Repetition Interval Research Articles

Dual Function Radar and Communication Signal Design with Combined Waveform Selection and Pulse Repetition Interval Agility

The traditional probe–pass integration system embeds communication information into a radar waveform, which leads to a high level of waveform autocorrelation sidelobes and a poor false symbol rate at low signal-to-noise ratios. This article proposes a three-dimensional indexed modulation-based design method for probe–pass integration waveforms. This method realises communication information modulation and demodulation by simultaneously indexing orthogonal waveform selection, transmitting pulse PRI changes and carrier frequency changes in three dimensions, and applying compressed perception technology to solve the problems of PRI shortcuts and carrier frequency, resulting in a velocity term in the received waveform that cannot be accumulated by phase reference to realise velocity super-resolution. Finally, the radar detection performance and communication performance are simulated and analysed, and the simulation results reveal that the method proposed in this paper can not only satisfy the radar detection performance requirements but also achieve a lower unsigned rate on the basis of an improved communication rate.

Deinterleaving Method for Radar Signals With Dwell and Switch Pulse Repetition Intervals in a Prior Unknown Scenario

Deinterleaving Method for Radar Signals With Dwell and Switch Pulse Repetition Intervals in a Prior Unknown Scenario

Ultrasound-triggered drug release and cytotoxicity of microbubbles with diverse drug attributes

Ultrasound (US)-triggered cavitation of drug-loaded microbubbles (MBs) represents a promising approach for targeted drug delivery, with substantial benefits attainable through precise control over drug release dosage and form. This study investigates Camptothecin-loaded MBs (CPT-MBs) and Doxorubicin-loaded MBs (DOX-MBs), focusing on how properties such as hydrophilicity, hydrophobicity, and charged functional groups affect their interaction with the lipid surfaces of MBs, thereby influencing the fundamental characteristics and acoustic properties of the drug-loaded MBs. In comparison to DOX-MBs, CPT-MBs showed larger MB size (2.2 ± 0.3 and 1.4 ± 0.1 μm, respectively), a 2-fold increase in drug loading, and an 18 % reduction in leakage after 2 h at 37℃. Under 1 MHz US with a 100 ms pulse repetition interval (PRI), 1000 cycles, 5-minute duration, and 550 kPa acoustic pressure, CPT-MBs undergo inertial cavitation, while DOX-MBs undergo stable cavitation. Drug particles released from these MBs under US-induced cavitation were analyzed using dynamic light scattering, NanoSight, cryo-electron microscopy, and density gradient ultracentrifugation. Results showed that CPT-MBs mainly release free CPT, while DOX-MBs release multilayered DOX-lipid aggregates. The cytotoxicity to C6 cells induced by US-triggered cavitation of these two types of MBs also differed. DOX-lipid aggregates delayed initial uptake, leading to less pronounced short-term (2 h) effects compared to the rapid release of free CPT from CPT-MBs. These findings underscore the need to optimize drug delivery strategies by fine-tuning MB composition and US parameters to control drug release kinetics and achieve the best tumoricidal outcomes.

Investigating the effects of destructive factors on pulse repetition interval modulation type recognition using deep convolutional neural networks based on transfer learning

AbstractAutomation and self‐sufficiency in the complex environment of modern electronic warfare (EW) are critical and necessary issues in electronic intelligence and support systems to detect real‐time and accurate threat radars. The task of these systems is to search, discover, analyse, and identify the parameters of radar signals. However, recognition pulse repetition interval (PRI) modulation is challenging in natural environments due to destructive factors, including missing pulses (MP), spurious pulses (SP), and large outliers (LO) (caused by antenna scanning), which lead to noisy sequences of PRI variation patterns. The current article examines the effects of destructive factors on recognising PRI modulation in radar signals using deep convolutional neural networks (DCNNs). The article uses simulations based on the actual environment to generate data and consider destructive factors with different percentages. The number of images obtained by applying the sum of destructive factors for each range of destructive factors (with different percentages) considered is 30,000. It is common for six types of modulation. Then, the DCNN models, including VGG16, ResNet50V2, InceptionV3, Xception, and MobileNetV2, are trained using the transfer learning method. The simulation results show that the accuracy of training and testing the models decreases significantly with the increase in the percentage of destructive factors. Also, the effects of the model type on the performance of the models have been investigated, and the results have shown that some models are more resistant to destruction and retain more accuracy. Finally, this analysis shows that to improve the performance of deep neural network (DNN) techniques in the face of changes caused by destructive factors, it is necessary to pay attention to these factors and apply appropriate strategies.

