Takashi Matsuzaki

A study of bias error estimation method by KGBE

Data fusion uses observations from networked multiple sensors and generates an integrated track. It achieves wide surveillance area and high accuracy, by minimizing error covariance matrix. In ideal environment, a fundamental assumption is that sensor biases are zero. However, the bias errors are not zero in real environment. As a result, the accuracy of integrated tracks deteriorate, even if the all sensors observe the same target. In this paper, we propose a new bias estimation algorithm is based on kalman filter bias estimator with grid search method. It is called the KGBE method (Kalman filter with Grid search Bias Estimator). As the result, we confirmed that the KGBE achieves higher accuracy than conventional algorithms.

Target track extraction in high false density environments using multiple hypothetical frame selection MLPDA

MLPDA (Maximum Likelihood Probabilistic Data Association) has drawn attention as an effective target track extraction algorithm in high false density environments. In this algorithm, the target track is estimated as the maximum likelihood state vector, by using multiple observation frames that include the target signal and many false signals. The track is confirmed whether it is the true target or not, by comparing its likelihood with a given track confirmation threshold. However, when the target signals are lost at several frames, the conventional MLPDA deteriorates the track estimation accuracy due to false signals in frames without the target signal. In this paper, we propose multiple hypothetical frame selection MLPDA, which can extract the target track under the situation where the target signals are lost in several frames. Specifically, a batch of stored frames is first selected for track extraction. If the track is not confirmed, our algorithm offers multiple frame selection hypotheses where some frames are assumed to be the frames without the target signal and the other frames include the target signal. The track is extracted under these hypotheses, respectively, and the most likely hypothesis is accepted. If all hypotheses are rejected, our proposed method generates hypotheses that increase the number of frames without the target signal, and verifies them again. Furthermore, the hypotheses that have likelihoods above a given threshold are retained in order to modify the wrong frame selection later. Simulation results show the validity of our proposed method.