Sinusoidal Frequency Modulation Sparse Recovery for Precession Rate Estimation Using Low-Frequency Long-Range Radar
TL;DR: A new approach, named sinusoidal frequency modulation sparse recovery (SFMSR) for m-D analysis with LFLRR, by exploiting the micro motion spectrum sparsity in SFM signal space and employing the Fourier modulation dictionary.
Abstract: Low-frequency long-range radars (LFLRRs) are assumed to lack the ability of extracting targets micro motion signature, due to their low and nonuniform track update rate, as well as the weak micro Doppler (m-D) owing to their large wave length. The recently proposed sinusoidal frequency modulated (SFM) Fourier transform can achieve a longer integral period gain, and consequently provides a new perspective for extracting weak m-D signature. However, its direct application is unavailable for LFLRRs, since their track update rate is very low and may not even be constant. This paper derives a new approach, named sinusoidal frequency modulation sparse recovery (SFMSR) for m-D analysis with LFLRR, by exploiting the micro motion spectrum sparsity in SFM signal space. SFMSR employs the Fourier modulation dictionary, which is determined only by the frequency in SFM signal space. Unlike other sparse representation-based methods whose dictionary is discretization of a 3-D space parameterized by the micro motion amplitude, frequency, and initial phase, the SFMSR reduces the m-D analysis to 1-D parameter optimization, and therefore can enhance the precision, stability, and computational efficiency simultaneously. The temporally correlated sparse Bayesian learning in SFM signal space is employed to decompose the signal and produce highly sparse solutions. The simulation results indicate that the proposed method outperforms the existing methods in accuracy and robustness, which can provide satisfactory performance even when the carrier frequency is 430 MHz and the average data rate is 0.5 Hz.
Citations
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Journal Article•
TL;DR: The Micro-Doppler Effect in Radar by V. C. Chen as discussed by the authors is a book review of "The Micro Doppler effect in radar" by Chen et al. 2011. 290pp + diskette.
Abstract: This is a book review of 'The Micro-Doppler Effect in Radar' by V. C. Chen. Artech House, 16 Sussex Street, London, SW1V 4RW, UK. 2011. 290pp + diskette. Illustrated. £90. ISBN 978-1-60807-057-2.
439 citations
TL;DR: The cyclostationary phase analysis (CPA) is proposed to estimate the m-D parameters for radar detection of small UAVs, which could simplify the procedure of the parameter estimation, as well as reducing them-D interference.
Abstract: Robust radar detection for small unmanned aerial vehicles (UAVs) is a challenging problem, as UAVs tend to fly at slow speed and low altitude, with a small radar cross section. Those properties may lead to difficulties in separating echoes from a significant clutter response. In this case, micro-Doppler (m-D) effect induced by the rotation of the rotor blades would be another preferable signature to enhance the discrimination between ground clutter and UAVs returns, as well as associated correct labels to small UAVs among others (such as birds). However, the detection presents two stubborn problems. First, the m-D signal always consists of multiple components, one of which is induced by the vibration of the platform. It is inevitable that the estimation precision would be affected by the m-D interference. Second, the m-D components would be rather weak. It could not always be observed due to the shelter of Doppler signal induced by the translation of the platform. In this paper, we propose the cyclostationary phase analysis (CPA) to estimate the m-D parameters for radar detection of small UAVs. This method utilizes the phase term of the returned signal as the input of the cyclostationary analysis. It could simplify the procedure of the parameter estimation, as well as reducing the m-D interference. Moreover, since the phase term of the Doppler components is not cyclostationary, the CPA could eliminate the impact of Doppler signal. Simulations and filed experiments are provided to showing an outranking performance than the existing methods in estimation precision.
40 citations
Cites background from "Sinusoidal Frequency Modulation Spa..."
...However, the model is difficult to predict, especially for the nonideal scattering points and in a great clutter environment [14]....
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TL;DR: The micro-Doppler can be reduced several dozen times its true value to avoid ambiguity through intra-pulse interference processing, and its high estimation precision and excellent noise immunity are verified by Monte Carlo simulation.
Abstract: Micro-Doppler, induced by micro-motion of targets, is an important characteristic of target recognition once extracted via parameter estimation methods. However, micro-Doppler is usually too significant to result in ambiguity in the terahertz band because of its relatively high carrier frequency. Thus, a micro-Doppler ambiguity resolution method for wideband terahertz radar using intra-pulse interference is proposed in this paper. The micro-Doppler can be reduced several dozen times its true value to avoid ambiguity through intra-pulse interference processing. The effectiveness of this method is proved by experiments based on a 0.22 THz wideband radar system, and its high estimation precision and excellent noise immunity are verified by Monte Carlo simulation.
