A Practical Reinforcement Learning Framework for Automatic Radar Detection

At present,the parameters of radar detection rely heavily on manual adjustment and empirical knowledge,resulting in low automation.Traditional manual adjustment methods cannot meet the requirements of modern radars for high efficiency,high precision,and high automation.Therefore,it is necessary to explore a new intelligent radar control learning framework and technology to improve the capability and automation of radar detection.Reinforcement learning is popular in decision task learning,but the shortage of samples in radar control tasks makes it difficult to meet the requirements of reinforcement learning.To address the above issues,we propose a practical radar operation reinforcement learning framework,and integrate offline reinforcement learning and meta-reinforcement learning methods to alleviate the sample requirements of reinforcement learning.Experimental results show that our method can automatically perform as humans in radar detection with real-world settings,thereby promoting the practical application of reinforcement learning in radar operation.

HQNN-SFOP:Hybrid Quantum Neural Networks with Signal Feature Overlay Projection for Drone Detection Using Radar Return Signals-A Simulation

With the wide application of drone technology,there is an increasing demand for the detection of radar return signals from drones.Existing detection methods mainly rely on time-frequency domain feature extraction and classical machine learning algorithms for image recognition.This method suffers from the problem of large dimensionality of image features,which leads to large input data size and noise affecting learning.Therefore,this paper proposes to extract signal time-domain statistical features for radar return signals from drones and reduce the feature dimension from 512×4 to 16 dimensions.However,the downscaled feature data makes the accuracy of traditional machine learning algorithms decrease,so we propose a new hybrid quantum neural network with signal feature overlay projection(HQNN-SFOP),which reduces the dimensionality of the signal by extracting the statistical features in the time domain of the signal,introduces the signal feature overlay projection to enhance the expression ability of quantum computation on the signal features,and introduces the quantum circuits to improve the neural network’s ability to obtain the inline relationship of features,thus improving the accuracy and migration generalization ability of drone detection.In order to validate the effectiveness of the proposed method,we experimented with the method using the MM model that combines the real parameters of five commercial drones and random drones parameters to generate data to simulate a realistic environment.The results show that the method based on statistical features in the time domain of the signal is able to extract features at smaller scales and obtain higher accuracy on a dataset with an SNR of 10 dB.On the time-domain feature data set,HQNNSFOP obtains the highest accuracy compared to other conventional methods.In addition,HQNN-SFOP has good migration generalization ability on five commercial drones and random drones data at different SNR conditions.Our method verifies the feasibility and effectiveness of signal detection methods based on quantum computation and experimentally demonstrates that the advantages of quantum computation for information processing are still valid in the field of signal processing,it provides a highly efficient method for the drone detection using radar return signals.

Detection with High Resolution Radar: Advanced Topics & Potential Applications

In a previous companion paper [1], the potential advantages of high resolution radar for improved target detection were introduced. In particular, the concept of shaping both the transmitted waveform and the receiving processor in accordance to the expected target down-range profile was highlighted and performance predictions were provided. In this paper, we present and evaluate an adaptive scheme devised to on-line estimate the target profile, in order to overcome a limited a-priori knowledge. In addition, we introduce a more general model of target impulse response, based on a statistical description, and we discuss the corresponding processing scheme and detection performance.

Short-time maritime target detection based on polarization scattering characteristics

In this paper,a detection method combining Cameron decomposition based on polarization scattering characteristics in sea clutter background is proposed.Firstly,the Cameron decomposition is exploited to fuse the radar echoes of full polarization channels at the data level.Due to the artificial material structure on the surface of the target,it can be shown that the non-reciprocity of the target cell is stronger than that of the clutter cell.Then,based on the analysis of the decomposition results,a new feature with scattering geometry characteristics in polarization domain,denoted as Cameron polarization decomposition scattering weight(CPD-SW),is extracted as the test statistic,which can achieve more detailed descriptions of the clutter scattering characteristics utilizing the difference between their scattering types.Finally,the superiority of the proposed CPD-SW detector over traditional detectors in improving detection performance is verified by the IPIX measured dataset,which has strong stability under short-time observation in threshold detection and can also improve the separability of feature space zin anomaly detection.

