An Efficient Strategy for Accurate Detection and Localization of UAV Swarms

Zheng, Jianhua; Chen, Rouxuan; Yang, Tianyuan; Liu, Xin; Liu, Hongwei; Su, Tao; Wan, Liangtian

doi:10.1109/jiot.2021.3064376

Cited by 67 publications

(37 citation statements)

References 28 publications

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“…This kind of processing method is statistically optimal, while it is normally computationally expensive [17][18][19]. The keystone transform (KT) can speed up the MLE method and its extensions [20][21][22][23][24][25]. Unfortunately, under many conditions, the KT may encounter the half-blind-velocity effect and Doppler spectrum coupling [26,27] which may induce the energy spread.…”

Section: Prior Workmentioning

confidence: 99%

“…In past decades, many researchers have contributed a lot to the development of the coherent methods [4,5,17,36,37]. Summarizing typical methods of most researchers, we find that the coherent methods can be generally categorized into (1) the linear RCM compensation and inter-pulse energy integration are processed uniformly via the same physical variable, such as the coherent methods proposed in [16][17][18][19], and (2) the linear RCM compensation and inter-pulse energy integration are processed separately via two different physical variables, such as the coherent methods proposed in [24][25][26][27][28][29][30][31][32][33][34][35]. Here, we separately name the first and second categories as the processing unification (PU) mode and processing separation (PS) mode.…”

Section: Prior Workmentioning

confidence: 99%

“…Here, we separately name the first and second categories as the processing unification (PU) mode and processing separation (PS) mode. Compared to the PU mode, the PS mode can reduce the computational cost at the expense of some losses, such as the anti-noise performance loss, cross-term interference, and so on [24][25][26][27][28][29][30][31][32][33][34][35]. It is worthwhile noting that, although the coherent methods have come a long way, some theoretical studies were still lost, such as the blind speed sidelobe (BSSL) and the relationship between the PU and PS modes [4][5][6][7][8][9][10][11][12][13][14][15][16][17].…”

Section: Prior Workmentioning

confidence: 99%

“…Unfortunately, target motions induce the RCM and DFM, and the signal energy cannot be coherently integrated via performing the Fourier transform on s com (t, t m ) along t m . For the long-time coherent integration, many successful coherent methods have been proposed [16][17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34][35]. Referring to the published literature and conducting analyses of Equation (7), we summarize interference factors to realize the long-time integration and list them below for further research.…”

Section: Signal Model and Problem Formulationmentioning

confidence: 99%

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Generalized Dechirp-Keystone Transform for Radar High-Speed Maneuvering Target Detection and Localization

et al. 2021

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The multivariate range function of the high-speed maneuvering target induces modulations on both the envelop and phase, i.e., the range cell migration (RCM) and Doppler frequency migration (DFM) which degrade the long-time coherent integration used for detection and localization. To solve this problem, many long-time coherent integration methods have been proposed. Based on mechanisms of typical methods, this paper names two signal processing modes, i.e., processing unification (PU) mode and processing separation (PS) mode, and presents their general forms. Thereafter, based on the principle of the PS mode, a novel long-time coherent integration method, known as the generalized dechirp-keystone transform (GDKT), is proposed for radar high-speed maneuvering target detection and localization. The computational cost, energy integration, peak-to-sidelobe level (PSL), resolution, and anti-noise performance of the GDKT are analyzed and compared with those of the maximum likelihood estimation (MLE) method and keystone transform-dechirp (KTD) method. With mathematical analyses and numerical simulations, we validate two main superiorities of the GDKT, including (1) the statistically optimal anti-noise performance, and (2) the low computational cost. The real radar data is also used to validate the GDKT. It is worthwhile noting that, based on closed analytical formulae of the MLE method, KTD method, and GDKT, several doubts in radar high-speed maneuvering target detection and localization are mathematically interpreted, such as the blind speed sidelobe (BSSL) and the relationship between the PU and PS modes.

