We herein propose a distributed collaborative feedback particle filter (D-FPF) based on the diffusion strategy for nonlinear filtering problems. Each particle at each sensor is updated via a collaborative feedback structure incorporating the collaborative feedback gain and aggregate innovation. The localized intermediate state estimates are further shared among the neighbors. The importance sampling, resampling, or variance calculation, which are mandatory in the traditional distributed particle filters, are not requisite. Illustrative target tracking simulations validate that the proposed D-FPF could markedly outperform the distributed Gaussian particle filter in terms of both tracking and particle variance performance. The D-FPF could obtain favourable tracking performance even with an extremely small amount of particles.