Target Detection Probability Research Articles

TWPT: Through-Wall Position Detection and Tracking System Using IR-UWB Radar Utilizing Kalman Filter-Based Clutter Reduction and CLEAN Algorithm

Against the backdrop of rapidly advancing Artificial Intelligence of Things (AIOT) and sensing technologies, there is a growing demand for indoor location-based services (LBSs). This paper proposes a through-the-wall localization and tracking (TWPT) system based on an improved ultra-wideband (IR-UWB) radar to achieve more accurate localization of indoor moving targets. The TWPT system overcomes the limitations of traditional localization methods, such as multipath effects and environmental interference, using the high penetration and high accuracy of IR-UWB radar based on multi-sensor fusion technology. In the study, an improved Kalman filter (KF) algorithm is used for clutter reduction, while the CLEAN algorithm, combined with a compensation mechanism, is utilized to increase the target detection probability. Finally, a three-point localization estimation algorithm based on multi-IR-UWB radar is applied for the precise position and trajectory estimation of the target. Experimental validation indicates the TWPT system achieves a high positioning accuracy of 96.91%, with a root mean square error (RMSE) of 0.082 m and a Maximum Position Error (MPE) of 0.259 m. This study provides a highly accurate and precise solution for indoor TWPT based on IR-UWB radar.

Methodology for assessing the effectiveness of the impact intermittent noise interference to a radar station with adaptive detection threshold formation

Problem statement. For aviation suppression equipment, it is characteristic to separate the modes of interference and reconnaissance radiation in time due to the problem of their electromagnetic compatibility. It is also possible to alternately suppress various objects with a limited bandwidth of the interference station. This leads to periodic interruptions of the noise interference and, consequently, a decrease in its spectral density. A feature of radar stations of the latest and modernized means of intercepting air defense systems aimed at hitting targets in a complex interference environment is adaptive control of parameters and modes of operation of various subsystems. In particular, modern radar stations provide for the adaptive formation of a detection threshold based on estimates of the statistical characteristics of such interference. Such estimates are formed over the interference integration interval, the size of which is generally selected from the condition of insignificant influence of signals reflected from the targets on them. Moreover, for the most correct consideration of the interference situation in the vicinity of the detected target, the strobe pulses of the false alarm probability stabilization circuit, forming the integration interval, are placed on both sides of the analyzed range resolution element. Goal. To evaluate the effect of intermittent noise interference parameters on the operation of radar stations with adaptive detection threshold foration. Assumptions. When developing the methodology, the following typical assumptions were made: in target detection mode, the radar station emits pulse signals with a low or medium pulse repetition rate; the signal reflected from the target, noise interference and internal noise are independent normal random processes; interference and internal noise have a uniform spectral density within the bandwidth of the radar receiver; the receiving path of the radar station has a traditional structure, which includes: an intermediate frequency amplifier, a linear amplitude detector, an integrator and a threshold device. Results. For the first time, the obtained technique takes into account the influence of time and energy parameters of intermittent noise interference on the probability of correct target detection by a radar station with adaptive detection threshold formation. Reducing the duration of the interference pulse in the period of intermittent noise interference leads to a nonlinear increase in the probability of correct target detection. To maximize this indicator, it is necessary to ensure a sufficiently high accuracy in estimating the statistical characteristics of the power of intermittent noise interference. The reliability of the results obtained using the developed methodology is confirmed by the fact that the effectiveness of intermittent noise interference, while excluding pauses in its radiation, is reduced to the effectiveness of continuous noise interference. Practical significance. The developed technique makes it possible to clarify the technical requirements for radar stations that implement flexible control of the target detection mode under the influence of intermittent noise interference of unknown intensity.

