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In order to deal with the track coalescence problem of the joint probabilistic data association (JPDA) algorithm, a novel approach from a state bias removal point of view is developed in this paper. The factors that JPDA causes the state bias are analyzed, and the direct computation equation of the bias in the ideal case is given. Then based on the definitions of target detection hypothesis and target-to-target association hypothesis, the bias estimation is extended to the general and practical case. Finally, the estimated bias is removed from the state updated by JPDA to generate the unbiased state. The results of Monte Carlo simulations show that the proposed method can handle track coalescence and presents better performance when compared with the traditional methods.

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.Image smear, produced by the shutter-less operation of full-frame charge-coupled device (CCD) sensors, greatly affects the performance of target detection, the centering accuracy, and visual magnitude estimation. We study the operation principle of full-frame CCDs, analyze the cause and properties of smear effect, and propose a smear removal algorithm for star images of full-frame CCDs. The proposed method locates the smears and extracts the rough profiles of the smeared stars by finding the conditional extrema. Then Gaussian fitting is applied to accurately extract the stars, in order to maintain the integrity of star images while minimizing the smear effect. The extraction of smears and stars requires parameters such as the size of the CCD, the integration time and the readout time, as well as the estimation of background noise. We assess the performance of our scheme with real observed data. The experimental results show that the proposed scheme improves the average signal-to-noise ratio of the images by about 22%, presenting better smear removal performance compared with several published methods. The limitation of the proposed algorithm includes the difficulty of distinguishing between two very close stars displaying the gray level of a single peak and overestimation of the background noise may also influence the performance of the algorithm.

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