Particle Filter-Based Track-Before-Detect Algorithm for Radar Systems

Radar Track-Before-Detect (TBD) technology represents a robust target tracking methodology specifically designed for low signal-to-noise ratio (SNR) environments, demonstrating exceptional capability in detecting weak and small targets. Traditional detection approaches typically perform single-frame detection first, followed by data association and tracking. However, when SNR falls below 1dB, single-frame detection performance deteriorates significantly, often leading to target loss. The particle filter-based TBD algorithm effectively enhances detection probability for weak targets by accumulating target state and measurement information over time.

The algorithm begins by establishing novel target and observation models, incorporating target motion characteristics into the state equation while constructing the observation equation using radar echo signals. For linear extended targets, the algorithm employs particle filtering to estimate the target's state distribution, where each particle represents a potential target state. During the filtering process, particle weights are updated based on measurement data, with high-likelihood particles being retained to achieve continuous target tracking. Implementation typically involves particle initialization, importance sampling, weight computation using likelihood functions, and systematic resampling to prevent particle degeneracy.

Compared to conventional methods, the particle filter-based TBD algorithm successfully detects weak targets with SNR as low as 1dB, exhibiting superior noise resistance and lower missed detection rates. Furthermore, the algorithm demonstrates flexibility in adapting to changes in target motion models, making it suitable for complex radar target tracking scenarios. Key implementation considerations include particle number optimization, efficient resampling techniques, and real-time processing capabilities for practical deployment.