Mohamad Hasan Bahari

High Maneuver Target Tracking Based on Combined Kalman Filter and Fuzzy Logic

In this paper, a new combined scheme is presented to overcome some drawbacks of the high maneuvering target tracking problems by using the mixed fuzzy logic and the standard Kalman filter. This scheme is consist of two important aspects; at first absolute value of difference between last target course and the present observation target course and the second aspect is the absolute value of measurement residual. The results compared with the augmented method and another combined fuzzy logic method which have been reported respectively. Simulation results show a high performance of the proposed innovation method and effectiveness of this scheme in high maneuvering targets tracking problems.

Early diagnosis of systemic lupus erythmatosus using ANN models of dsDNA binding antibody sequence data.

In this paper a new method based on artificial neural networks (ANN), is introduced for identifying pathogenic antibodies in Systemic Lupus Erythmatosus (SLE). dsDNA binding antibodies have been implicated in the pathogenesis of this autoimmune disease. In order to identify these dsDNA binding antibodies, the protein sequences of 42 dsDNA binding and 608 non-dsDNA binding antibodies were extracted from Kabat database and encoded using a physicochemical property of their amino acids namely Hydrophilicity. Encoded antibodies were used as the training patterns of a general regression neural network (GRNN). Simulation results show that the accuracy of proposed method in recognizing dsDNA binding antibodies is 83.2%. We have also investigated the roles of the light and heavy chains of anti-dsDNA antibodies in binding to DNA. Simulation results concur with the published experimental findings that in binding to DNA, the heavy chain of anti-dsDNA is more important than their light chain.

A New Algorithm Based on Generalized Target Maneuver Detection

In the airborne vehicles, complex target tracking with more accurately is highly desirable. Conventional techniques based on augmented or batch algorithms are computationally expensive, which is the major drawback for the real time target parameter estimation. In contrast, other conventional input estimation techniques which overcome this problem, assume constant acceleration and therefore this is not a generalized modeling (T.C. Wang and P.K. Varshney, 1993). The proposed algorithm is developed to overcome these drawbacks by using a new generalized dynamic modeling of acceleration and acceleration rate (jerk) with reduction on the vector state dimension. The proposed modified Kalman filter algorithm is based on a generalized formulation including our earlier works. Results show the significant improvement for high maneuver target tracking problem.

A new algorithm based on combined fuzzy logic and Kalman filter for target maneuver detection

High maneuvering targets normally maneuver on circular paths which have lead to tracking filters on circular turns. In this paper, an innovation technique is presented to combine the tracking-maneuvering target problems using fuzzy logic and augmented Kalman filter. The proposed combined filter is composed of two automatic switching modes: one for the mild maneuver using augmented Kalman filter and the other for the high maneuver using fuzzy acceleration prediction method. It is demonstrated by means of numerical acceleration examples that the tracking capability of the proposed mixed method is essentially as good as that of the augmented method, especially at high maneuver target

An Intelligent Self-Tuning Approach for High Maneuvering Target Tracking

In this paper an intelligent self-tuning approach for high maneuvering target tracking is proposed. Recently, a new modified input estimation (MIE) method has been introduced. Although this method represents satisfactory performance in low and mild maneuvering situations, its ability to track the high maneuvering targets is still unacceptable. In this study we present an intelligent self-tuning approach based on a fuzzy forgetting factor in order to enjoy satisfactory tracking performance in low, Medium and high maneuvering target cases. Simulation results show the efficiency of the proposed method in comparison with the simple MIE.

High maneuvering target tracking using fuzzy fading memory

In this paper, a new fuzzy fading memory (FFM) is developed in order to aid a modified input estimation (MIE) technique and enhance its performance in tracking high maneuvering targets. The MIE has been introduced recently and performs well in tracking low and medium maneuvering targets. However, due to some modeling errors, the accuracy of this tracker may be seriously degraded in presence of high maneuvers. To cope with this difficulty, an intelligent approach based on FFM is presented in this paper. Simulation results prove the efficiency of the proposed method in tracking high maneuvering targets.