Extended Kalman Filter vs Particle Filter Performance Comparison in Target Tracking

In the field of target tracking, the Extended Kalman Filter (EKF) and Particle Filter (PF) represent two commonly used nonlinear filtering methods, each with distinct advantages and limitations. The EKF approximates true system states by linearizing nonlinear system models, offering relatively high computational efficiency through its Gaussian assumption and first-order Taylor series expansion. This makes EKF suitable for scenarios with limited computational resources where extreme precision isn't critical. However, when dealing with highly nonlinear systems, EKF's linear approximation can lead to significant estimation errors or even filter divergence, particularly noticeable in implementations involving strong nonlinear measurement models.

In contrast, Particle Filter employs Monte Carlo methods, approximating state distributions through a set of random samples (particles) that propagate through the system model. This approach better handles strong nonlinearities and non-Gaussian noise problems, especially when implementing complex observation models or dealing with multimodal distributions. The PF algorithm typically involves importance sampling and resampling steps to prevent particle degeneracy. However, PF requires higher computational costs proportional to the number of particles used. Insufficient particle counts can lead to sample impoverishment issues, potentially affecting tracking stability and requiring careful tuning of resampling strategies in practical implementations.

In practical applications, the choice between EKF and PF involves balancing computational resources against accuracy requirements. For real-time systems with limited computational capacity (such as drone tracking applications), EKF often proves more practical due to its deterministic computation requirements. Meanwhile, in high-precision or multi-target tracking scenarios (like autonomous driving systems), Particle Filter generally delivers more robust results, particularly when implementing complex motion models or dealing with ambiguous measurement associations.