An improved track-before-detect(TBD) algorithm based on the particle filter is proposed for weak target detection
and tracking in low signal to noise radio(SNR) environment. Under the theory framework of particle filter, an algorithm which
combines the particle filter with unscented Kalman filter(UKF) algorithm is presented. When the important probability
density distribution is calculated by using the algorithm, the sampling particles are most likely to be in the region of
high likelihood based on the current measurement, which makes the particles distribution more approach to the posterior
distribution of the state. Simulation results show that the proposed algorithm has an improved performance of detection and
tracking compared with the standard particle filter.