Parameter evaluation of complex maneuvering targets using Kalman fitering and multi-model adaptation

The object of research is the system determines the target angular coordinates on the missile’s homing head. Current target coordinate determination systems employed in seekers often operate under significant limitations. When a target’s actual motion deviates from the simplified, hypothetical model used to synthesize the coordinate system, a critical issue arises: the errors in evaluating both the coordinates and their derivative components rapidly and significantly increase. Problem that was solved is to evaluate complex maneuvering target parameters. But there is no need to know the target’s maneuver frequency. This study presents a novel filtering algorithm that accurately estimates all parameters of complex maneuvering targets without prior knowledge of their maneuver frequency. The algorithm achieves a significant advantage, reducing estimation error by over 95% within the first 5 seconds. With its simple structure, high stability, and fast convergence, this robust solution is essential for modern guidance systems, greatly enhancing the effectiveness of tracking unpredictable threats. A key strength of the proposed algorithm lies in its simple structure. Furthermore, it demonstrates high convergence rates and exceptional stability, crucial attributes for real-time applications. Its design also ensures ease of practical implementation, making it a viable solution for contemporary guidance systems. The algorithm is built on modern control techniques, combining extended Kalman filtering with interactive multi-models. It is necessary to accurately evaluate the target’s position, velocity, acceleration, and acceleration derivative without needing to know in advance the target’s maneuver frequency