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Section: Prior Workmentioning

confidence: 99%

Section: Prior Workmentioning

confidence: 99%

Section: Prior Workmentioning

confidence: 99%

Section: Signal Model and Problem Formulationmentioning

confidence: 99%

See 2 more Smart Citations

Generalized Dechirp-Keystone Transform for Radar High-Speed Maneuvering Target Detection and Localization

et al. 2021

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“…The DL method in [18] has the ability to constrain the uncertainty set of the steering vector and obtain the loading factor under the circumstances of the steering vector estimation error. From another point of view, if the estimation error for the steering vector is not accurate enough, the performance of beamformers will decline sharply or even fail totally [19], [20]. There is no doubt that many other robust beamforming methods have been carried out, such as the convex optimization-based method [21], the Bayesian beamformer [22], and the fuzzy-inference-based beamformer [23].…”

Section: Introductionmentioning

confidence: 99%

Robust Array Beamforming via an Improved Chicken Swarm Optimization Approach

2021

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Robust array beamforming is a challenging task in radar, sonar and communications due to the influence of direction of arrival (DOA) mismatch and sensor position errors. However, how to enhance the robustness of beamforming is a key issue in antenna arrays. The current paper focuses on a novel approach called the improved chicken swarm optimization (ICSO) method to settle the optimization model of conventional linearly constrained minimum variance (LCMV) based on support vector machine (SVM) to against the mismatch problems as well as control the sidelobe level (SLL). As far as the ICSO method is concerned, considering that the particle swarm optimization (PSO) algorithm has outstanding convergence performance in the early iteration, the dominance of the alpha wolf in the grey wolf optimization (GWO) algorithm and the innovative mutual attraction mechanism in the firefly algorithm (FA), and we introduce these three strategies into the solution update method of conventional chicken swarm optimization (CSO) algorithm for achieving better optimization capability. Moreover, an operation of removing duplicate solutions is proposed to enhance the utilization of the population. In terms of the SVM-based LCMV beamforming algorithm, we adopt the so-called linear ε -insensitive loss function to reconstruct the final cost function of LCMV by penalizing the errors between the actual and ideal array responses. Finally, we conduct simulations to evaluate the performance of the swarm intelligent optimization algorithms under an ideal scenario without mismatch and an actual scenario with the mismatch, respectively. And the results demonstrate that the developed ICSO algorithm obtains excellent robustness for different scenarios compared to PSO, FA, GWO and CSO optimization algorithms.INDEX TERMS Robust array beamforming, improved chicken swarm optimization, linearly constrained minimum variance, support vector machine, sidelobe level, steering vector errors.

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Modular design automation of the morphologies, controllers, and vision systems for intelligent robots: a survey

Wang

Mai

et al. 2023

Vis. Intell.

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Design automation is a core technology in industrial design software and an important branch of knowledge-worker automation. For example, electronic design automation (EDA) has played an important role in both academia and industry. Design automation for intelligent robots refers to the construction of unified modular graph models for the morphologies (body), controllers (brain), and vision systems (eye) of intelligent robots under digital twin architectures, which effectively supports the automation of the morphology, controller, and vision system design processes of intelligent robots by taking advantage of the powerful capabilities of genetic programming, evolutionary computation, deep learning, reinforcement learning, and causal reasoning in model representation, optimization, perception, decision making, and reasoning. Compared with traditional design methods, MOdular DEsigN Automation (MODENA) methods can significantly improve the design efficiency and performance of robots, effectively avoiding the repetitive trial-and-error processes of traditional design methods, and promoting automatic discovery of innovative designs. Thus, it is of considerable research significance to study MODENA methods for intelligent robots. To this end, this paper provides a systematic and comprehensive overview of applying MODENA in intelligent robots, analyzes the current problems and challenges in the field, and provides an outlook for future research. First, the design automation for the robot morphologies and controllers is reviewed, individually, with automated design of control strategies for swarm robots also discussed, which has emerged as a prominent research focus recently. Next, the integrated design automation of both the morphologies and controllers for robotic systems is presented. Then, the design automation of the vision systems of intelligent robots is summarized when vision systems have become one of the most important modules for intelligent robotic systems. Then, the future research trends of integrated “Body-Brain-Eye” design automation for intelligent robots are discussed. Finally, the common key technologies, research challenges and opportunities in MODENA for intelligent robots are summarized.

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An Efficient Strategy for Accurate Detection and Localization of UAV Swarms

Cited by 67 publications

References 28 publications

Generalized Dechirp-Keystone Transform for Radar High-Speed Maneuvering Target Detection and Localization

Generalized Dechirp-Keystone Transform for Radar High-Speed Maneuvering Target Detection and Localization

Robust Array Beamforming via an Improved Chicken Swarm Optimization Approach

Modular design automation of the morphologies, controllers, and vision systems for intelligent robots: a survey

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