Adaptive Clutter Suppression and Detection Algorithm for Radar Maneuvering Target With High-Order Motions Via Sparse Fractional Ambiguity Function

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

Low observable moving target-detection technology under clutter background is the key factor affecting the radar performance. Here, frequency diverse array (FDA) MIMO radar is investigated for low-observable moving target detection. FDA-MIMO radar not only has all the advantages of phased array and MIMO radar, but also can make a two-dimensional joint estimation of the target distance and azimuth. In order to achieve coherent integration for Doppler processing, a novel three-dimensional processing method is proposed, i.e. space-range-Doppler focus (SRDF) processing. It utilises the property of FDA and high-resolution Doppler processing of MIMO. After discussing signal model of FDA-MIMO radar, the flowchart of SRDF-based low-observable moving target detection and estimation is provided. Finally, simulation of moving target detection in clutter background verifies that proposed method has better ability for joint angle-range-Doppler processing with higher resolutions.

High-speed, low-observable, and maneuvering target detection is always the difficult problem for the early warning and detection of space target, ballistic target, and passive radar signal processing. The high-order motion information of maneuvering target is employed in this paper, such as acceleration and jerk. Accordingly, several long-time coherent integration methods are demonstrated and illustrated, i.e., Radon-fractional Fourier transform (RFRFT), Radon fractional ambiguity function (RFRAF), Radon-linear canonical ambiguity function (RLCAF), and phase differentiation-Radon-Lv's distribution (PD-RLVD). These methods can compensate the across range unit (ARU) and Doppler frequency migration (DFM) effects simultaneously during the long-time integration. The background clutter and noise can be well suppressed with higher integration gain. Experimental results using real radar data show the superiority of the proposed methods.

In a clutter-dominant target detection problem, an adaptive detector needs to either whiten or null the clutter prior to signal detection. To whiten clutter, it is required to estimate the covariance matrix of a received clutter, while a proper set or estimate of the parameters of clutter subspace model is required to null the received clutter. In this paper, we investigate the detection performance of a subspace-based detector and a sample covariance matrix(SCM)-based detector presented in [1] and [2], respectively, but for a mono-static radar system. Our simulation results show that the subspace-based detector not only can reach a predetermined probability of false alarm (fully nulling clutter) but also offers better detection performance as compared with its counterpart. In this case, the SCM-based detector only offers better detection performance for detecting targets with Doppler frequency resided in clutter region as compared with that of subspace-based one.

Joint transform correlator is one of the most successful optical pattern recognizers which is composed of Ar⁺ laser, spatial filter, Fourier transform lenses, EALCD (electrically addressed liquid crystal displays) and CCD (Charge Coupled Device). It has many applications in industry and military. The bottle technologies are how to recognize the target in clutter scene and how to increase the brightness of the correlation peak which represents the detected target. In this paper, we researched joint transform correlator (JTC) for infrared target detection, and used wavelet transform technology to increase the contrast of input image which contains target and reference objects, reduced the high frequency noise and realized target detection in clutter scene. According to our requirement, we selected the two-order spline wavelet, and made input image have discrete binary wavelet transformed. Because edge always contains much information of image, we adopted the method of modular maximum to extract the edge of different scales after wavelet transform mentioned above. In addition, combined with threshold division which could eliminate some of the clutter, we could get the remarkable edge. As experiment result, we detected the infrared image, both with computer correlation matched method and optical JTC method. The experiments show that the wavelet transform technology is one of the best data processing methods for the target detection in clutter background.

Drones and birds are typically small, slow targets that fly at a low altitude that surveillance radar systems cannot easily detect. Classic radar detection based on signal amplitude threshold detection. If the envelope of the receiver output exceeds a pre-established threshold, a signal is described as present from a target. The entire premise of amplitude detection is that RCS of an object is larger than the noise in a background, and the signal-to-noise ratio (SNR) is utilized to maintaining the detection probability. If the detection probability is larger than 50%, the SNR of the target is at least 13.1 dB for single pulse detection. This method sometimes fails to detect those weak signals at a long distance due to their small radar cross section (RCS) values and low velocities. Here, we propose a novel radar detection method based on detecting signal to clutter ratios (SCR) in the spectrum. The SCR is identified as the ratio between the spectral peak amplitude of the target to the mean amplitude of the entire spectrum. Although the spectral peak amplitude of the target is lower along the increasing range, the power of the background clutter decays as well. Therefore, SCR value of a target is independent of the detection range. This is useful for detecting targets with small RCS values, including birds, drones, vortex et.al. Theoretically, by using the SCR threshold, the detection range of a target can be as long as the range when the Doppler frequency is unambiguous. We present detection results of birds and drones by using a Ku-band pulsed Doppler radar. When we set the SCR detection threshold at 8 dB, we can detect radar echoes from birds and drones in different channels and range gates well, even at the range of 10km from the radar. We find that birds and multirotor drones have the similar SCR. When using SCR to detect a target, the crucial problem is the recognition of the target peaks, because the false alarm rate must be controlled.