Interrupted-Sampling Repeater Jamming Countermeasure Based on Intrapulse Frequency–Coded Joint Frequency Modulation Slope Agile Waveform

Interrupted-sampling repeater jamming (ISRJ) is widely used in the field of electronic countermeasures, and can severely affect radar detection. Therefore, the problem of ISRJ suppression is a compelling task. In this paper, we propose an ISRJ suppression method based on an intrapulse frequency-coded joint frequency modulation (FM) slope agile waveform. The intrapulse frequency-coded joint FM slope agile waveform is first designed. The delay inserted between subpulses makes the waveform easy to implement in engineering, and the ambiguity function diagram of the waveform approximates the ideal thumbtack type. Next, the echo slices are classified in the fractional domain utilizing the discontinuity of ISRJ and the focusing property of fractional Fourier transform for chirp signals. Then, the target and interference in the interfered echo slices are reconstructed by compressed sensing, and a time-domain filter is constructed based on interference-free echo slices. Finally, the echo signal after interference suppression is further filtered in the time domain to degrade range sidelobes. Simulation results show that the proposed method can effectively suppress three typical types of ISRJ. Moreover, the probability of target detection after interference suppression exceeds 90% when the jamming-to-signal ratio equals 50 dB.

Power Allocation Scheme for Multi-Static Radar to Stably Track Self-Defense Jammers

Due to suppression jamming by jammers, the signal-to-interference-plus-noise ratio (SINR) during tracking tasks is significantly reduced, thereby decreasing the target detection probability of radar systems. This may result in the interruption of the target track. To address this issue, we propose a multi-static radar power allocation algorithm that enhances the detection and tracking performance of multiple radars in relation to their targets by optimizing power resource allocation. Initially, the echo signal model and measurement model of multi-static radar are formulated, followed by the derivation of the Bayesian Cramér–Rao lower bound (BCRLB). The multi-objective optimization method is utilized to establish the objective function for joint tracking and detection, with dynamic adjustment of the weight coefficient to balance the tracking and detection performance of multiple radars. This ensures the reliability and anti-jamming capability of the multi-static radar system. Simulation results indicate that the proposed algorithm can prevent the interruption of jammer tracking and maintain robust tracking performance.

Dynamic Programming-Based Track-before-Detect Algorithm for Weak Maneuvering Targets in Range–Doppler Plane

This paper focuses on detecting and tracking maneuvering weak targets in the range–Doppler (RD) plane with the track-before-detect (TBD) algorithm based on dynamic programming (DP). Traditional DP-TBD algorithms integrate target detection and tracking in their framework while searching the paths provided by a predefined model of the kinematic properties within the constraints allowed. However, both the approximate motion model used in the RD plane and the model mismatch caused when the target undergoes a maneuver can degrade the TBD performance sharply. To address these issues, this paper accurately describes the evolution of the RD equation based on Constant Acceleration (CA) and Coordinated Turn (CT) motion models with the process noise in the Cartesian coordinate system, and it also employs a recursive method to estimate the parameters in the equations for efficient energy accumulation and path searches. Facing the situation that targets energy accumulation during the DP iteration process will lead to an expansion of the target energy accumulation process. This paper designs a more efficient Optimization Function (OF) to inhibit the expansion effect, improve the resolution of the nearby targets, and increase the detection probability of the weak targets simultaneously. In addition, to search the trajectory more efficiently and accurately, we improved the process of DP multi-frame accumulation, thus reducing the computation amount of large-scale searches. Finally, the effectiveness of the proposed method for CA and CT motion target detection and tracking is verified by many of the simulation experiments that were conducted in this paper.

Mixture texture model with weighted generalized inverse Gaussian distribution for target detection

With the improvement of radar resolution, the amplitude distribution of sea clutter has begun to exhibit significant heavy-tailed characteristics. Existing models for sea clutter amplitude distribution do not sufficiently solve this problem, which results in a diminished target detection probability in two-step generalized likelihood ratio test (GLRT) based target detectors. To address this issue and to enhance the radar target detection capabilities, we propose a new compound-Gaussian clutter model based on a weighted mixture of generalized inverse Gaussian distributions to model the texture. The CG-WGIG model demonstrates greater flexibility and effectively captures the characteristics of sea clutter amplitude distributions with heavy tails, thus affording a better fit. We combine this model with the two-step GLRT criterion to propose a GLRT detector endowed with a weighted generalized inverse Gaussian texture (GLRT-WGIG), specifically tailored for detecting range-spread targets (RSTs). We have conducted a rigorous mathematical proof to demonstrate that the constant false alarm rate (CFAR) characteristic of the proposed GLRT-WGIG detector is independent of the covariance matrix, thereby ensuring its robustness in various operational scenarios. Using measured data, we demonstrate that the proposed model and detector are flexible and outperform the existing methods in terms of fitting and detection performance, where we obtain a mean 12.02% MSE of fitting performance promotion.

