O. A. Agapov - JSC "NIIDAR". E-mail: stealth333@yandex.ru

The algorithm of reduction of an active interference signal in a multi-channel phased array in the conditions of a priori uncertainty was presented in [1]. It is able to detect the expected signal directly from the received mix of the desired signal, interference signal and noise. The algorithm was synthesized in the framework of adaptive Bayesian approach [2]. This algorithm is right when all receive channels of the radar have the same structure, and the receiving elements of the antenna array forming receiving channels are arranged regularly. There is the problem of processing of a large flow of incoming information from each receiving element if the antenna array has a fairly large number of receiving elements. This paper discusses the compression of the digital stream signal data by combining (summing with some weights) signals from all receiving elements of the radar with optimal number of output channels. The work shows three methods of data compression that way. The first method is uniting the receiving elements in subarrays with quasi-random form [3]. The second method is a system of partial beams directed towards the interferences. In this case, it forms a beams fan, some of which are directed to the active interference sources, and other ones are directed to a target. This scheme of compensation of interference signals, as shown in the work, is equivalent to the element-by-element processing. The third method of data compression is a scheme of coverage of the entire field of view or part of it, containing all sources of interfering signals, by the partial beams. For this purpose we used the extended beams to compensate of signal jamming and unextended beams directed to a target. The paper shows that the effectiveness of the algorithm with data compression is less than or equal to the efficiency element-by-element processing. The paper also shows the conditions under which the effectiveness equivalence is achieved.

 

1. CHeremisin O.P. Adaptivnye algoritmy obrabotki signalov v mnogokanalnykh priemnykh sistemakh s antennymi reshetkami // Radiotekhnika i ehlektronika. 2006. T. 51. № 9. S. 1087-1097.
2. Repin V.G., Tartakovskijj G.P. Statisticheskijj sintez pri apriornojj neopredelennosti i adaptacija informacionnykh sistem. M.: Sov. radio. 1977.
3. Agapov O.A. Metod umenshenija urovnja difrakcionnykh lepestkov v diagramme napravlennosti antennojj reshetki na osnove diagrammy Voronogo // Radiopromyshlennost. 2014. Vyp. 1. S. 39-50.
4. Strang G.Introduction to linear algebra. Wellesley: Wellesley-Cambridge Press. 2003.
5. Sosulin JU.G. Teoreticheskie osnovy radiolokacii i radionavigacii. M.: Radioisvjaz. 1992.
6. Gantmakher F.R. Teorija matric. M.: Fizmatlit. 2010.