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Multi-Radar Track Fusion

Multi-Radar Track Fusion Method Based on Parallel Track Fusion Model


With the development of multi-sensor collaborative detection technology, radar track fusion has become a key means to improve target tracking accuracy. Traditional fusion methods based on Kalman filtering and weighted averaging have the problem of insufficient adaptability in complex environments. This paper proposes an end-to-end deep learning track fusion method, which achieves high-precision track reconstruction through residual extraction and parallel network fusion, providing a new end-to-end method for track fusion.

The method combines the attention mechanism and the long short-term memory network in parallel and optimizes the computational complexity. Through the uncertainty weighting mechanism, the fusion weight is dynamically adjusted according to the reliability of the track features. Experimental results show that the mean absolute error of fusion accuracy of this method is 79% lower than the Kalman filter algorithm and about 87% lower than the mainstream deep learning model, providing an effective way for multi-radar track fusion in complex scenarios.

Modern radar systems face challenges from complex electromagnetic environments and multi-target interference. Track estimation from a single sensor often suffers from insufficient accuracy and poor robustness. Track fusion can effectively improve the accuracy and reliability of target state estimation by integrating multi-sensor data. It is a core technology in military reconnaissance, intelligent transportation, and other fields.

As the core area of multi-sensor information fusion, track fusion technology aims to achieve high-precision joint estimation of the state of moving targets by collaboratively processing distributed sensor data. Linear weighted fusion methods, such as variance weighting and convex combination fusion, only use single-time position information and have low accuracy. Kalman filter fusion achieves dynamic updates through state estimation, but it has a strong dependence on motion models and noise parameters, and its performance degrades significantly when the model mismatches. Deep learning technology can establish a direct mapping from raw sensor data to fused track data, breaking through the limitations of traditional methods that rely on staged processing. Optimal fusion of multisource information can be achieved through end-to-end global optimization.

This paper proposes a radar track fusion method based on parallel fusion model of deep learning. Through model-driven residual extraction, the attention mechanism and LSTM parallel processing simultaneously capture key spatiotemporal features and long-term dependencies, solving the single-dimensional defects of the LSTM model and the local information loss of the Transformer. Through the coordinated optimization of various modules, high-precision track fusion in complex scenarios is achieved. Experimental results show that this method is significantly better than traditional algorithms in fusion accuracy and motion pattern generalization ability, providing a new solution for radar track fusion technology. Future research will further explore the application of graph neural networks in multi-sensor spatiotemporal correlation modeling to improve the real time and robustness of the system.

radar track, target detection, signal processing, Doppler shift, tracking system, air surveillance, radar cross section, moving target indicator, electronic warfare, phased array radar, range resolution, clutter suppression, pulse compression, beamforming, target acquisition, trajectory prediction, radar imaging, sensor fusion, real-time tracking, aerospace defense

#RadarTrack, #SignalProcessing, #TargetDetection, #AirSurveillance, #RadarSystems, #DopplerShift, #TrajectoryTracking, #ElectronicWarfare, #PhasedArray, #Beamforming, #PulseCompression, #RadarImaging, #TrackingTechnology, #ClutterSuppression, #AerospaceDefense, #TargetAcquisition, #RealTimeTracking, #SensorFusion, #RadarEngineering, #AviationSafety

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