In this paper, an estimation algorithm for the position and velocity of a moving target in a multistatic radar system is investigated. Estimation accuracy is improved by using bistatic range (BR), time-difference-of-arrival (TDOA), and Doppler shifts. Multistatic radar system includes several independent receivers and transmitters of time synchronization. Different transmitters radiate signals of different frequencies, and receivers detect the Doppler shifts of the received signals. These estimation parameters, BR, TDOA, and Doppler shifts, are readily available. The proposed algorithm combines different estimated parameters and optimizes estimation accuracy by two-step weighted least squares minimisations (WLS). This estimation algorithm is analysed and verified by simulations, which can reach the Cramer–Rao lower bound (CRLB) performance under mild Gaussian noise when the measurement error is small. Numerical simulations also demonstrate the superior performance of this method.
Off-grid algorithms for direction of arrival (DOA) estimation have become attractive because of their advantages in resolution and efficiency over conventional ones. In this paper, we propose a grid reconfiguration direction of arrival (GRDOA) estimation method based on sparse Bayesian learning. Unlike other off-grid methods, the grid points of GRDOA are treated as dynamic parameters. The number and position of the grid points are varied iteratively via a root method and a fission process. Then, the grid gets reconfigured through some criteria. By iteratively updating the reconfigured grid, DOAs are estimated completely. Since GRDOA has fewer grid points, it has better computational efficiency than the previous methods. Moreover, GRDOA can achieve better resolution and relatively higher accuracy. Numerical simulation results validate the effectiveness of GRDOA.
This paper presents an improved rate control method for H.264. First, the scene changes are detected by the average absolute difference of the brightness histograms between the adjacent frames. Then, the bit allocation and quantization parameters are adjusted, using a certain threshold. In addition, the calculation of the mean absolute difference (MAD) is modified in an alternative way, which makes the rate distortion optimization (RDO) more accurate. Extensive simulation results show that the proposed method, compared with G012, can improve the average peak signal-to-noise ratio (PSNR) and moderate the image quality.The goal of rate control is to adjust the bit-rate in order to use the bandwidth of channel sufficiently with better image quality. The rate control in H.264 is more complex than TMN8 in the sense that the statistics of the current frame is available in TMN8 while it is not available for the rate control in H.264 [1,2] . This is because that the quantization parameters are involved in both rate control and rate distortion optimization of H.264 while it is only involved in rate control of MPEG2, MPEG4 and H.263 [3][4][5][6][7] . There exists a typical chicken and egg dilemma when the rate control is implemented for H.264: to perform rate distorition optimization(RDO) for an macro block(MB), a quantization parameter should be first determined for the MB by using the statistics of the current frame. However, the statistics of the current frame is only available after performing the RDO. The JVT gives a linear model to predict the mean absolute difference of current basic unit in the current frame by that of the basic unit in the co-located position of the previous frame. This solves the chicken and egg dilemma. And JVT adopts two algorithms for H.264 rate control: JVT-F086 [8] and JVT-G012 [9] .Classic rate control algorithms do not detect and deal with the scene change, when the scene change happens, the image quality descends. The rate control for H.264 also has this problem. So, the problem of the scene change becomes an important one for rate control.This paper improves the algorithm based on the G012. The algorithm detects the scene change by the
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