K.K. Vasiliev – Dr.Sc.(Eng), Professor, 

Ulyanovsk State Technical University

Е-mail: vkk@ulstu.ru

О.В. Саверкин – Post-graduate Student, 

Department «Telecommunications», Ulyanovsk State Technical University E-mail: saverkin-oleg@mail.ru

The paper considers the known linear stochastic motion model and a motion model in the body-fixed frame. The main disadvantage of the known model is the relation of acceleration parameters and axes of basic reference coordinate system. This disadvantage is the reason of complexity to set acceleration parameters along the axes. In addition, it can be a reason of some mistakes during trajectory filtration that carries out taking into account the described acceleration parameters. The motion model in the body-fixed frame depends on target motion direction only and is not determined by the Cartesian coordinate system. The linear Kalman filter based on the first model and another two algorithms based on the second model are also discussed. The only difference of filters based on the model in the body-fixed frame from the linear Kalman filter is a prediction vector equation that contains estimated values of acceleration parameters based on estimated values of speed, heading and elevation angle on the previous estimation step. That achieves to adjust filters to the direction and maneuvering of tracking object. Another feature of the one of proposed algorithms is an ability of separate trajectory parameters estimation that provides less computational cost. A comparative analysis showed that proposed algorithms provide an increase in the accuracy of trajectory parameters estimation in comparison with the known linear Kalman filter. In addition, the proposed algorithm with separate estimation can be applied in the target tracking systems with limited computational resource.

The article is recommended for postgraduate students and engineers whose work is related to the statistical processing of radar information and target tracking. The results of the simulation will allow the researchers to carry out further studies on the optimization of existing methods for processing radar information.

1. Kazarinov Yu.M. i dr. Radiotekhnicheskie sistemy: Uchebnik dlya studentov vuzov. Pod red. Yu.M. Kazarinova. M.: Izdatelskii tsentr «Akademiya». 2008. 592 s. (in Russian)
2. Farina A., Studer F. Tsifrovaya obrabotka radiolokatsionnoi informatsii. Soprovozhdenie tselei. M.; Radio i svyaz. 1993. 320 s. (in Russian)
3. Kuzmin S.Z. Osnovy teorii tsifrovoi obrabotki radiolokatsionnoi informatsii. M.: Sov. radio. 1974. 432 s. (in Russian)
4. Konovalov A.A. Osnovy traektornoi obrabotki radiolokatsionnoi informatsii. V 2-kh chastyakh. SPb.: Izd-vo SPbGETU. 2014. Ch. 1. 164 s. (in Russian)
5. Konovalov A.A. Osnovy traektornoi obrabotki radiolokatsionnoi informatsii. V 2-kh chastyakh. SPb.: Izd-vo SPbGETU. 2014. Ch. 2. 180 s. (in Russian)
6. Vasilev K.K. Optimalnaya obrabotka signalov v diskretnom vremeni: Ucheb. posobie. M.: Radiotekhnika. 2016. 288 s. (in Russian)
7. Vasilev K.K., Luchkov N.V., Saverkin O.V. Sravnitelnyi analiz algoritmov traektornoi filtratsii. Radiotekhnika. 2017. № 6. S. 27−31. (in Russian)
8. Vasilev K.K., Mattis A.V. Svyazannye stokhasticheskie modeli dvizheniya radiolokatsionnykh tselei. Avtomatizatsiya protsessov upravleniya. 2017. № 4(50). S. 14−18. (in Russian)
9. Saverkin O.V. Comparative analysis of digital radar data processing algorithms. CEUR Workshop Proceedings of the 2st International Workshop on Radio Electronics and Information Technologies (REIT 2017). 2017. P. 120−126.
10. Saverkin O.V. Ob effektivnosti traektornoi filtratsii v svyazannykh koordinatakh. Avtomatizatsiya protsessov upravleniya. 2018. № 3(53). S. 90−94. (in Russian)
11. Mattis A.V., Saverkin O.V. Effektivnost traektornoi filtratsii v svyazannykh koordinatakh. Informatsionno-izmeritelnye i upravlyayushchie sistemy. 2018. № 11. S. С. 19−23. DOI 10.18127/j20700814-201811-04. (in Russian)
12. Vasilev K.K., Saverkin O.V. Sravnitelnaya otsenka algoritmov traektornoi filtratsii. Tsifrovaya obrabotka signalov i ee primenenie (DSPA2019). M.: RNTORES im. A.S. Popova. 2019. S. 343−347. (in Russian)