9 citations
Cites methods from "Sinusoidal Frequency Modulation Spa..."
...The traditional methods based on circular correlation coefficients or sparse recovery for period estimation [12,13], as well as inverse Radon transform [8] and Fourier–Bessel transform [14] for micro-Doppler and initial phase estimation, will also be available....
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...The traditional methods based on circular correlation coefficients or sparse recovery for period estimation [12,13], as well as inverse Radon transform [8] and Fourier–Bessel transform [14] for micro‐Doppler and initial phase estimation, will also be available....
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TL;DR: In this paper , an identification method for estimating the rotor blade width based on the peak time-shift effect is proposed for the first time, which has the characteristics of high-precision extraction of rotor width, and its effectiveness is verified by simulation and experiments.
Abstract: The aim of this study was to solve the problem that the existing identification parameters of rotor unmanned aerial vehicles (UAVs) are few and limited by the detection mode, and an identification method for estimating the rotor blade width based on the peak time-shift effect is proposed for the first time. Taking the width of the rotor blade as the parameter to identify the rotor of UAVs, the time-shift effect and its relationship with rotor blade width are verified by theoretical analysis and simulation. The proposed time-shift method has the characteristics of high-precision extraction of rotor width, and its effectiveness is verified by simulation and experiments. The aspect ratio of the rotor is accurately extracted based on the proposed time-shift method under the condition of an unknown pitch angle. Simulation results show that the estimation accuracy of the width and aspect ratio is up to 98 and 98.4%, respectively. The experimental results show that the relative errors of the width and aspect ratio are less than 7 and 4%, respectively. This study provides the theoretical basis and technical support for the high-accuracy identification of rotorcraft UAVs.
2 citations
TL;DR: The overall dynamic range of the new single-type-substrate system has been improved by 15 dB, while miniaturising the overall size to enable light weight portable packaging, and the achieved performance is the highest reported in literature for similar type of radar testbeds that can be used for advanced industrial and military applications.
Abstract: A miniaturisation of an architecture novel for an S-band radar testbed is reported. The fabricated radar is a low-cost, high-performance and printed circuit board (PCB)-based system. The system and its individual sub-stages along with the miniaturised radar prototype have been simulated using advanced design system and applied wave research. Printed filters replacing bulky microwave components and impedance matching techniques to minimise power losses in the amplifiers are reported. The measured results are compared with electromagnetic simulations. The reduction in the overall system package size is significant, without degrading performance. The insertion loss and signal reflections through different stages are reduced, which contributed significantly to the enhancement of the measurement. The implemented radar PCB prototype used stretch processing technique to achieve high dynamic range of 75 dB, experimentally measured over a wide signal bandwidth of 600 MHz. This achieves a range resolution of 0.25 m. The overall dynamic range of the new single-type-substrate system has been improved by 15 dB, while miniaturising the overall size to enable light weight portable packaging. The achieved performance is, to the best of the authors’ knowledge, the highest reported in literature for similar type of radar testbeds that can be used for advanced industrial and military applications.
2 citations
References
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TL;DR: It is demonstrated that by exploiting a probabilistic Bayesian learning framework, the 'relevance vector machine' (RVM) can derive accurate prediction models which typically utilise dramatically fewer basis functions than a comparable SVM while offering a number of additional advantages.
Abstract: This paper introduces a general Bayesian framework for obtaining sparse solutions to regression and classification tasks utilising models linear in the parameters Although this framework is fully general, we illustrate our approach with a particular specialisation that we denote the 'relevance vector machine' (RVM), a model of identical functional form to the popular and state-of-the-art 'support vector machine' (SVM) We demonstrate that by exploiting a probabilistic Bayesian learning framework, we can derive accurate prediction models which typically utilise dramatically fewer basis functions than a comparable SVM while offering a number of additional advantages These include the benefits of probabilistic predictions, automatic estimation of 'nuisance' parameters, and the facility to utilise arbitrary basis functions (eg non-'Mercer' kernels) We detail the Bayesian framework and associated learning algorithm for the RVM, and give some illustrative examples of its application along with some comparative benchmarks We offer some explanation for the exceptional degree of sparsity obtained, and discuss and demonstrate some of the advantageous features, and potential extensions, of Bayesian relevance learning
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Book•
01 Jan 2004TL;DR: This paper establishes the possibility of stable recovery under a combination of sufficient sparsity and favorable structure of the overcomplete system and shows that similar stability is also available using the basis and the matching pursuit algorithms.
