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Air platforms use radar warning receiver systems to detect threat radars in military operations. It is critical for an aircraft operating in a hostile environment to detect and classify radar signals and determine radar emitters' identity and capability. This study proposes an innovative solution to the signal separation problem for pulse radar signals by the computer vision-based connected component labeling method. With the proposed solution, clustering is performed automatically on the three-dimensional image matrix created by using the parameters of the arrival angle, radio frequency, pulse width of the signals. After clustering, the pulse repetition interval (PRI) deinterleaving is provided by the time of arrival analysis in each cluster. Also, various simulations were carried out using different synthetic radar datasets containing pulse-on-pulse states. In the simulations, PRI accuracy analyzes were performed. [ABSTRACT FROM AUTHOR]

Copyright of Telecommunication Engineering is the property of Telecommunication Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Radio frequency (RF) stealth is one of the essential research hotspots in the radar field. The anti-sorting signal is an important direction of the RF stealth signal. Theoretically speaking, the anti-sorting signal design is based on the failure principle of the radar signal sorting algorithm, and the SDIF algorithm is a core sorting algorithm widely used in engineering. Thus, in this paper, the SDIF algorithm is first analyzed in detail. It is pointed out that the threshold function of the SDIF algorithm will fail when the signal pulse repetition interval (PRI) value obeys the interval distribution whose length is 20 times larger than the minimum interval of PRI. Secondly, the correctness of the failure principle of SDIF threshold separation is proved by the formula. Finally, the correctness is further verified by the signal design case. The principle of SDIF sorting threshold failure provides theoretical support for anti-sorting RF stealth signal design. It also complements the shortcoming of the casual design for the anti-sorting signal. Furthermore, the principle of SDIF sorting threshold failure helps improve anti-sorting signal design efficiency. Compared with the Dwell & Switch (D&S) signal and jitter signal, the anti-sorting ability of the signal designed by using the sorting failure principle is notably enhanced through simulation and experimentation. [ABSTRACT FROM AUTHOR]

This Letter describes a pulsed waveform which exhibits extreme Doppler tolerance, enabling large time‐bandwidth‐product observations of high‐velocity targets with low computational complexity. The waveform, which supports pulsewidth, pulse repetition interval, and frequency agility, utilises hyperbolic frequency‐modulated pulses with modulation parameters constrained to suppress range migration. [ABSTRACT FROM AUTHOR]

Pulse repetition interval (PRI) modulation recognition plays an important role in electronic warfare. Conventional recognition methods based on handcrafted features and elaborate threshold values suffer from the accuracy for multiple PRI modulations at low signal-to-noise ratio (SNR) with high percentages of missing pulses. In this letter, a method based on Asymmetric Convolution Squeeze-and-Excitation (ACSE) networks and features in autocorrelation domain is proposed to recognize six PRI modulation modes automatically. First, features in the time domain, frequency domain and autocorrelation domain are converted to images. Then the images are input into ACSE networks which can extract and learn deep features without complex data pre-processing. Finally, a linear layer will output modulation modes directly. Via simulations, robustness of autocorrelation features is proved. The simulation results also demonstrate that the proposed recognition method can achieve higher than 91% accuracy at −12 dB under normal conditions for six modulations and higher than 95% at −4 dB under extreme conditions. Compared with the conventional SVM and three CNN methods, ACSE networks outperform at low SNRs under extreme conditions. [ABSTRACT FROM AUTHOR]

In radar, uniform side lobe pattern is useful for detection of weak targets. Important radar characteristic function, i.e., ambiguity function, a two-dimensional function of time delay c(t,f) and Doppler frequency that portrays the distortion of a returned pulse induced by the receiver matched filter due to the Doppler shift of the return radar signal from the moving target, is useful for identification of poor targets. Nonuniform PRI LFM (ascending and descending order) with length 11 as well as uniform Pulse Repetition Interval LFM with length 11 has been considered here. The ambiguity diagram, which defines the radar's precision, is used to quantify PSLR (Peak Side Lobe Ratio) and ISLR (Integrated Side Lobe Ratio). To minimise side lobes, this paper proposes a new paradigm for PCW (Pulse Continuous Wave) and LFM signals. Several applications are discussed that are specifically related to the strengths and limitations of nonuniform PRI waveforms. [ABSTRACT FROM AUTHOR]

Pulse repetition interval (PRI) modulation recognition is a significant means to analyze radar working statuses and missions in Electronic Support system. Traditional methods may be insufficient to accurately recognize complex PRI at low SNR with high percentages of missing pulses. In this letter, an approach based on Squeeze-and-Excitation networks (SE-Net) and the autocorrelation functions for PRI modulation recognition automatically is proposed. Firstly, the features of six PRI modulation types in the autocorrelation domain are converted into images by calculating instantaneous autocorrelation functions. Then, the images will be the input of SE-Net which will automatically learn about deep features of different PRI modulation modes. Finally, SE-Net will output PRI modulation modes directly. Simulation results show that SE-Net is robust to the noise and missing pulses. The accuracies for all PRI modulation modes are more than 95% at −10dB with 30% missing pulses and more than 96% at −2dB with 50% missing pulses. Compared with traditional method and other networks, SE-Net can achieve better recognition performance at low SNR and high percentages of missing pulses. [ABSTRACT FROM AUTHOR]

