Compression technique is a vital tool commonly used in radar to increase range resolution and signal to noise ratio. Pulse compression allows achieving the performance of a shorter pulse using a longer pulse and hence gain of a large spectral bandwidth. Unwanted signals from sidelobes returns affect the detection capability of any radar. Different sidelobe reduction/cancellation techniques based on pulse compression for Linear Frequency Modulated (LFM) radars have been deployed and addressed before. In this paper, a new optimum filter for enhancing radar detection capabilities of LFM radars is introduced. The proposed filter response is compared with the windowed classical matched filter response associated with Hamming window function. The filter is implemented using Software Defined Radio (SDR). A practical test has been carried to investigate its performance. Results show superior performance of our proposed matched filter compared to that of classical versions

Summary Decoding of frequency‐hopping spread spectrum (FHSS) methods to intercept the symbol content in signals and brilliant measurement is presented in this paper. The switching of the frequency channels rapidly using a pseudorandom code makes interception of FHSS signals very difficult. Therefore, a proposed adaptive compressive measurement for decoding the frequency‐hopping receivers that have no prior knowledge of the hopping sequence is the main focus of this work. The proposed methods rely on the instantaneous sparsity of the spectra of FHSS signals, and the transmitted symbols can be recovered from the proposed compressive measurements. Furthermore, the contribution of this work is the design of a proposed adaptive method that is used to design measurement kernels for compressive measurement and decoding based on knowledge enhancement, which includes both prior information enhanced and adaptive methods based on the properties of the intersignals. Simulation results on Gaussian frequency‐shift keying (GFSK) FHSS signals illustrate that the proposed compressive method enhances the detection performance of the received frequencies with acceptable decoding accuracy compared with other techniques such as enhanced orthogonal matching pursuit (EOMP) and an adaptive sampling kernels method with a random compressive ratio. Also, an enhancement of the proposed algorithm is achieved due to enhancement in both the normalized mean square error (NMSE) and the probability of correction error (Pce). The comparison between these algorithms is performed using simulation based on MATLAB and receiver operating characteristic (ROC) curves.

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Nowadays, the Compressive Sensing theory has an important effect on many communication system’s performances, and one of these applications is the radar system. Applying CS in the radar such as Linear Frequency Modulation Continuous Wave (LFMCW) radar signals has many advantages but suffers from the processing time in the two-dimensional processing. The performance of the LFMCW radar signals is achieved by using both conventional Complex Approximate Message Passing (CAMP) and adaptive recovery algorithms using a suitable reduction factor in both range and Doppler directions. In this paper, a modification is made to the adaptive CAMP algorithm to enhance the radar detection performance compared to both the conventional and adaptive algorithms under the same conditions. One of the main problems in radar detection is off-pin targets, which can be overcome using a certain filter in both range and Doppler directions. A comparison is achieved among these algorithms concerning detection performance using Receiver Operating Characteristic curves and the resolution performance in both range and Doppler directions. During the system analysis, it was found that an enhancement was achieved using the modified algorithm in the radar performance without any degradation in both range and Doppler resolution compared with the other algorithms.