FPGA-based SDR implementation for FMCW maritime surveillance radar

This paper presents an overview of FPGA-based SDR implementation on FMCW X-band maritime surveillance radar INDERA MX-4. The FPGA implementation proposed in this work significantly simplifies the hardware architecture of the radar. In particular, the RF and electronic systems can be simplified into 3 main units only, i.e. LO, FPGA (with integrated ADC) and transceiver. Such architecture simplification should lead to more compact and robust RF and electronic system hardware. The FPGA development is carried out on FPGA development board ALTERA Stratix III following standard FPGA programming steps. The radar functionalities programmed in the FPGA unit include DSP, mixers, frequency agility, RTDC and ADC, and have been successfully verified. The developed FPGA unit has been integrated with the rest of the radar subsystems (antennas, transceiver, RF unit, etc.) to realize an FPGA-based SDR. Field measurements, located at the harbor of Merak in Java, Indonesia, have been carried out to verify the developed FPGA-based SDR. It has been demonstrated that the FPGA unit has worked properly to support the designed SDR implementation on the radar. In particular, good detection of various ships in the harbor area has been achieved. This result demonstrates the successful implementation of the FPGA unit in the complete integrated SDR system

Mining frequent arrangements of temporal intervals

The problem of discovering frequent arrangements of temporal intervals is studied. It is assumed that the database consists of sequences of events, where an event occurs during a time-interval. The goal is to mine temporal arrangements of event intervals that appear frequently in the database. The motivation of this work is the observation that in practice most events are not instantaneous but occur over a period of time and different events may occur concurrently. Thus, there are many practical applications that require mining such temporal correlations between intervals including the linguistic analysis of annotated data from American Sign Language as well as network and biological data. Three efficient methods to find frequent arrangements of temporal intervals are described; the first two are tree-based and use breadth and depth first search to mine the set of frequent arrangements, whereas the third one is prefix-based. The above methods apply efficient pruning techniques that include a set of constraints that add user-controlled focus into the mining process. Moreover, based on the extracted patterns a standard method for mining association rules is employed that applies different interestingness measures to evaluate the significance of the discovered patterns and rules. The performance of the proposed algorithms is evaluated and compared with other approaches on real (American Sign Language annotations and network data) and large synthetic datasets