Copyright of Telecommunication Engineering is the property of Telecommunication Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In modern electronic warfare environments, there are multiple-radar transmitting signals. For an electronic support system, it is essential to recognize the modulation of pulse repetition intervals (PRIs), since it is directly related to the indication of radar emitters. However, PRI modulations are more difficult to recognize in modern electronical environments due to the high ratio of lost and spurious pulses. Therefore, a fully automatic approach for recognizing seven PRI modulation types using a convolutional neural network (CNN) is proposed in this letter. Simulation results show that our CNN-based recognition method not only promotes performance but is also robust to the environment with lost and spurious pulses. The recognition accuracy is 96.1% with 50% lost pulses and 20% spurious pulses in simulation scenario. [ABSTRACT FROM AUTHOR]

Histogram-based methods are among the best methods for deinterleaving, because of their easy implementa- tion. However, they have a basic defect when they encounter a jittered pulse repetition interval (PRI). Jittered PRI is one of the most sophisticated patterns for electronic warfare (EW) receivers. In jittered PRI, the time among successive pulses varies in a totally random manner; thus its detection is very complicated. In this paper we present a new method for extracting jittered PRI from histogram-based methods. Simulation results demonstrate excellent performance of the proposed method in normal as well as hard circumstances where a higher missing pulse rate occurs or even when several targets with PRI of type jitter exist. [ABSTRACT FROM AUTHOR]

Since histogram-based methods are formed by some simple differences, they are very desirable for deinterleaving. However, their main imperfection concerns recognizing complex pulse repetition interval (PRI) patterns like jittered and staggered ones. In this paper, we present new thresholds to detect jittered and staggered PRI from histogram-based methods even in complex circumstances such as noisy time of arrival (TOA), complex PRI patterns, and large missing pulses. Simulation results demonstrate that our method can detect and extract constant, jittered, and staggered PRI correctly. Moreover, the method is proved to be considerably robust and reliable at a missing pulses rate up to 30%. [ABSTRACT FROM AUTHOR]

Pulse repetition interval modulation (PRIM) recognition is a critical task in electronic intelligence (ELINT) and electronic support measure (ESM) systems for detecting radar threats accurately. However, PRI recognition is a complex issue due to missing and spurious pulses, resulting in noisy PRI pattern changes in real environments. To address this problem, this paper proposes a novel approach that recognizes the five common types of PRIM through a four-phase process. In the first phase, a deep convolutional neural network (DCNN) is used as a feature extractor. Then, extreme learning machines (ELMs) are used for real-time recognition of the PRIM patterns in the second phase. In the third phase, we employ the biogeography-based optimizer (BBO) to enhance the network's robustness by optimizing the connection weights and biases. To address the increasing complexity of the model, we introduce an optimized variable-length internet protocol-based BBO (VBBO) in the fourth phase. In this approach (i.e., DCNN-VBBO-ELM), each layer of DCNN is encoded by an IP address into a habitat of VBBO in the same sequence as the DCNN layers. To evaluate the proposed method, we develop a real experimental dataset consisting of five common PRI patterns. Our approach achieves a final accuracy of 97.05%, which is better than other ELM-based benchmark models. Moreover, the proposed model requires only 27 s of training time to process 50,000 training images, confirming its real-time capabilities. In conclusion, our proposed approach improves PRI recognition by leveraging DCNN, ELM, and VBBO, resulting in a more accurate and robust real-time radar PRI classifier. [ABSTRACT FROM AUTHOR]

Synthetic aperture radar (SAR) is a remote sensing technique, capable of providing high-resolution images independent of weather conditions and sunlight illumination. This makes SAR very attractive for the systematic observation of dynamic processes on the Earth's surface. However, conventional SAR systems are limited in that a wide swath can only be achieved at the expense of a degraded azimuth resolution. This limitation can be overcome by using systems with multiple receive apertures, displaced in along track, but a very long antenna is required to map a wide swath. If a relatively short antenna with a single aperture in along track is available, it is still possible to map a wide area: Multiple swaths can be, in fact, simultaneously imaged using digital beamforming in elevation, but “blind ranges” are present between adjacent swaths. This paper considers an innovative concept, staggered SAR, where the pulse repetition interval (PRI) is continuously varied. This concept allows the imaging of a wide continuous swath without the need for a long antenna with multiple apertures. The choice of the sequence of PRIs and the preprocessing of the raw data are discussed in detail, showing how the staggered SAR is even less affected by ambiguities of pointlike or extended targets with respect to a system with constant PRI, which simultaneously maps multiple swaths. Some system design examples are finally presented and compared. [ABSTRACT FROM PUBLISHER]

The problem of estimating the period of a series of periodic events is considered under the condition where the measurements of the times of occurrence are noisy and sparse. The problem is common to bit synchronisation in telecommunications and pulse-train parameter estimation in electronic support, among other applications. Two new algorithms are presented which represent different compromises between computational and statistical efficiency. The first extends the separable least squares line search (SLS2) algorithms of Sidiropoulos et al., having very low computational complexity while attaining good statistical accuracy. The second is an approximate maximum-likelihood algorithm, based on a low complexity lattice search, and is found to achieve excellent accuracy. [ABSTRACT FROM AUTHOR]