Long-time Coherent Integration Method Research Articles

Long-Time Coherent Integration for Marine Targets Based on Segmented Compensation

Long-time coherent integration is an effective method for dim target detection from heavy sea clutter. To detect dim targets, a novel long-time coherent integration method based on segmented compensation is proposed in this paper. The method models the complex motion of a marine target as the combination of multi-stage uniformly accelerated motions. According to the difference of energy distribution in Doppler frequency domain, this method can suppress sea clutter and detect the regions of interest (ROIs). Using time–frequency domain energy analysis, the potential target can be extracted. After estimating the parameters and segmentation, for the potential targets, the phase compensation factor can be used to eliminate the Doppler frequency modulation caused by the complex motion. Finally, for the compensated signal, long-time coherent integration is performed to realize the target detection and discrimination under low signal-to-clutter ratio. To verify the effectiveness of the proposed method, we apply simulation data and measured CSIR data in the experiments. The results show that the proposed method can integrate the energy of target more effectively than MTD and RFrFT, and the novel method has better detection performance for complex moving targets under low signal-to-clutter ratio situation.

An Hybrid Integration Method-Based Track-before-Detect for High-Speed and High-Maneuvering Targets in Ubiquitous Radar

Due to the limited transmission gain of ubiquitous radar systems, it has become necessary to use a long-time coherent integration method for range-Doppler (RD) analysis. However, when the target exhibits high-speed and high-maneuver capabilities, it introduces challenges, such as range migration (RM), Doppler frequency migration (DFM), and velocity ambiguity (VA) in the RD domain, thus posing significant difficulties in target detection and tracking. Moreover, the presence of VA further complicates the problem. To address these complexities while maintaining integration efficiency, this study proposes a hybrid integration approach. First, methods called Keystone-transform (KT) and matched filtering processing (MFP) are proposed for compensating for range migration (RM) and velocity ambiguity (VA) in Radar Detection (RD) images. The KT approach is employed to compensate for RM, followed by the generation of matched filters with varying ambiguity numbers. Subsequently, MFP enables the production of multiple RD images covering different but contiguous Doppler frequency ranges. These RD images can be compiled into an extended RD (ERD) image that exhibits an expanded Doppler frequency range. Second, an improved particle-filter (IPF) algorithm is raised to perform incoherent integration among ERD images and to achieve track-before-detect (TBD) for a target. In the IPF, the target state vector is augmented with ambiguous numbers, which are estimated via maximum posterior probability estimation. Then, to compensate for the DFM, a line spread model (LSM) is proposed instead of the point spread model (PSM) used in traditional PF. To evaluate the efficacy of the proposed method, a radar simulator is devised, encompassing comprehensive radar signal processing. The findings demonstrate that the proposed approach achieves a harmonious equilibrium between integration efficiency and computational complexity when it comes to detecting and tracking high-speed and high-maneuvering targets with intricate maneuvers. Furthermore, the algorithm’s effectiveness is authenticated by exploiting ubiquitous radar data.

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

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.

Deep neural network-aided coherent integration method for maneuvering target detection

Generalized Radon-Fourier transform (GRFT) is a classical long-time coherent integration method for radar maneuvering target detection. GRFT, whose core is to achieve motion parameter estimation via searching, can almost reach the optimal detection performance but heavily suffers from the high computational cost. Motivated by the fact that motion parameter estimation is essentially a non-linear mapping from the radar echo to the target’s motion parameters, one can use a deep neural network (DNN), a kind of modeling tool that can learn complex mappings from training data, to directly realize this mapping, thus alleviating the heavy computational burden brought by the searching efforts. Based on this idea, a DNN-aided long-time coherent integration algorithm, which can be viewed as a fast implementation of GRFT, is proposed in this paper. More specifically, we first use a pre-trained DNN to roughly estimate the motion parameters of the target to be detected from the radar echo, and then accomplish the coherent integration of the target for detection via a fine grid search in the neighborhood of the obtained rough estimation results. Finally, simulation results demonstrate that the proposed algorithm can achieve the detection performance close to that of GRFT but with a much lower computational cost.

Long-Time Coherent Integration Algorithm for Radar Maneuvering Weak Target With Acceleration Rate

In this paper, we propose a long-time coherent integration method for a maneuvering target with the first-, second-, and third-order range migrations, and the complex Doppler frequency migration. In this method, after range compression, the echo signal is first transformed into the range-frequency and Doppler domain based on series reversion, and then an azimuth matched filtering procedure is implemented in the 2-D frequency domain. It can eliminate the coupling effects between range and azimuth jointly caused by the radial velocity, radial acceleration, and radial acceleration rate of a moving target. Due to the linear transform property, the proposed method can work well under low signal-to-clutter and noise ratio. In addition, the Doppler ambiguities, when target azimuth spectrum either is within a pulse repetition frequency (PRF) or spans over neighboring PRF bands, can be well solved. Both simulated and real synthetic aperture radar data processing results are provided to validate the effectiveness of the proposed algorithm.

Fast parameter estimation method for maneuvering target by using non-uniformly resampling reducing order technique

Abstract In radar systems, prolonging the integration time can improve the performance of motion parameter estimation for weak targets. However, when the target is maneuvering, the long integration time may lead to rang migration (RM) and Doppler frequency migration (DFM) problems. In traditional methods, multi-dimensional search of motion parameters is often used, but the computational cost is high. In this paper, a fast long-time coherent integration method for maneuvering target is proposed. By using a newly defined cross correlation function based on a non-uniform resampling technique, we eliminate the high-order RM and DFM simultaneously in the range frequency-slow time domain. Then, the target energy is integrated efficiently by performing inverse fast Fourier transform (IFFT) and fast Fourier transform (FFT) along the range and azimuth directions. The proposed method has lower computational complexity than traditional methods, because it uses FFT and IFFT instead of direct multi-dimensional search. In addition, the proposed method is applicable for multi-targets and fast moving target with ambiguities. The simulations and theoretical analysis are provided to demonstrate the effectiveness of the proposed method.