Reduction of lidar ranging error in turbulent water based on WT-ICA method

A new signal-processing method for the underwater lidar system based on wavelet transform (WT) and independent component analysis (ICA) is presented in this paper. Turbulence in water causes stronger scattering in underwater lidar returns, which can reduce the ranging accuracy and spatial resolution of the underwater lidar system. The method we proposed combines wavelet decomposition and independent component analysis (ICA), the data sets from wavelet decomposition are used to form the input matrix of ICA. The new method benefits from adaptive filtering of WT and blind source separation of ICA, therefore, the scattering clutters are further reduced. The WT-ICA signal processing algorithm is tested with experimental data. A mirror and a black Polyvinyl chloride (PVC) plate were used as the targets, respectively. Without using the WT-ICA, the ranging error with the mirror was 10.0 cm at 3 m distance when the attenuation coefficient of the water was 2.0 m-1. A pump was used to generate turbulence in the water tank. When the flow rate of the pump was 200 L/min, after using WT-ICA, the ranging error was reduced from 14.0 cm to 4.0 cm. For the PVC plate, the ranging errors were 4.5 cm and 19.0 cm at 6.0 attenuation length with and without the algorithm respectively. In both cases, applying this algorithm can significantly improve the ranging accuracy. When the algorithm is applied, the ranging error and detection probability of target echoes with and without turbulence were similar. These results indicated that the WT-ICA method had the ability to reduce the influence of turbulence.

A coherent integration method of an active sonar for maneuvering turning target detection.

The detection probability of underwater weak targets using active sonar is low, and inter-pulse coherent integration can improve the signal-to-noise ratio of echoes. When a target executes a maneuvering turn, complex range and Doppler frequency migrations occur during the coherent integration time that decrease the coherent integration gain. Most existing integration methods simplify the target motion to a finite-order polynomial model but fail to integrate a maneuvering turning target (MTT) due to model mismatch. Hence, this study proposes an underwater MTT integration method based on the modified Radon-Fourier transform. The proposed method constructs a theoretically accurate motion model for the MTT and a phase compensation function to compensate for the Doppler frequency migration. Furthermore, it yields a well-focused integration peak in the range-velocity and offset angle-turn rate dimensions and accurately estimates the target motion parameters. Moreover, the proposed method is suitable for targets with radial and oblique uniform motions. The effectiveness of the proposed method is demonstrated through simulations and a lake test. The proposed method demonstrates good integration performance, with an integration gain approximately 4-7 dB higher than that of traditional methods when using 30 integration pulses.

ITS Efficiency Analysis for Multi-Target Tracking in a Clutter Environment

The Integrated Track Splitting (ITS) is a multi-scan algorithm for target tracking in a cluttered environment. The ITS filter models each track as a set of mutually exclusive components, usually in the form of a Gaussian Mixture. The purpose of this research is to determine the limits of the ‘endurance’ of target tracking of the known ITS algorithm by analyzing the impact of target detection probability. The state estimate and the a-posteriori probability of component existence are computed recursively from the target existence probability, which may be used as a track quality measure for false track discrimination (FTD). The target existence probability is also calculated and used for track maintenance and track output. This article investigates the limits of the effectiveness of ITS multi-target tracking using the method of theoretical determination of the dependence of the measurements likelihood ratio on reliable detection and then practical experimental testing. Numerical simulations of the practical application of the proposed model were performed in various probabilities of target detection and dense clutter environments. Additionally, the effectiveness of the proposed algorithm in combination with filters for various types of maneuvers using Interacting Multiple Model ITS (IMMITS) algorithms was comparatively analyzed. The extensive numerical simulation (which assumes both straight and maneuvering targets) has shown which target tracking limits can be performed within different target detection probabilities and clutter densities. The simulations confirmed the derived theoretical limits of the tracking efficiency of the ITS algorithm up to a detection probability of 0.6, and compared to the IMMITS algorithm up to 0.4 in the case of target maneuvers and dense clutter environments.