A novel oil spill detection method from synthetic aperture radar imageries via a bidimensional empirical mode decomposition

Oil spills pose a major threat to ocean ecosystems and their health. Synthetic aperture radar（SAR） sensors can detect oil spills on the sea surface. These oil spills appear as dark spots in SAR images. However, dark formations can be caused by a number of phenomena. It is aimed to distinguishing oil spills or look-alike objects. A novel method based on a bidimensional empirical mode decomposition is proposed. The selected dark formations are first decomposed into several bidimensional intrinsic mode functions and the residue. Subsequently, 64 dimension feature sets are calculated using the Hilbert spectral analysis and five new features are extracted with a relief algorithm. Mahalanobis distances are then used for classification. Three data sets containing oil spills or look-alikes are used to test the accuracy rate of the method. The accuracy rate is more than 90%. The experimental results demonstrate that the novel method can detect oil spills validly and accurately.

Design and Tests of A Cable Detection Laser Imaging Radar System

Rotorcraft in low-level flight is endangered by power lines or telephone wires. The development of automation tools that can detect obstacles in the flight path and warn the crew would significantly reduce pilot workload and increase safety. Therefore, a cable detection radar system is developed The real-time dynamic imaging synchronizing with radar space scanning has been implemented in developed ladar system. The requirements of the flight mission to prevent ＂wire strike＂ are analyzed and estimated, the advantages and disadvantages of the millimeter wave system with the laser system are weighted The result shows that Laser system is the best suited for helicopter avoidance obstacle. In addition, several design gist of detecting wire radar that was used in the developed ladar system is proposed and the developed zero backlash imaging technology and several advanced warning function are described. The detailed results of system ground tests and the performances description are presented The ground test of the developed ladar system has demonstrated that the developed imaging ladar system performance can achieve and satisfy the requirements of the mission to prevent ＂wire strike＂.

Investigation of system structure and information processing mechanism for cognitive skywave over-the-horizon radar

Based on the cognitive radar concept and the basic connotation of cognitive skywave over-the-horizon radar（SWOTHR）, the system structure and information processingmechanism about cognitive SWOTHR are researched. Amongthem, the hybrid network system architecture which is thedistributed configuration combining with the centralized cognition and its soft/hardware framework with the sense-detectionintegration are proposed, and the information processing framebased on the lens principle and its information processing flowwith receive-transmit joint adaption are designed, which buildand parse the work law for cognition and its self feedback adjustment with the lens focus model and five stages informationprocessing sequence. After that, the system simulation andthe performance analysis and comparison are provided, whichinitially proves the rationality and advantages of the proposedideas. Finally, four important development ideas of futureSWOTHR toward ＂high frequency intelligence information processing system＂ are discussed, which are scene information fusion, dynamic reconfigurable system, hierarchical and modulardesign, and sustainable development. Then the conclusion thatthe cognitive SWOTHR can cause the performance improvement is gotten.

Radar fast long-time coherent integration via TR-SKT and robust sparse FRFT

Long-time coherent integration(LTCI)is an effective way for radar maneuvering target detection,but it faces the problem of a large number of search parameters and large amount of calculation.Realizing the simultaneous compensation of the range and Doppler migrations in complex clutter back-ground,and at the same time improving the calculation efficiency has become an urgent problem to be solved.The sparse transformation theory is introduced to LTCI in this paper,and a non-parametric searching sparse LTCI(SLTCI)based maneuvering target detection method is proposed.This method performs time reversal(TR)and second-order Keystone transform(SKT)in the range frequency&slow-time data to complete high-order range walk compensation,and achieves the coherent integra-tion of maneuvering target across range and Doppler units via the robust sparse fractional Fourier transform(RSFRFT).It can compensate for the nonlinear range migration caused by high-order motion.S-band and X-band radar data measured in sea clutter background are used to verify the detection performance of the proposed method,which can achieve better detection performance of maneuvering targets with less computational burden compared with several popular integration methods.

SAR target detection based on the optimal fractional Gabor spectrum feature

In this paper,an algorithm based on a fractional time-frequency spectrum feature is proposed to improve the accuracy of synthetic aperture radar(SAR)target detection.By extending the fractional Gabor transform(FrGT)into two dimensions,the fractional time-frequency spectrum feature of an image can be obtained.In the achievement process,we search for the optimal order and design the optimal window function to accomplish the two-dimensional optimal FrGT.Finally,the energy attenuation gradient(EAG)feature of the optimal time-frequency spectrum is extracted for high-frequency detection.The simulation results show the proposed algorithm has a good performance in SAR target detection and lays the foundation for recognition.