HighlightsWhat are the main findings?This paper presents a high-order phase compensation and parameter estimation algorithm designed for maneuvering targets, which effectively balances detection performance and computational efficiency.The proposed method achieves precise compensation of high-order range migrations without the need for exhaustive parameter search, thereby circumventing the blind speed sidelobe effect. Futhermore, the incorporation of a time-delay variable significantly improves the anti-noise capability of the approach.What is the implication of the main finding?This algorithm introduces a novel motion compensation technique to address the challenges of range migration, Doppler frequency migration, and Doppler ambiguity in detecting weak maneuvering targets. It achieves precise range migration compensation with enhanced accuracy and robustness, forming a vital preprocessing step for high-quality SAR imaging.The algorithm supports simultaneous multi-target processing and holds promising application potential in fields such as airborne radar detection, maritime moving target indication, cooperative observation by UAV formations, and other fields.This study focuses on the key challenges in detecting and estimating motion parameters of ground maneuvering targets for airborne radar sensors. The complex unknown motion states of the ground maneuvering target, including velocity, acceleration, and jerk, result in range migrations (RMs) and Doppler frequency migrations (DFMs). These effects severely degrade the long-time coherent accumulation performance of the airborne radar, thereby limiting the reliable detection and precise parameter estimation of maneuvering targets. To address this issue, a new detection and motion parameter estimation method based on the range frequency reversal transform (RFRT) and searching Lv’s distribution (SLVD), i.e., RFRT-SLVD, is proposed. Specifically, the third-order RM (TRM) and quadratic DFM (QDFM) are considered. The proposed method operates as follows: First, RMs are eliminated simultaneously via the RFRT operation, which multiplies the echo by its reversed data in the range frequency and slow-time domains, leveraging the symmetric equal-interval sampling property of the range frequency. Subsequently, a phase compensation function (PCF) related to the jerk is constructed to compensate the QDFM. Finally, the LVD is performed to remove residual DFMs and achieve effective signal energy accumulation. Additionally, the case of a fast-moving target with Doppler ambiguity is analyzed, and a method for estimating three motion parameters is provided. A key advantage of the proposed technique is its ability to directly compensate the RMs without requiring prior knowledge of the maneuvering target, while also avoiding the blind speed sidelobe (BSSL) effect. In comparison with existing algorithms, RFRT-SLVD achieves a balanced trade-off between parameter estimation performance and computational efficiency. Numerical analyses and experiments are conducted to validate the method, assessing its detection capability for ground maneuvering targets, Doppler ambiguity resolution in parameter estimation, computational complexity, and method applicability in multi-target scenarios.

This paper presents a novel single target particle filter with spawn model and particle labeling approach, abbreviated SL-PF. The purpose of this filter is to detect instantaneously occurring target maneuvers, e.g. course changes of maritime targets, and to provide accurate tracking performance before and after the maneuvers. The key idea is to borrow the spawn model from the probability hypothesis density (PHD) filter since this model is naturally suited for these kinds of maneuvers. Secondly, each particle in the filter carries a label which is updated in a systematic manner in the spawning step so that it is possible to recognize spawned particles representing a target maneuver. This provides an integrated maneuver detection procedure within the particle filter. Monte Carlo simulations verify the SL-PF approach and indicate a significant estimation accuracy improvement compared to a conventional particle filter.

This paper addresses the coherent integration problem for detecting a maneuvering target with complex motions, involving range migration (i.e., range walk and range curvature) and Doppler frequency migration (DFM) within the coherent processing interval. A new method based on adjacent cross correlation function (ACCF) and fractional Fourier transform (FRFT) is proposed, where the ACCF operation is firstly employed to remove the range migration (RM) and reduce the order of DFM. After that, the FRFT is applied to achieve the coherent accumulation. Several simulation experiments are provided to demonstrate the effectiveness.