Abstract: Overcomplete representations are attracting interest in signal processing theory, particularly due to their potential to generate sparse representations of signals. However, in general, the problem of finding sparse representations must be unstable in the presence of noise. This paper establishes the possibility of stable recovery under a combination of sufficient sparsity and favorable structure of the overcomplete system. Considering an ideal underlying signal that has a sufficiently sparse representation, it is assumed that only a noisy version of it can be observed. Assuming further that the overcomplete system is incoherent, it is shown that the optimally sparse approximation to the noisy data differs from the optimally sparse decomposition of the ideal noiseless signal by at most a constant multiple of the noise level. As this optimal-sparsity method requires heavy (combinatorial) computational effort, approximation algorithms are considered. It is shown that similar stability is also available using the basis and the matching pursuit algorithms. Furthermore, it is shown that these methods result in sparse approximation of the noisy data that contains only terms also appearing in the unique sparsest representation of the ideal noiseless sparse signal.
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Book•
01 Jan 2005
TL;DR: This revised edition of Fundamentals of Radar Signal Processing provides in-depth coverage of radar digital signal processing fundamentals and applications and has been updated to include coverage of measurement accuracy and target tracking.
Abstract: This detailed guide clearly and concisely presents radar digital signal processing for both practicing engineers and engineering students. This revised edition of Fundamentals of Radar Signal Processing provides in-depth coverage of radar digital signal processing (DSP) fundamentals and applications. It has been updated to include coverage of measurement accuracy and target tracking. Additionally, to make it more useful as a teaching tool, it now includes end-of-chapter problems and a solutions manual. New to this Edition: New chapter on Measurement Accuracy and Target Tracking Two new appendices--Important Digital Signal Processing Facts; Important Probability Density Function and Their Relationships Addition of 20 to 30 problems to ends of chapters Solutions manual
1,765 citations
TL;DR: This paper adapts SBL to the signal processing problem of basis selection from overcomplete dictionaries, proving several results about the SBL cost function that elucidate its general behavior and providing solid theoretical justification for this application.
Abstract: Sparse Bayesian learning (SBL) and specifically relevance vector machines have received much attention in the machine learning literature as a means of achieving parsimonious representations in the context of regression and classification. The methodology relies on a parameterized prior that encourages models with few nonzero weights. In this paper, we adapt SBL to the signal processing problem of basis selection from overcomplete dictionaries, proving several results about the SBL cost function that elucidate its general behavior and provide solid theoretical justification for this application. Specifically, we have shown that SBL retains a desirable property of the /spl lscr//sub 0/-norm diversity measure (i.e., the global minimum is achieved at the maximally sparse solution) while often possessing a more limited constellation of local minima. We have also demonstrated that the local minima that do exist are achieved at sparse solutions. Later, we provide a novel interpretation of SBL that gives us valuable insight into why it is successful in producing sparse representations. Finally, we include simulation studies comparing sparse Bayesian learning with basis pursuit and the more recent FOCal Underdetermined System Solver (FOCUSS) class of basis selection algorithms. These results indicate that our theoretical insights translate directly into improved performance.
1,339 citations
"Sinusoidal Frequency Modulation Spa..." refers background in this paper
..., few nonzero weights, which can be reduced to a problem of sparse representation [27], can be rewritten as follows:...
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TL;DR: A fast algorithm for overcomplete sparse decomposition, called SL0, is proposed, which tries to directly minimize the l 1 norm.
Abstract: In this paper, a fast algorithm for overcomplete sparse decomposition, called SL0, is proposed. The algorithm is essentially a method for obtaining sparse solutions of underdetermined systems of linear equations, and its applications include underdetermined sparse component analysis (SCA), atomic decomposition on overcomplete dictionaries, compressed sensing, and decoding real field codes. Contrary to previous methods, which usually solve this problem by minimizing the l 1 norm using linear programming (LP) techniques, our algorithm tries to directly minimize the l 1 norm. It is experimentally shown that the proposed algorithm is about two to three orders of magnitude faster than the state-of-the-art interior-point LP solvers, while providing the same (or better) accuracy.
1,033 citations
"Sinusoidal Frequency Modulation Spa..." refers methods in this paper
...Thus, translation estimation and compensation is required before SFMSR. Assuming that the translation can be modeled as a 2-order polynomial function, we employ the smoothed L0 (SL0) [28] algorithm for coarse Doppler estimation, which can be used for compensating the migration between Doppler bins induced by translation....
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...Assuming that the translation can be modeled as a 2-order polynomial function, we employ the smoothed L0 (SL0) [28] algorithm for coarse Doppler estimation, which can be used for compensating the migration between Doppler bins induced by translation....
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