Помехи неоднозначности сигналов космических РСА при использовании круглой зеркальной антенны

Reflector antennas are quite widely used in radar devices, including space-borne synthetic aperture radars (SARs). One of the problems of such SARs is to ensure the unambiguous measurement of the Doppler frequency of received signals. The shape of the Doppler spectrum of the echo signal is determined by the shape of the antenna pattern in the azimuthal plane. Thus, in general, the narrower the pattern (the larger the antenna size), the lower the level of the interference signal received at the side peaks of the ambiguity function. However, a significant increase in the antenna size is limited by the difficulties of its placement on the SAR launcher, which is especially important for small spacecraft. On the other hand, the required beam width in the elevation plane must satisfy two conditions: ensuring swath and ambiguity detuning. The time ambiguity interval is equal to half the distance of radio wave propagation during the pulse repetition interval. A technical contradiction arises between reducing the level of azimuthal and elevation ambiguity of SAR. The purpose of the article is estimation of azimuth and range ambiguities in a space-based SAR using a circular reflector antenna. In this regard, the material in the article is relevant. We consider a normal looking view which is performed using X-band space-borne SAR. The reasons for the emergence of ambiguities are considered. The necessary mathematical calculations are presented and calculations are made of the projection of circular antenna pattern and the achievable levels of azimuth and range ambiguity for space-based SARs. The obtained values were compared with similar data for a rectangular antenna. An analysis of the influence of the viewing angle and pulse repetition frequency on the ambiguity level was carried out. The results obtained make it possible to estimate the range and azimuth ambiguity level in space-borne SAR and present requirements for the need and level of additional suppression.

Do All Fast Radio Bursts Repeat? Constraints from CHIME/FRB Far Sidelobe FRBs

We report 10 fast radio bursts (FRBs) detected in the far sidelobe region (i.e., ≥5° off-meridian) of the Canadian Hydrogen Intensity Mapping Experiment (CHIME) from August 28 2018 to August 31 2021. We localize the bursts by fitting their spectra with a model of the CHIME/FRB synthesized beam response. We find that the far sidelobe events have on average ∼500 times greater fluxes than events detected in CHIME’s main lobe. We show that the sidelobe sample is therefore statistically ∼20 times closer than the main lobe sample. We find promising host galaxy candidates (P cc < 1%) for two of the FRBs, 20190112B and 20210310B, at distances of 38 and 16 Mpc, respectively. CHIME/FRB did not observe repetition of similar brightness from the uniform sample of 10 sidelobe FRBs in a total exposure time of 35,580 hr. Under the assumption of Poisson-distributed bursts, we infer that the mean repetition interval above the detection threshold of the far sidelobe events is longer than 11,880 hr, which is at least 2380 times larger than the interval from known CHIME/FRB detected repeating sources, with some caveats, notably that very narrowband events could have been missed. Our results from these far sidelobe events suggest one of two scenarios: either (1) all FRBs repeat and the repetition intervals span a wide range, with high-rate repeaters being a rare sub-population, or (2) non-repeating FRBs are a distinct population different from known repeaters.

Emitter Signal Deinterleaving Based on Single PDW with Modulation-Hypothesis-Augmented Transformer

Radar emitter signal deinterleaving based on pulse description words (PDWs) is a challenging task in the field of electronic warfare because of the parameter sparsity and uncertainty of PDWs. In this paper, a modulation-hypothesis-augmented Transformer model is proposed to identify emitters from a single PDW with an end-to-end manner. Firstly, the pulse features are enriched by the modulation hypothesis mechanism to generate I/Q complex signals from PDWs. Secondly, a multiple-parameter embedding method is proposed to expand the signal discriminative features and to enhance the identification capability of emitters. Moreover, a novel Transformer deep learning model, named PulseFormer and composed of spectral convolution, multi-layer perceptron, and self-attention based basic blocks, is proposed for discriminative feature extraction, emitter identification, and signal deinterleaving. Experimental results on synthesized PDW dataset show that the proposed method performs better on emitter signal deinterleaving in complex environments without relying on the pulse repetition interval (PRI). Compared with other deep learning methods, the PulseFormer performs better in noisy environments.