The target motion parameter estimation is an important index to measure the performance of radar system. Accurate estimation of acceleration parameter has important influence on improving the detection and tracking performance of high maneuvering target. When radar transmits a signal with fixed frequency, the maneuvering target echo signal can be approximated as linear frequency modulation signal. Therefore, the acceleration parameter estimation of maneuvering target is transformed into the parameter estimation of linear frequency modulation signal. In this paper, a novel method for acceleration parameter estimation of maneuvering target is proposed based on the compressed sensing theory. The linear frequency modulation signal has sparse characteristic in fractional Fourier transform domain and compressed sensing theory asserts that we can employ nonadaptive linear projections to preserve the structure of the signal and reconstruct the signal with these projections using an optimization process. Theoretical analysis and experimental results verify that the method can effectively solve the precision problem of parameter estimation caused by the large sampling rate in the traditional signal processing method, and effectively reduce the data storage and transmission pressure in radar signal processing.

With the flood of information available today the question how to deal with high dimensional data/signals, which are cumbersome to handle, to calculate with and to store, is highly important. One approach to reducing this flood is to find sparse signal representations, as a signal that is the linear combination of a few elements from a pool of building blocks, can be reduced to the few coefficients of this representation. If these building blocks form a basis, finding the sparse representation poses no problem but unfortunately not many signal classes are sparse in a basis. Taking more building blocks, i.e. a redundant dictionary, increases the chances of having sparse representations, but actually finding them becomes very hard. This led to the development of numerous strategies and algorithms for finding sparse representations, with varying complexity and success rate. The first part of the thesis deals with two of those algorithms, Thresholding and Matching Pursuit, from a more theoretical point of view. It is shown that both those greedy algorithms can be improved with a little trick, that does not increase their complexity, and that when considering their average instead of their worst case performance they perform quite well in comparison to more complex methods. The second part of thesis treats questions concerning the whole dictionary and its properties. First it gives more evidence that sparsity is useful by extending the concept of compressed sensing to signals that are sparse not in a basis but in a redundant dictionary. Thus to record a sparse signal it is not necessary to make as many measurements as the dimension of the signal but only a multiple of the number of dictionary elements used to represent it. Next we show that dictionaries cannot only provide sparse representations but that their geometric properties can also be exploited to model data structures. Here we explain how to model different subclasses of a class of signals by incoherent subspaces, present an algorithm to learn a dictionary made out of these subspaces and then use it for classification of faces. Finally we turn back to the sparse representation problem and study the fundamental question how to find a dictionary providing sparse representations. We pick up the idea to learn a dictionary via minimisation of a continuous cost function and provide conditions, guaranteeing that the decomposition of a collection of training signals into a dictionary and a coefficient matrix constitutes a local minimum. We also analyse statistically when these conditions are fulfilled with high probability.

The difficulty for tracking maneuver target is to decide whether a maneuver occurs and when the target begins to maneuver. Since target tracking is a real. time problem, the crucial factor is to detect the target maneuver as accurately and quickly as possible. A new approach for the target maneuver detection is presented, especially for the weak target maneuver detection in heavy noise. The algorithm is based on the criterion where the maneuver detection delay is made minimum, which replaces the original measurements by those of the average interpolation to reduce the delay to avoid tracker divergence, and uses wavelet transforms to increase the probability of detection. Simulation results demonstrate the superior performance of our scheme especially for tracking weak maneuvering targets in heavy noise.

Sparse gammatone signal model optimized for English speech does not match the human auditory filters

Infrared small target detection in heavy sky scene clutter based on sparse representation

The traditional infrared small target detection method is easy to cause false alarms in the complex sea and sky background. In this letter, a fast detection method for infrared small targets in the complex sea and sky background is proposed. Firstly, in order to make full use of the Gaussian characteristics of infrared small targets and effectively distinguish the target from the background clutter, the SODD (second order directional derivative) filter is used to improve the target signal-to-clutter ratio (SCR) and suppress the background clutter; Secondly, in order to improve the real-time performance of the algorithm, we improve the existing MPCM algorithm by reducing the difference dimension between the target area and the local background area; Finally, we use pipeline filtering that can take full advantage of candidate target location, size, and total grayscale 3D information to complete multi-frame target validation and achieve continuous target detection in image sequences. The experimental results show that the proposed algorithm can stably and accurately detect infrared small targets in the infrared image sequence.