Extended target trajectory Poisson multi-Bernoulli mixture filters with unknown detection probability

In most multi-extended target tracking scenarios, the target detection probability is usually unknown and time-varying, which leads to biased estimation of the state and cardinality of extended targets in online filtering. In addressing the challenge, this paper presents the unknown detection probability extended target trajectory Poisson multi-Bernoulli mixture (U-TPMBM) filter. Compared to the existing extended target Poisson multi-Bernoulli Mixture (PMBM) filter, the U-TPMBM is firstly based on sets of trajectories, which allows for direct output of target trajectory and can lead to improved trajectory estimation performance. Besides, the U-TPMBM filter integrates the unknown detection probability with the target trajectory state and thus obtains the augmented state space. By recursively estimating the augmented states via multi-target filtering approaches, it successfully realizes online and joint estimates of the unknown detection probability and the target trajectory. Finally, the U-TPMBM filter is implemented by the Beta-Gamma Gaussian Inverse Wishart (BGGIW) mixture method, especially the BGGIW-TPMBM filter. The Beta distribution is utilized to propagate densities of the unknown detection probability and the GGIW distribution to propagate densities of the target trajectory. Based on the BGGIW distribution, the trackers's recursive and closed solutions are derived in detail. The simulation experiments demonstrate that the BGGIW-TPMBM proposed in this paper can achieve robust tracking performance, even when dealing with unknown detection probabilities.

Time Allocation Approaches for a Perceptive Mobile Network Using Integration of Sensing and Communication

One of the main challenges of popularizing the integration of sensing and communication (ISAC) network is mutual interference between the two functions. A viable solution is the time division scheme where communication and sensing are separated in time domain. This paper considers a multi-cluster ISAC network model, where the time-domain radio resources are allocated to sensing and communication. At the same time, the time resources can be reused in space-domain. Particularly, the terminals in different work phases can access radio resources of different or the same clusters simultaneously, depending on interference. In this way, the interference is isolated while the resources utilization is improved. Two different resource allocation approaches are proposed according to how interference is considered. The aim is to maximize the sensing detection probability under the constraint of network throughput. The performance improvement in terms of target detection probability brought by the proposed schemes is shown by numerical results compared with benchmark methods.

Generalised covariance intersection‐Gamma Gaussian Inverse Wishart‐Poisson multi‐Bernoulli Mixture: An intelligent multiple extended target tracking scheme for mobile aquaculture sensor networks

AbstractPoisson multi‐Bernoulli Mixture (PMBM) filter has been known as an available or practical point and multiple extended target tracking (METT) method. The authors present an improved PMBM filter with adaptive detection probability and adaptive newborn distributions, accompanying with an associated distributed fusion strategy for the tracking extended multiple targets. First, the augmented state of unknown and changing target detection probability is assumed as Gamma (GAM) distribution. Second, extended states are described by Inverse Wishart (IW) distribution based on this augmented state, accompanying with dynamic states presented by Gaussian distribution. And then, an adaptive newborn distribution is adopted to describe the newborn targets appearing arbitrarily. Consequently, the closed‐form solutions of the proposed filter can be derived by approximating the intensity of newborn and potential targets to the Gamma Gaussian Inverse Wishart (GGIW) form. Moreover, the fused means that Generalised Covariance Intersection (GCI) is performed in such a large‐scale aquaculture sensor network. Experiments are presented to verify the availability of the GCI‐GGIW‐PMBM method, and comparisons with other METT filters also demonstrate that tracking behaviours are improved largely.

Target Detection in Single-Photon Lidar Using CNN Based on Point Cloud Method

To enhance the detection capability of weak targets and reduce the dependence of single-photon lidar target detection on the number of the time-correlated single-photon counting detection cycles, a convolutional neural network (CNN) based on the point cloud (CNN-PC) method is proposed in this paper for detecting targets in single-photon lidar. This approach utilizes the exceptional feature extraction capabilities offered by CNN. The CNN-PC method utilizes the feature extraction module of the trained CNN to simultaneously extract features from two-dimensional point cloud slices. Subsequently, it combines these features and feeds them into the classification module of the trained CNN for final target detection. By training the CNN using point cloud slices generated with a minimal number of detection cycles and employing a parallel structure to extract features from multiple point cloud slices, the CNN-PC method exhibits remarkable flexibility in adapting to varying numbers of detection cycles. Both simulation and experimental results demonstrate that the CNN-PC method outperforms the classical constant false alarm rate method in terms of the target detection probability at the same signal-to-noise ratio and in terms of the imaging rate and error rate at the same number of detection cycles.

