Jing Zhanrong

Multi-sources information fusion algorithm in airborne detection systems

Abstract To aim at the multimode character of the data from the airplane detecting system, the paper combines Dempster-Shafer evidence theory and subjective Bayesian algorithm and makes to propose a mixed structure multimode data fusion algorithm. The algorithm adopts a prorated algorithm relate to the incertitude evaluation to convert the probability evaluation into the precognition probability in an identity frame, and ensures the adaptability of different data from different source to the mixed system. To guarantee real time fusion, a combination of time domain fusion and space domain fusion is established, this not only assure the fusion of data chain in different time of the same sensor, but also the data fusion from different sensors distributed in different platforms and the data fusion among different modes. The feasibility and practicability are approved through computer simulation.

Adaptive Extended Kalman Filter Based on Genetic Algorithm for Tightly-Coupled Integrated Inertial and GPS Navigation

An adaptive Extended Kalman Filter is derived for integrating inertial measurements from gyros, accelerometers with GPS pseudorange and pseudorange rate measurements. The adaptive filter uses an estimator based on state residual to provide a positive definite estimate of the process noise covariance matrix. The Genetic Algorithm is utilized to optimally determine the estimator’s parameter which is a slide window size. The filter formulation is based on standard inertial navigation equations. Simulation results are shown to compare the performance of nonadaptive and adaptive Extended Kalman Filter.

An efficient zero-blocks early detection algorithm for H.264/AVC

In low bit-rate video coding, all-zero block determination is a common technique to reduce the computational complexity of the encoder. We aim to improve the algorithm for the early detection of zero-blocks in H.264 video encoding. An improved algorithm for the early detection of all-zero blocks in H.264 video encoding is proposed in this paper. Based on the theoretical analyzes for the integer transform and quantization in H.264, a precise sufficient condition is proposed by modifying the calculation formula of discrete cosine transform in the motion estimation. The simulation results show that the proposed algorithm achieves approximately an 12%-39% computational saving without video-quality degradation, compared to the conventional method.

An Improved Terminal Guidance Algorithm Based on Differential Game Theory

Two-side optimal problem of pursuit-evasion conflicts is studied in this paper, and a new missile guidance law for air defense missile intercepting maneuver target is developed. The guidance law could be called as Reduced Sensitivity Guidance Law (RSGL), which is based on the differential game theory and the capacity of target maneuver. The mathematical model of RSGL is solved by riccati equation method. According to the target acceleration estimation values processed by kalman filter method, the missiles terminal guidance of RSGL parameters can be modified to improve the guidance performance, which with low sensitivity to time-to-go estimation error. Compared to the simulation results under the same situation, RSGL has higher performance than the augmented proportional navigation (APN) and the minimum effort guidance law (MEL) with less miss distance and lower acceleration of pursuit missile.

Research on Real-time Localization Algorithm of Aircraft on use of Pilot Signals

A 3-D passive localization algorithm on the low-altitude aircraft is presented, which takes use of CDMA cellular network pilot signals. The method is using the time differences of arrival (TDOA) of CDMA cellular network pilot signals to make twice weighted least squares(WLS), and then taking use of the original information of Inertial Navigation System(INS) to make fuzzy elimination, finally achieving 3-D real-time positioning of the low-altitude aircraft. Monte Carlo simulation shows that the algorithm is of high speed and high precision, in the Gaussian white noise conditions, the mean square error approaches Cramer-Lao (CRLB).

Gaussian particle filtering based on coarse and fine-scale sampling on target tracking algorithm

Sequence Monte Carlo estimation for dynamic target involves recursive algorithm and predictive distributions of unobserved time varying signal based on noisy observations. A new Gaussian particle filtering based on coarse and fine-scale coupled chain sampling is presented in this paper, which approximates the posterior distributions by single Gaussians. By using a coarser scale, the target state chain can run faster and better explore the posterior while a fine scale sampling can guarantee the accuracy of target tracking; the coupled chain is included updates that allow target state information to pass between the two scales. Simulation result show the improved GPF (Gaussian particle filtering) reduces the complexity and ensures the accuracy of target tracking.