Pulse compression is used to achieve large radiated energy but simultaneously a short pulse for range resolution. Range resolution is an ability of the receiver to detect nearby targets. Pulse compression can also detect small target signals out of clutter. The matched filter output consists of unwanted but unavoidable side lobes however the reduction of this side lobe strength is the active research topic in radar signal processing. In this paper a new technique is proposed to suppress the side lobes of radar signals that result from standard matched filtering. In this technique, it produces better peak side lobe ratio than all other conventional side lobe reduction techniques. In simulation results, the performance of this filter technique for Barker codes is compared with the amplitude shift code side lobe reduction technique.
The major advantages of pulse compression are low pulsepower which makes it suitable for solid-state devices, higher maximum range, good range resolution and better jamming immunity. The matched filter is the optimal linear filter for maximizing the signal to noise ratio (SNR) in the presence of additive stochastic noise. Pulse compression is an example of matched filtering. But this matched filter output consists of unwanted but unavoidable side lobes. For multiple-target radar, the side lobes of the compressed pulse must be considered in the system design because of the likelihood of false alarms. At the receiver the signal processor uses weighting filters which are not matched to the transmitted waveform. When this filter is not matched to the transmitted waveform then filter output consists of unwanted but unavoidable side lobes. In this paper a new technique is proposed to suppress the side lobes of radar signals that result from standard matched filtering. This technique produces better peak side lobe ratio than all other conventional side lobe reduction techniques. In simulation the results of this filter technique for compound Barker codes is compared with the other side lobe reduction techniques.
This paper describes an approach for mitigating distributed denial-of-services attacks in Cloud. DDoS attacks are huge pitfall for Cloud, and still, this is not very well handled. We presented a survey of existing work to defend DDoS attacks and mechanisms. In Cloud, intruder detection systems can be deployed at various positions like front-end, back-end or at Virtual Machine. Most of the existing IDS have been deployed at Virtual Machine in cloud. We proposed a new frame work using ensemble classifiers, namely, Bagging and Stacking to detect intrusions of both insiders and outsiders, and our proposed frame work will deploy at back-end. We focused on defending DDoS attacks in cloud environment which are the bottle neck of a Cloud environment compared to other type of attacks.
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