Structural frequency-based maintenance management method for steel truss bridges under atmospheric corrosion

Deterioration of steel truss bridges due to atmospheric corrosion can result in a degradation of performance that fails to meet basic safety requirements before the structure has fulfilled its design life. In order to prevent unexpected costs and premature structural damages caused by deterioration in steel truss bridges, cost-effective corrosion protection and maintenance strategies are required. In this study, a novel maintenance management method was proposed to keep the structural frequencies of steel truss bridges in between predefined boundaries throughout the service life. To pursue this aim, preventive and essential maintenance techniques were applied to provide appropriate atmospheric corrosion protection to steel truss bridges in a cost-effective way. Different repetitive repainting intervals were used for preventive maintenance, and member replacement alternatives with different ratios of total members were applied for essential maintenance approaches. Moreover, the replacement of members using conventional carbon steel, or A709-50CR was investigated separately. For the illustration of the proposed maintenance management method, a planar and a spatial steel truss bridge were selected from the literature, and maintenance strategies were applied considering two different corrosion classes. Life cycle cost analyses of steel truss bridges were conducted to evaluate different maintenance alternatives. Consequently, a cost-effective maintenance strategy was determined for each steel truss bridge in different corrosion environments.

Vanadium Aluminium Carbide Saturable Absorber in Passively Q-switched Erbium-doped Fibre Laser

Following the success of graphene saturable absorber (SA) in the development of passively Q-switched fibre lasers within the past decade, research on two-dimensional (2-D) nanomaterials SAs have shown a significant rise in demands. Nonetheless, there have been several unavoidable setbacks due to the nature of certain nanomaterials, in terms of optical, electronic and physical properties up until the reveal of transition metal carbide (MXene) nanomaterials. These metal-ceramic materials exhibit excellent optical and electrical properties as well as outstanding physical characteristics. However, their synthesis processes involve hazardous and highly acidic chemicals, which drove the researchers to adopt their precursors— ternary nanolaminate carbides (MAX phases) nanomaterials with a simpler and more exquisite fabrication process. This paper presents a passively Q-switched erbium-doped fibre laser (EDFL) with the utilisation of a MAX phases nanomaterial, vanadium aluminium carbide (V4AlC3) as SA. The V4AlC3 SA was fabricated through the solution casting technique with polyvinyl alcohol as the host polymer. The V4AlC3 SA was then integrated into a passively Q-switched EDFL configuration. The performance of V4AlC3 SA as a Q-switcher was determined with the assistance of an optical spectrum analyser (OSA), mixed domain oscilloscope (MDO), and optical power meter (OPM). The maximum pulse repetition rate and minimum pulse width were 54.37 kHz and 6.43 ms, respectively. The minimum repetition interval was 18.33 ms and the duty cycle was 0.326. The highest attained output power was 2.5 mW. Through calculation, the maximum pulse energy and peak power were identified as 45.246 nJ and 6.602 mW. The extensively gigantic pulses in nanosecond duration at 1560.16 nm was accomplished by the V4AlC3 SA, thus proving its stability which also signifies good applications especially those acquiring stable high power and energy.

System error analysis and target localization of distributed pulse radars

AbstractTarget localization for distributed radars requires precise knowledge of the calibration errors in time, frequency, and space. Previous studies focus on the sensor location error and sensor directional error in space. This paper concentrates on the system errors in time and frequency which directly affect the targets estimation of range and velocity. Based on linear frequency modulation waveform, the impact of the system errors including sample interval, pulse repetition interval, and centre frequency are analysed. An alternating optimization algorithm is proposed to achieve the target localization, velocity estimation and system errors estimation simultaneously. Simulation shows the effectiveness of the algorithm and the influence on localization due to varying number of targets and radars.

Nondestructive detection of lead content in oilseed rape leaves under silicon action using hyperspectral image

