KEYWORDS: Signal to noise ratio, Detection and tracking algorithms, Edge detection, Signal detection, Fluctuations and noise, Time-frequency analysis, Fourier transforms, Sensors, Radar, Digital filtering
Precise radar pulse detection is of great significance for estimating parameters in electronic countermeasure and reconnaissance. An adaptive detection algorithm is proposed, which considers short-time Fourier transforms (STFT), constant false alarm rate (CFAR) in frequency domain, and difference of box (DOB) filter. First, STFT with the Gaussian window is used to acquire the time-frequency spectrum of the radar pulse signal. Second, in order to determine the existence of the pulse, CFAR detector is introduced into the frequency domain to generate an adaptive threshold, and then the rough pulse edges are obtained by mn method. Finally, the data where the rough pulse edges locate are processed by the refined STFT and DOB filter to get the precise pulse edges. The proposed algorithm is processed in the time-frequency domain, which cannot only adapt to low signal-to-noise ratio, but also has a high measurement accuracy. We also draw parallels to the conventional energy-based detection method, the results validate that the proposed algorithm is more robust and effective in practice. Simulations via various noisy input pulse data demonstrate the viability and validity of our proposed algorithm. The algorithm has been implemented in a spaceborne radar receiver.
KEYWORDS: Particles, Radar, Particle swarm optimization, Signal to noise ratio, Fluctuations and noise, Doppler effect, Detection and tracking algorithms, Computer simulations, Signal processing, Motion models
Signals from Costas-coded frequency-hopped radars exhibit an approximate “thumbtack” ambiguity function and prevent range-Doppler coupling. Therefore, these radars provide high-resolution range and velocity performance. However, the radar signals are highly sensitive to Doppler frequency shifts, and peak divergence arises due to Doppler mismatches when the target is moving. To solve these problems, we thoroughly analyze the effect of velocity on range profiles. Then, we propose a method of motion compensation based on particle swarm optimization using improved waveform entropy as the objective function. The target velocity is accurately determined through an iterative search. After motion compensation, the focus of the target range profile is notably improved. Simulation results confirm that the proposed method has a low computational complexity and retrieves accurate velocity estimates even under low signal-to-noise ratio and high target speeds, suggesting its adaptability and robustness for real-world applications.
The existing modulation recognition algorithms for a pulse compression radar (PCR) signal can hardly adapt to complex modulation types and low signal-to-noise ratio (SNR). To solve the problems, with respect to the seven kinds of widely used PCR signals—including linear frequency modulation signal, Baker code, Frank code, P1 code, P2 code, P3 code, and P4 code—a modulation type recognition algorithm based on integrated quadratic phase function (IQPF) and fractional Fourier transform (FrFT) is proposed. First, signals are preclassified according to their chirp rates (CRs) estimated through IQPF. Then, FrFT is carried out depending on the order, which is correlated to the estimated CR. Finally, signals in each class are subdivided and modulation recognition is accomplished according to the features of the FrFT spectrum. The simulation results validate the feasibility of the algorithm. They also demonstrate that, compared against existing research, the proposal achieves better correct recognition performance for various modulation types under low SNR condition.
KEYWORDS: Receivers, Radar, Signal to noise ratio, Interference (communication), Signal processing, Digital signal processing, Antennas, Automatic target recognition, Field programmable gate arrays, Digital filtering
In this paper, we present a novel hardware-efficient direction of arrival (DOA) estimation method based on digital channelized receiver. The proposed method splits the wideband array output into multiple frequency sub-channels and estimates the signal parameters using digital channelization receiver. Based on the accurate signal parameters estimation, signals with different bandwidths are isolated reasonably. Different DOA estimation methods are used to signals with different bandwidths. The proposed channelization based method can improve the output signal noise ratio (SNR). It outperforms those conventional DOA estimation methods on estimation accuracy, especially in real environment. Simulations are presented to demonstrate the performance. The results verify the effectiveness of the proposed method.
KEYWORDS: Signal to noise ratio, Edge detection, Signal detection, Image filtering, Electronic filtering, Monte Carlo methods, Sensors, Detection and tracking algorithms, Fourier transforms, Digital image processing
In this paper, we propose a new pulse edge detection method based on short time Fourier transform (STFT) and difference of boxes (DOB) filter. Firstly, detect the coarse starting and ending positions in frequency domain after STFT. Then obtain the precise pulse edge through DOB filter. It achieves a better performance than the classical energy detection (ED) method, especially when signal to noise ratio (SNR) is low. Simulation results and real data application validate the effectiveness of the proposed method.
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