Long‐time coherent integration method for high‐speed target detection using frequency agile radar

In frequency agile radar (FAR), successive pulses each with different carrier frequencies are used as the transmission waveform. Since the range–Doppler coupling effect of a high-speed target varies significantly with the carrier frequency, large ranging errors will exist between different pulses. In this case, great distortion of the moving trace in the echo plane will be present after conventional pulse compression. The distortion will defocus the echo energy in the integration process when combining multiple pulses. In addition, for high-speed targets, the traditional narrowband matched filter will suffer from a decrease in signal-to-noise ratio, especially when pulses with large duration or bandwidths are used. A long-time coherent integration method for the high-speed target detection using FAR is proposed. The effectiveness is verified by simulation experiments.

Radon-fractional ambiguity function-based detection method of low-observable maneuvering target

Based on the quadratic frequency modulated (FM) signal of a maneuvering target, a novel long-time coherent integration method, i.e., Radon-fractional ambiguity function (RFRAF), is proposed to compensate the range and Doppler migrations simultaneously. By the long-time instantaneous autocorrelation function and rotating the time-frequency plane, the observation values of a maneuvering target can be well matched and accumulated as a peak in the RFRAF domain. Results of experiments with simulated and real data prove its effectiveness.

Radon-Linear Canonical Ambiguity Function-Based Detection and Estimation Method for Marine Target With Micromotion

Robust and effective detection of a marine target is a challenging task due to the complex sea environment and target's motion. A long-time coherent integration technique is one of the most useful methods for the improvement of radar detection ability, whereas it would easily run into the across range unit (ARU) and Doppler frequency migration (DFM) effects resulting distributed energy in the time and frequency domain. In this paper, the micro-Doppler (m-D) signature of a marine target is employed for detection and modeled as a quadratic frequency-modulated signal. Furthermore, a novel long-time coherent integration method, i.e., Radon-linear canonical ambiguity function (RLCAF), is proposed to detect and estimate the m-D signal without the ARU and DFM effects. The observation values of a micromotion target are first extracted by searching along the moving trajectory. Then these values are carried out with the long-time instantaneous autocorrelation function for reduction of the signal order, and well matched and accumulated in the RLCAF domain using extra three degrees of freedom. It can be verified that the proposed RLCAF can be regarded as a generalization of the popular ambiguity function, fractional Fourier transform, fractional ambiguity function, and Radon-linear canonical transform. Experiments with simulated and real radar data sets indicate that the RLCAF can achieve higher integration gain and detection probability of a marine target in a low signal-to-clutter ratio environment.

Detection of a Low Observable Sea-Surface Target With Micromotion via the Radon-Linear Canonical Transform

In this letter, a novel long-time coherent integration method, known as the Radon-linear canonical transform (RLCT), is proposed for detection of a low observable moving target in sea clutter. The micro-Doppler (m-D) of a sea-surface target is studied and modeled as multiple linear-frequency-modulated signals, which result from the accelerated and 3-D rotated movements. The RLCT-based algorithm employs m-D as a useful signature for target detection and can simultaneously compensate the range and Doppler migrations during long observation time, which simplifies the operational procedure. By searching along the moving trajectory and using extra three degrees of freedom, the observation values of m-D signals can be well matched and accumulated as peaks in the RLCT domain. Then, the target can be declared by comparing the peak value with an adaptive threshold. The definition of the RLCT demonstrates that it is the generalization of the popular moving target detection, Radon-Fourier transform, fractional Fourier transform, and linear canonical transform methods. Finally, experiments using a real sea clutter data set show that the proposed method can achieve high integration gain and detection probability of a micromotion target in heavy sea clutter.

Wideband‐scaled Radon‐Fourier transform for high‐speed radar target detection

High target speed, long pulse duration or wide signal bandwidth may cause noticeable scale effect on radar echoes, for which the conventional narrowband matched filter may introduce an obvious signal-to-noise ratio (SNR) loss for target detection. To obtain the ideal SNR gain in the above scenarios, a novel long-time coherent integration method has been proposed, namely wideband-scaled Radon-Fourier transform (WSRFT). The proposed WSRFT can compensate the scale effect not only on the single pulse but also among the multiple pulses in a long coherent integration time. Furthermore, the performance comparison between the proposed WSRFT and the existing Radon-Fourier transform method is given. Finally, some numerical experimental results are also provided to demonstrate the effectiveness of the proposed WSRFT method.

Radon-Fourier Transform for Radar Target Detection (II): Blind Speed Sidelobe Suppression

This paper gives a detailed performance analysis for the novel radar long-time coherent integration method, i.e., Radon-Fourier transforms (RFT). Some important properties of RFT, e.g., two-dimensional (2D) impulse response, 2D translational invariance, multitarget linear additivity, linear signal-to-noise ratio gain in additive white Gaussian noise (AWGN), as well as the 2D correlation function of transformed AWGN, are derived for continuous and discrete RFT, respectively. However, because of discrete pulse sampling, finite range resolution, and limited integration time, the "blind-speed sidelobes (BSSL)" of discrete RFT may inevitably appear in real applications. Although the BSSL are reduced with the increase of the blind-speed integer, they may still lead to false alarms or loss detections in a real multitarget scenario. Based on the analytic expression derived for BSSL, the causes of BSSL are analyzed and the effective BSSL suppression methods are proposed. Finally, numerical experiments are also provided to demonstrate the effectiveness of the proposed methods.