This study explored the feasibility of employing hyperspectral imaging (HSI) technology to quantitatively assess the effect of silicon (Si) on lead (Pb) content in oilseed rape leaves. Aiming at the defects of hyperspectral data with high dimension and redundant information, this paper proposed two improved feature wavelength extraction algorithms, repetitive interval combination optimization (RICO) and interval combination optimization (ICO) combined with stepwise regression (ICO-SR). The entire oilseed rape leaves were taken as the region of interest (ROI) to extract the visible near-infrared hyperspectral data within the 400.89–1002.19 nm range. In data processing, Savitzky-Golay (SG) smoothing, detrending (DT), and multiple scatter correction (MSC) were utilized for spectral data preprocessing, while recursive feature elimination (RFE), iteratively variable subset optimization (IVSO), ICO, and the two enhanced algorithms were employed to identify characteristic wavelengths. Subsequently, based on the spectral data of preprocessing and feature extraction, partial least squares regression (PLSR) and support vector regression (SVR) methods were used to construct various Pb content prediction models in oilseed rape leaves, with a comparison and analysis of each model performance. The results indicated that the two improved algorithms were more efficient in extracting representative spectral information than conventional methods, and the performance of SVR models was better than PLSR models. Finally, to further improve the prediction accuracy and robustness of the SVR models, the whale optimization algorithm (WOA) was introduced to optimize their parameters. The findings demonstrated that the MSC-RICO-WOA-SVR model achieved the best comprehensive performance, with Rp2 of 0.9436, RMSEP of 0.0501 mg/kg, and RPD of 3.4651. The results further confirmed the great potential of HSI combined with feature extraction algorithms to evaluate the effectiveness of Si in alleviating Pb stress in oilseed rape and provided a theoretical basis for determining the appropriate amount of Si application to alleviate Pb pollution in oilseed rape.

Object detection based deinterleaving of radar signals using deep learning for cognitive EW

In a real-world environment, multifunction radars (MFRs) pose major challenges to the electronic support system that is a part of cognitive electronic warfare. One of the main problems is deinterleaving of signals belonging to MFRs. The ability of MFRs to change their carrier frequency, pulse width, and pulse repetition interval (PRI) from pulse to pulse makes the deinterleaving task challenging. In this paper, an object detection based deinterleaving approach exploiting amplitude patterns caused by radar beam motions, which are determined based on radar antenna scan types, is proposed to deinterleave MFR signals. Amplitude patterns are created in two-dimensional images using amplitude (AMP) and time of arrival (TOA) parameters obtained from radar pulses. Deinterleaving is performed using a deep learning algorithm applied to these images. To the best of the authors’ knowledge, object detection based deinterleaving of radar signals using AMP-TOA images has not been considered so far. Contrary to the common approaches, in which searching for PRI patterns is required, amplitude patterns formed by the radar beam motions on the electronic support system are used in the proposed method. This enables robust identification of signals of MFRs having PRI, carrier frequency, and pulse width agility. With the proposed method, the intention of the radar, i.e., search or tracking, can be acquired in the deinterleaving stage, which ensures earlier situational awareness. The performance of the method is evaluated for varying number of radar signals from one to five through simulations, in which scenarios with missing pulses at different rates are considered. Simulation results demonstrate that, on the average, more than 0.98 mean average precision (mAP50) is achieved at 30% missing pulse rate. The performance of the method for search radar signals is slightly lower compared to that achieved for tracking radar signals due to the lower number of pulses received by the system. However, more than 0.93 AP50 is achieved for search radar signals in the presence of five radar signals with different missing pulse rates. Besides, real-time performance can be achieved using the proposed method on the GPU platform.

Apnea or normal breathing during repeated high-intensity swimming enhance sprint efficiency and which metabolic disorder?

The aim of this study was to investigate the effect of normal breath and apnea swimming on acid-base balance, physiological responses and performance during high-intensity interval training in swimming. Sixteen swimmers completed 6×50 m intervals of freestyle swimming with normal breath and apnea at maximum intensity, with 1 minute rest. Capillary blood gases (pH, PCO<inf>2</inf>, PO<inf>2</inf>, HCO<inf>3</inf>, Hct, Hb) were collected at four (4) measurement time points: 1) at rest once; 2) at rest just after the 3rd repetition; 3) at finish; and 4) at 10 min of recovery. Heart rate (HR) during swimming, lactate acid (La) concentration and swimming time (t<inf>50</inf>) were measured. Our study showed uncompensated metabolic acidosis due to increased lactic acidosis in both breathing conditions, more pronounced in apnea, and faster swimming times in apnea without different heart rate responses. Apnea during repetitive high intensity long distance interval 50 m freestyle swimming causes uncompensated metabolic acidosis but improves sprint performance.

Temporal dynamics of short-term neural adaptation across human visual cortex.