PERFORMANCE EVALUATION OF OSSO-CFAR WITH BINARY INTEGRATION IN WEIBULL BACKGROUND

The performance of the Ordered-Statistic Smallest Of (OSSO) Constant False Alarm Rate (CFAR) with binary integration in Weibull background with known shape parameter is analyzed, in the cases that the processor operates in homogeneous background and non-homogeneous situation caused by multiple targets and clutter edge. The analytical models of this scheme for the performance evaluation are given. It is shown that the OSSO-CFAR with binary integration can greatly improve the detection performance with respect to the single pulse processing case. As the clutter background becomes spiky, a high threshold S of binary integration (S/M) is required in order to obtain a good detection performance in homogeneous background. Moreover, the false alarm performance of the OSSO-CFAR with binary integration is more sensitive to the changes of shape parameter or power level of the clutter background.

Switching variability index based multiple strategy CFAR detector

A switching variability index （SVl） constant false alarm rate （CFAR） detector is proposed for improving the detection performance of VI-CFAR detectors in multiple targets backgrounds. When the presence of non-homogeneity in CFAR reference windows is indicated by a VI-CFAR detector, a switching- CFAR detector is introduced to optimize the performance of the VI-CFAR detector in homogeneous, multiple targets and clutter edge backgrounds. The structure and parameters selection method of the SVI-CFAR detector is presented. Comparisons with classic CFAR detectors and recently proposed detectors are also given. Theoretical analysis and simulation results show that SVICFAR detector maintains the good performance of the VI-CFAR detector in homogeneous and clutter edge backgrounds, while greatly improving the capacity of anti-multi targets.

IMPROVEMENT TO THE EVAPORATION DUCT MODEL BY INTRODUCING NONLINEAR SIMILARITY FUNCTIONS IN STABLE CONDITIONS

Modified refractivity (M) profile is an important parameter to describe the atmospheric refraction environment,as well as a key factor in uniquely evaluating electromagnetic propagation effects.In order to improve the model-derived M profile in stable (especially very stable) conditions,three nonlinear similarity functions,namely BH91,CB05,SHEBA07,are introduced in this paper to improve the original Babin_V25 model,and the performances of these modified models are verified based on the hydrometeorological observations from tower platforms,which are finally compared with the original Babin_V25 model and Local_HYQ92 model.Results show that introducing nonlinear similarity functions can significantly improve the model-derived M profile;especially,the newly developed SHEBA07 functions manage to reduce the predicted root mean square (rms) differences of M and M slope (for both 0-5m and 5-40m) by 64.5%,16.6%,and 60.4%,respectively in stable conditions.Unfortunately,this improved method reacts little on the evaporation duct height;in contrast,Local_HYQ92 model is capable of reducing the predicted rms differences of M,M slope (for both 0-5m and 5-40m),and evaporation duct height by 76.7%,40.2%,83.7%,and 58.0% respectively.Finally,a new recommendation is made to apply Local_HYQ92 and Babin_SHEBA07 in very stable conditions considering that M slope is more important than evaporation duct height and absolute M value in uniquely determining electromagnetic propagation effects.

Hybrid integration method for highly maneuvering radar target detection based on a Markov motion model

To detect highly maneuvering radar targets in low signal-to-noise ratio conditions, a hybrid long-time integration method is proposed, which combines Radon-Fourier Transform(RFT), Dynamic Programming(DP), and Binary Integration(BI), named RFT-DP-BI. A Markov model with unified range-velocity quantification is formulated to describe the maneuvering target’s motion. Based on this model, long-time hybrid integration is performed. Firstly, the whole integration time is divided into multiple time segments and coherent integration is performed in each segment via RFT. Secondly, non-coherent integration is performed in all segments via DP. Thirdly, 2/4 binary integration is performed to further improve the detection performance. Finally, the detection results are exported together with target range and velocity trajectories. The proposed method can perform the long-time integration of highly maneuvering targets with arbitrary forms of motion.Additionally, it has a low computational cost that is linear to the integration time. Both simulated and real radar data demonstrate that it offers good detection and estimation performances.

SURVEILLANCE OF MOVING TARGET BEHIND PARTITIONS USING A UWB-TWDR

This paper firstly analyzes the property of the low frequency electromagnetic wave, which can penetrate many types of non-metallic materials, and the ability of Ultra-Wide Band (UWB) impulse signal which has high range resolution. Then the methods are discussed for conducting surveillance through walls, detecting and locating the moving persons behind the partitions. The schematic diagram of Through-Wall Detecting Radar (TWDR) and the models of moving target are shown and the principle of detecting the moving target is also provided with coherent superimposing technique on a range gate. Finally an algorithm for estimating the location of targets is given. The performance of TWDR is validated by the experiments of penetrating a wood block, a red brick wall and a reinforced concrete wall.