Title :
FPGA implementation of multi parameter deinterleaving
Author :
Pandu, J. ; Balaji, N. ; Naidu, C.D.
Author_Institution :
Dept. of ECE, Sreyas IET, Hyderabad, India
Abstract :
Electronic warfare (EW) is defined as military action involving the use of electromagnetic and directed energy to control the electromagnetic spectrum or to attack the enemy. One possible task of an EW system is to sort and classify received pulses from a dense environment of hostile radars so that the pulses can be processed. this process is known as pulse deinterleaving. The deinterleaving process needs to be completed with very low latency to support timely decision making required in modern EW environments. One method for deinterleaving streams of incoming radar pulses is incremental clustering. Clustering is the unsupervised partitioning of similar data samples into groups called clusters. The goal is to create clusters in which members of a particular cluster are as similar as possible to one another and as different as possible from members of other clusters. Many clustering algorithms require a complete data set to be present a priori and require multiple passes through the data in order to produce a result. Such algorithms are referred to as non-incremental. On the other hand an incremental clustering algorithm considers each input data point only once at which point it assigns it to a cluster. Such a technique allows streaming data such as radar pulse descriptor words (PDWs) to be clustered in real time.
Keywords :
decision making; electromagnetic pulse; electronic warfare; field programmable gate arrays; military radar; pattern clustering; EW system; FPGA; PDW; data sample; decision making; electromagnetic spectrum; electronic warfare; incremental clustering algorithm; multiparameter deinterleaving process; radar pulse descriptor word; unsupervised partitioning; Classification algorithms; Frequency measurement; Pulse measurements; Radar; Radio frequency; Receivers; Reliability; Angle of Arrival; Cumulative Difference; Incremental Clustering of Evolving Data; Pulse Descriptor Word; Support Vector Machine Minimum Description Length; Time Adaptive Clustering;
Conference_Titel :
Electronics and Communication Systems (ICECS), 2014 International Conference on
Conference_Location :
Coimbatore
Print_ISBN :
978-1-4799-2321-2
DOI :
10.1109/ECS.2014.6892676