Neural responses in visual cortex adapt to prolonged and repeated stimuli. While adaptation occurs across the visual cortex, it is unclear how adaptation patterns and computational mechanisms differ across the visual hierarchy. Here we characterize two signatures of short-term neural adaptation in time-varying intracranial electroencephalography (iEEG) data collected while participants viewed naturalistic image categories varying in duration and repetition interval. Ventral- and lateral-occipitotemporal cortex exhibit slower and prolonged adaptation to single stimuli and slower recovery from adaptation to repeated stimuli compared to V1-V3. For category-selective electrodes, recovery from adaptation is slower for preferred than non-preferred stimuli. To model neural adaptation we augment our delayed divisive normalization (DN) model by scaling the input strength as a function of stimulus category, enabling the model to accurately predict neural responses across multiple image categories. The model fits suggest that differences in adaptation patterns arise from slower normalization dynamics in higher visual areas interacting with differences in input strength resulting from category selectivity. Our results reveal systematic differences in temporal adaptation of neural population responses between lower and higher visual brain areas and show that a single computational model of history-dependent normalization dynamics, fit with area-specific parameters, accounts for these differences.

Automatic reconstruction of radar pulse repetition pattern based on model learning

Compared with the traditional pulse repetition interval (PRI) parameters, PRI pattern can describe the temporal characteristics of radar more completely, and the method based on PRI pattern is more suitable for tasks of deinterleaving and identification. Therefore, effective reconstruction of PRI pattern is an important task in radar signal processing. However, previous PRI pattern reconstruction methods either set too many subjective parameters or have high requirements for the number of pulses, making it not very practical for analyzing actual radar signals. In this paper, the temporal rule of pulse train is modeled from the perspective of radar timing state switching. Then the pulses of different timing states in the pulse train are clustered based on state merging algorithms in model learning. The transitions between different timing states are established based on the pulse sets obtained by clustering. Finally, the PRI pattern of radar is reconstructed after processing interferential pulses and missing pulses. Based on the above method, this paper further proposes a method to reconstruct PRI patterns of all radars in interleaved pulse trains. Simulation results verify the effectiveness of the proposed PRI pattern reconstruction method.

Maritime Moving Target Reconstruction via MBLCFD in Staggered SAR System

Imaging maritime targets requires a high resolution and wide swath (HWRS) in a synthetic aperture radar (SAR). When operated with a variable pulse repetition interval (PRI), a staggered SAR can realize HRWS imaging, which needs to be reconstructed due to echo pulse loss and a nonuniformly sampled signal along the azimuth. The existing reconstruction algorithms are designed for stationary scenes in a staggered SAR mode, and thus, produce evident image defocusing caused by complex target motion for moving targets. Typically, the nonuniform sampling and complex motion of maritime targets aggravate the spectrum aliasing in a staggered SAR mode, causing inevitable ambiguity and degradation in its reconstruction performance. To this end, this study analyzed the spectrum of maritime targets in a staggered SAR system through theoretical derivation. After this, a reconstruction method named MBLCFD (Modified Best Linear Unbaised and Complex-Lag Time-Frequency Distribution) is proposed to refocus the blurred maritime target. First, the signal model of the maritime target with 3D rotation accompanying roll–pitch–yaw movement was established under the curved orbit of the satellite. The best linear unbiased (BLU) method was modified to alleviate the coupling of nonuniform sampling and target motion. A precise SAR algorithm was performed based on the method of inverse reversion to counteract the effect of a curved orbit and wide swath. Based on the hybrid SAR/ISAR technique, the complex-lag time-frequency distribution was exploited to refocus the maritime target images. Simulations and experiments were carried out to verify the effectiveness of the proposed method, providing precise refocusing performance in staggered mode.

Joint waveform design and resource allocation strategy for cognitive radar target situation awareness

AbstractTraditional radar systems use fixed patterns and constant electromagnetic wave transmission to illuminate targets, but they often do not effectively use prior information about targets and consume significant radar resources. Cognitive radar has emerged as a way to improve resource efficiency and address these shortcomings. A joint waveform design and resource allocation strategy for cognitive radar that incorporates target situational awareness is proposed. This method integrates the interacting multiple model algorithm and the Unscented Kalman Particle Filter to achieve target situation awareness as prior knowledge. By combining the target attitude and the frequency response function of the target radar cross section at different time points in the prior knowledge, a joint beam control and power allocation strategy is formulated and transformed into an optimization problem. In addition, a cognitive pulse‐to‐pulse frequency agile waveform design method is proposed to support multiple target tracking under complex motion models. Simulation experiments demonstrate the effectiveness of this approach in obtaining accurate target situation information, achieving beam control, and optimizing power allocation. The designed waveforms can enhance radar target detection performance and improve low probability of intercept characteristics by adjusting the pulse repetition interval. This method has significant technical value.

