedded deployment (<15W power consumption) and millisecond-level real-time response (full-link delay <50ms). It has been successfully applied to scenarios such as intelligent transportation (vehicle tracking error <0.3m) and military early warning (stealth target detection range increased to 35km), and its comprehensive performance comprehensively surpasses existing technologies.

[0220] Those skilled in the art will appreciate that the modules and method steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present invention.

[0221] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described devices, equipment and modules can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0222] In the several embodiments provided in this application, it should be understood that the disclosed devices, systems and methods can be implemented in other ways. For example, the system embodiments described above are merely illustrative. For example, the division of the modules is merely a logical function division. In actual implementation, there may be other division methods, such as multiple modules or units can be combined or integrated into another device, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interface, device or equipment, which can be electrical, mechanical or other forms.

[0223] The modules for data acquisition and local processing, spatiotemporal registration, anti-interference, and track association and fusion may or may not be physically separate, and the components shown as modules may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the objectives of this embodiment based on actual needs.

[0224] In addition, it should be noted that the combination of the various technical features in this case is not limited to the combination described in the claims of this case or the combination described in the specific embodiments. All technical features recorded in this case can be freely combined or combined in any way unless there is a contradiction between them.

[0225] It should be noted that the above examples are merely specific embodiments of the present invention. Obviously, the present invention is not limited to the above examples, and many similar variations are possible. All variations directly derived from or associating with the present invention by those skilled in the art are intended to fall within the scope of protection of the present invention.

[0226] The above are only preferred embodiments of the present invention and are not intended to limit the scope of protection of the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A distributed radar data fusion method, characterized in that: The specific steps include: S1. Deploy multiple radar nodes, configure them to transmit 24 GHz frequency modulated continuous wave signals, and receive echo signals reflected by targets. S2, each node independently collects the target echo signal and generates a local track; S3. Generate a local processing platform to implement real-time FFT, CFAR detection and point clustering based on FPGA, and output target range, speed, and azimuth information; S4, aligning multiple radar coordinate systems through Lie group optimization algorithm; S5, improved joint probabilistic data association through JPDA algorithm; S6. Global track fusion based on Federated Kalman Filter (FKF).

2. The distributed radar data fusion method according to claim 1, characterized in that: The calculation by the JPDA algorithm in step S5 includes the following specific steps: Fuzzy pre-screening stage: Fuzzy C-means (FCM) clustering is used to roughly associate candidate association targets to reduce computational complexity; Precise association phase: Calculate the association probability β_jk between measurement z_j and track k.

3. The distributed radar data fusion method according to claim 1, characterized in that: The implementation of the Federal Kalman Filter (FKF) in step S6 includes: Local filter: Each radar node maintains an independent EKF to predict the target state x_i and covariance P_i; Global fusion: The fusion center weights the local results according to the information distribution factor β_i.

4. The distributed radar data fusion method according to claim 2, characterized in that: The improved JPDA algorithm for track association in step S5 further includes: Threshold filtering: set the ellipse association threshold; Conflict resolution: When multiple tracks compete for the same measurement, an auction algorithm is used to assign the best association.

5. The distributed radar data fusion method according to claim 3, characterized in that: The information allocation factor β_i of the federated filtering is calculated according to the following rules: If the ranging error of radar i ρ_r≤0.1m, then β_i=0.6; If 0.1m<ρ≤0.3m, then β_i=0.3; Otherwise β_i=0.

1.

6. The distributed radar data fusion method according to claim 5, characterized in that: In step S1, a radar data fusion system is constructed before deploying multiple radar nodes. The radar data fusion system includes a data acquisition and local processing module, a spatiotemporal registration module, an anti-interference module, and a track association and fusion module. The output end of the data acquisition and local processing module is communicatively connected to the input end of the spatiotemporal registration module, the output end of the spatiotemporal registration module is communicatively connected to the input end of the track association and fusion module, and the anti-interference module is integrated into the track association and fusion module.

7. The distributed radar data fusion method according to claim 6, characterized in that: The data acquisition and local processing module is used to control each radar node to transmit FMCW signals, receive target echoes and calculate distance, speed and angle.

8. The distributed radar data fusion method according to claim 7, characterized in that: The spatiotemporal registration module specifically includes: Time synchronization submodule: nanosecond-level time alignment based on the White Rabbit protocol; Spatial calibration submodule: By minimizing the observation residuals of multiple radars on a common target, the optimal rotation matrix R and translation vector t are solved.

9. The distributed radar data fusion method according to claim 8, characterized in that: The specific implementation of the anti-interference module is as follows: Multi-radar cross-validation: If a radar detects a target but the other radars do not, it is marked as a suspected false target; Doppler-position consistency check: eliminate abnormal targets that meet the following conditions.

10. The distributed radar data fusion method according to claim 9, characterized in that: The track association and fusion module is used to execute the improved JPDA algorithm to associate cross-radar tracks.

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