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An LED strobe lamp with energy storage mode is proposed. This strobe lamp uses a two-stage converter. The first-stage converter boosts the input voltage and stores energy in the capacitor. The second-stage converter uses this stored energy drives the 100W LED lamp group in a constant current manner, and emits a strong flashing light in a short time. Compared with the traditional strobe light composed of high-voltage strobe tube, this LED type strobe light has the advantages of long life, high safety, strong anti-vibration ability, etc. At the same time, such a strobe light has a small size and has a relatively small requirement on the output power. It can be driven by a small solar charger, so it can replace traditional flashing lights and is widely used in transportation and industrial environments.

When particle filter is applied in radar target tracking, the accuracy of the initial particles greatly effects the results of filtering. For acquiring more accurate initial particles, a new method called “competition strategy algorithm” is presented. In this method, initial measurements give birth to several particle groups around them, regularly. Each of the groups is tested several times, separately, in the beginning periods, and the group that has the most number of efficient particles is selected as the initial particles. For this method, sample initial particles selected are on the basis of several measurements instead of only one first measurement, which surely improves the accuracy of initial particles. The method sacrifices initialization time and computation cost for accuracy of initial particles. Results of simulation show that it greatly improves the accuracy of initial particles, which makes the effect of filtering much better.

In practical multi-sensor information fusion systems, there exists uncertainty about the network structure, active state of sensors, and information itself (including fuzziness, randomness, incompleteness as well as roughness, etc). Hence it requires investigating the problem of uncertain information fusion. Robust learning algorithm which adapts to complex environment and the fuzzy inference algorithm which disposes fuzzy information are explored to solve the problem. Based on the fusion technology of neural networks and fuzzy inference algorithm, a multi-sensor uncertain information fusion system is modeled. Also RANFIS learning algorithm and fusing weight synthesized inference algorithm are developed from the ANFIS algorithm according to the concept of robust neural networks. This fusion system mainly consists of RANFIS confidence estimator, fusing weight synthesized inference knowledge base and weighted fusion section. The simulation result demonstrates that the proposed fusion model and algorithm have the capability of uncertain information fusion, thus is obviously advantageous compared with the conventional Kalman weighted fusion algorithm.

In particle filter algorithm, resampling is always used to release sample impoverishment phenomenon, but it weakens the diversity of samples set and cause the algorithm unrobust. Based on imitating biology evolvement regulation, paper (Mo Yi-wei, et al., 2005) brought forward the evolutionary particle filter (EPF) algorithm. On the cost of much calculation, this method ameliorates the diversity of samples set to relieve the effect caused by samples impoverishment, but in paper (Mo Yi-wei, et al., 2005) the way to select the variation strength is not related. In radar tracking, this paper brings forward an improved evolutionary particle filter (IEPF) algorithm, in which variation strength is based on state noise and measurement noise. What's more, unlike EPF in previous paper, in which the evolution proceeds at each step, the algorithm sets threshold of effective particles to determine if variation is necessary at current step, and much calculation is saved. Simulations demonstrate the feasibility of proposed algorithm