A Sparse SAR Imaging Method for Low-Oversampled Staggered Mode via Compound Regularization

High-resolution wide-swath (HRWS) imaging is the research focus of the modern spaceborne synthetic-aperture radar (SAR) imaging field, with significant relevance and vast application potential. Staggered SAR, as an innovative imaging system, mitigates blind areas across the entire swath by periodically altering the radar pulse repetition interval (PRI), thereby extending the swath width to multiples of that achievable by conventional systems. However, the staggered mode introduces inherent challenges, such as nonuniform azimuth sampling and echo data loss, leading to azimuth ambiguities and substantially impacting image quality. This paper proposes a sparse SAR imaging method for the low-oversampled staggered mode via compound regularization. The proposed method not only effectively suppresses azimuth ambiguities arising from nonuniform sampling without necessitating the restoration of missing echo data, but also incorporates total variation (TV) regularization into the sparse reconstruction model. This enhances the accurate reconstruction of distributed targets within the scene. The efficacy of the proposed method is substantiated through simulations and real data experiments from spaceborne missions.

Investigate of unsimilar effect of longer rest interval in multiple sets by performing deload repetition training regimes to micro strength gain change on light weeks of weightlifters

Continuum zone repetition training strategies are one of repetition manipulation both multiple set and repetition zone periodization on weightlifters. The present study was aimed to strength level on weightlift exercise condition of weightlifting athletes, deload repetition resistance training regimes involved in proper rest interval and repetition range by performing multiple set and longer rest interval within different maximize performance on light weeks. The experimental study was conducted to 2 olympic, 1 national, 6 professional weightlifters participated on age (16-21 years) this study. With study maximize performance condition over 3 week resistance training finished to weightlifters. Priority experimental pre-test and post-test outcomes after resistance training showed significant different (p &lt; .05) in dynamic strength to countermovement jump shrug (p = .035; d = 0.69; = 17.78%). However, significant different in low load repetition failure to overhead press (p = .007; d = 0.86; = 5.56%), high load repetition failure to bent-over row (p = .017; d = 1.14; = 8.00%), low and high load repetition failure to countermovement jump shrug (p = .017; d = 1.17; = 13.78% - p = .048; d = 0.73; = 5.44%), in localize endurance not significant performance level to resistance exercise performance. However, isometric strength detected to overhead press (p = .016; d = 1.30; = -0.72%), bent-over row (p = .012; d = 1.67; = -0.51%) and countermovement jump shrug (p = .004; d = 2.45; = -0.48%) in weightlifters. Unsimilar longer rest interval multiple set configuration suggested to maximize strength gain on short time deload repetition training regimes by manipulated repetition and set addition rest interval currently manipulation of weightlifters.

On a Closer Look of a Doppler Tolerant Noise Radar Waveform in Surveillance Applications.

The prevalence of Low Probability of Interception (LPI) and Low Probability of Exploitation (LPE) radars in contemporary Electronic Warfare (EW) presents an ongoing challenge to defense mechanisms, compelling constant advances in protective strategies. Noise radars are examples of LPI and LPE systems that gained substantial prominence in the past decade despite exhibiting a common drawback of limited Doppler tolerance. The Advanced Pulse Compression Noise (APCN) waveform is a stochastic radar signal proposed to amalgamate the LPI and LPE attributes of a random waveform with the Doppler tolerance feature inherent to a linear frequency modulation. In the present work, we derive closed-form expressions describing the APCN signal's ambiguity function and spectral containment that allow for a proper analysis of its detection performance and ability to remove range ambiguities as a function of its stochastic parameters. This paper also presents a more detailed address of the LPI/LPE characteristic of APCN signals claimed in previous works. We show that sophisticated Electronic Intelligence (ELINT) systems that employ Time Frequency Analysis (TFA) and image processing methods may intercept APCN and estimate important parameters of APCN waveforms, such as bandwidth, operating frequency, time duration, and pulse repetition interval. We also present a method designed to intercept and exploit the unique characteristics of the APCN waveform. Its performance is evaluated based on the probability of such an ELINT system detecting an APCN radar signal as a function of the Signal-to-Noise Ratio (SNR) in the ELINT system. We evaluated the accuracy and precision of the random variables characterizing the proposed estimators as a function of the SNR. Results indicate a probability of detection close to 1 and show good performance, even for scenarios with a SNR slightly less than -10 dB. The contributions in this work offer enhancements to noise radar capabilities while facilitating improvements in ESM systems.