A real-time detection and positioning method for small and weak targets using a 1D morphology-based approach in 2D images

A small and weak target detection method is proposed in this work that outperforms all other methods in terms of real-time capability.It is the first time that two-dimensional(2D)images are processed using only one-dimensional1D structuring elements in a morphology-based approach,enabling the real-time hardware implementation of the whole image processing method.A parallel image readout and processing structure is introduced to achieve an ultra-low latency time on the order of nanoseconds,and a hyper-frame resolution in the time domain can be achieved by combining the row-by-row structure and the electrical rolling shutter technique.Experimental results suggest that the expected target can be successfully detected under various interferences with an accuracy of 0.1 pixels(1σ)under the worst sky night test condition and that a centroiding precision of better than 0.03 pixels(1σ)can be reached for static tests.The real-time detection method with high robustness and accuracy is attractive for application to all types of real-time small target detection systems,such as medical imaging,infrared surveillance,and target measurement and tracking,where an ultra-high processing speed is required.

Detection of weak target for MIMO radar based on Hough transform

An effective method of multiple input multiple output （MIMO） radar weak target detection is proposed based on the Hough transform. The detection time duration is divided into multiple coherent processing intervals （CPIs）. Within each CPI, conventional methods such as fast Fourier transform （FFT） is exploit to coherent inte- grating in same range cell. Furthermore, noncoherent integration through several range cells can be implemented by Hough transform among all CPIs. Thus, higher integration gain can be obtained. Simulation results are also given to demonstrate that the detection performance of weak moving target can be dramatically improved.

Fractal-based weak target detection within sea clutter

The target on the sea surface is complex and difficult to detect due to the interference of backscattered returns from the sea surface illuminated by the radar pulse. Detrended fluctuation analysis （DFA） has been used successfully to extract the time-domain Hurst exponent of sea-clutter series. Since the frequency of the sea clutter mainly concentrates around Doppler center so that we consider to extract frequency-do- main fractal characterization and then detect a weak target within sea clutter by using the difference of frequency-domain fractal characterization. The generalized detrended fluctuation analysis （GDFA） is more flexible than traditional DFA owing to its smoothing action for the clutters. In this paper, we apply the GDFA to evaluate the generalized Hurst exponent of sea-clutter series in the frequency domain. The difference of generalized Hurst exponents between different sea-clutter range bins would be used to determine whether the target exists. Moreover, some simulations with the real IPIX radar data have also been demonstrated in order to suooort this conclusion.

M-FCN based sea-surface weak target detection

This paper focuses on the sea-surface weak target detection based on memory-fully convolutional network(M-FCN)in strong sea clutter.Firstly,the constant false alarm rate(CFAR)detection method utilizes a low threshold with high probability of false alarm to detect sea-surface weak targets after non-coherent integration.Reducing the detection threshold can generate a large number of false alarms while increasing the detection rate,and how to suppress a large number of false alarms is the key to improve the performance of weak target detection.Then,the detection result of the low threshold is operated to construct the target matrix suitable for the size of fully convolutional networks and the convolution operator form.Finally,the M-FCN architecture is designed to learn the different accumulation characteristics of the target and the sea clutter between different frames.For improving the detection performance,the historical multi-frame information is memorized by the network,and the end-to-end structure is established to detect sea-surface weak target automatically.Experimental results on measured data demonstrate that the M-FCN method outperforms the traditional track before detection(TBD)method and reduces false alarm tracks by 35.1%,which greatly improves the track quality.

Multiframe track-before-detect method based on velocity filtering in mixed coordinates

In this paper,a velocity filtering based track-before-detect algorithm in mixed coordinates is presented to address the problem of integration loss caused by inaccurate motion model in polar coordinate sensors.Since the motion of a con-stant velocity(CV)target is better modeled in Cartesian coordi-nates,the search of measurements for integration in polar sensor coordinates is carried out according to the CV model in Cartesian coordinates instead of an approximate model in polar sensor coordinates.The position of each cell is converted into Cartesian coordinates and predicted according to an assumed velocity.Then,the predicted Cartesian position is converted back to polar sensor coordinates for multiframe accumulation.The use of the correct model improves integration effectiveness and consequently improves algorithm performance.To handle the weak target with unknown velocity,a velocity filter bank in mixed coordinates is presented.The influence of velocity mis-match on the performance of filter bank is analyzed,and an effi-cient strategy for filter bank design is proposed.Numerical re-sults are presented to demonstrate the effectiveness of the pro-posed algorithm.

Track-before-detect for bistatic radar based on pseudo-spectrum

Track-Before-Detect(TBD) is an efficient method to detect dim targets for radars. Conventional TBD usually follows an approximate motion model of the target, which may cause an inaccurate integration of the target energy. A TBD technique on basis of pseudo-spectrum in mixed coordinates adopting an accurate motion model for bistatic radar system is developed in this paper.The predicted position in bistatic polar plane is derived according to a nonlinear function that exactly describes the constant Cartesian velocity motion. Then around the predicted position, a pseudo-spectrum is formulated and its samples are accumulated to the integration frame for energy integration. The evolution of the state and the procedure of accumulation of the target energy are derived elaborately. The superior performance of the proposed method is demonstrated by some simulations.

Multiframe weak target track-before-detect based on pseudo-spectrum in mixed coordinates

Traditional multiframe Track-Before-Detect(TBD)may incur adverse integration loss resulting from model mismatch in sensor coordinates.Its suboptimal integration strategy may cause target envelope degradation.To address these issues,a pseudo-spectrum-based multiframe TBD in mixed coordinates is proposed firstly.The data search for energy integration is conducted based on an accurate model in the x-y plane while target energy is integrated based on pseudo-spectrum in sensor coordinates.The algorithm performance is improved since the model mismatch is eliminated,and the pseudo-spectrum based integration facilitates well maintained target envelope.The detailed multiframe integration procedure and theoretical target integrated envelope are derived.Secondly,to cope with the unknown target velocity,a velocity filter bank based on pseudo-spectrum in mixed coordinates is proposed.The effect of velocity mismatch on algorithm performance is analyzed and an efficient method for filter bank design is presented.Thirdly,a parameter estimation method using characteristics of integrated envelope is presented for improved target polar position and Cartesian velocity estimation.Finally,numerical results are provided to demonstrate the effectiveness of the proposed method.