FIRING EFFICIENCY OF ANTI-AIRCRAFT MISSILE COMPLEX “STRILA-10” IN TARGET DETECTION THROUGH THE OPTICAL VISOR AND AUTOMATED TARGET POINTER

When preparing for and conducting combat operations of the “SA-13 Gopher” anti-aircraft missile system, commanders and combat personnel of the system must know the missile firing efficiency indicators [1–4]. As an indicator of missile firing efficiency, the probability of hitting a target with one missile is usually used. Massive homing is carried out via the contrast channel or the infrared channel of the missile homing head. Under various conditions of use, it is necessary to evaluate the detection ranges that are realised and the probability of timely target detection, launch range and missile launch probability. It is also necessary to imagine the possible values of the inclined distances to the far edge of the kill zone of the complex. The probability of hitting a target with a single missile is affected by the geometric dimensions of the targets, their colour and contrast in the infrared wavelength range, the presence of target designation, meteorological range of visibility etc.
 The task of determining the firing efficiency of the “SA-13 Gopher” anti-aircraft missile system when detecting targets through the optical sight and automated targeting is therefore relevant and important.
 Research presented in the published [1–4; 12–13] do not allow determining the value of the probability of hitting different air targets in various conditions of the complex’s use. This literature provides only general approaches to solving this problem for typical conditions of use of an anti-aircraft missile system. A numerical modeling technique for determining the firing efficiency indicator in the form of values of the probability of hitting targets by a missile has been developed. The modeling took into account the speed of the targets, their plane in the shooting picture, their radiating capabilities and meteorological visibility range, the quality of the combat work of the operators of the combat vehicle, the sun exposure and the closing angles of the combat vehicle. Found possible detection ranges and detection probabilities of a typical target and a small unmanned aerial vehicle (UAV). A high probability of the operator of a combat vehicle missing a typical target at low and medium flight altitudes was noted. Considered launch ranges of missiles that are implemented and the probability of launching a missile when targets are captured in the contrast and infrared channels of the homing head. The largest inclined ranges to the far boundary of the zone of damage of the complex were calculated. The values of the probability of hitting a typical target and UAV in various firing conditions are determined. Sufficient firing efficiency has been proven when capturing and launching a missile in the contrast guidance channel and firing at a typical target. A comparison of the contrast and infrared channels of the homing missile head when firing at UAVs was carried out. Analytical expressions for the calculation of missile firing efficiency indicators and corresponding graphic material are presented.

Age differentially mediates the relationship between plasma biomarkers and cognition detected by digital screening

AbstractBackgroundPlasma biomarkers are emerging as a useful tool to measure neurodegeneration, particularly in patients with Alzheimer’s Disease (AD). Digital cognitive testing is also increasingly used to detect cognitive changes in AD. Age is a major determinant of performance at cognitive testing and can affect the levels of different plasma biomarkers. However, little is known about how age mediates the relationship between plasma biomarkers and cognition.Method38 AD and 48 age‐matched controls were recruited from the Oxford Centre for Cognitive Disorders. We used a fully remote digital visual short‐term memory (STM) task (Figure 1) and extracted eight cognitive metrics (Figure 1): Identification Accuracy (percentage of correctly identified items), Absolute Error (distance from the response location to the original item), Target detection (probability of correctly identifying the target), Misbinding (erroneously localizing an item to the remembered location of another item in memory), Guessing (random guessing), Identification and Localization Time (time to identify and drag the correct object to its remembered location), and memory Imprecision (width of the distribution around the target). Plasma p‐tau 181, GFAP, NFL, Aß40 and Aß42 were measured, and the Aß42/40 ratio was calculated. In Model 1 (Figure 2) the correlation between digital cognitive metrics and plasma biomarkers is shown, while in Model 2 age was regressed out. A mediation analysis was then performed to look at the effect of age as a mediator.ResultIn Model 1, most cognitive metrics on the STM task were strongly associated with p‐tau 181, GFAP and NFL. Identification Accuracy was also negatively correlated with the Aß42/40 ratio. No association with Aß42 or Aß40 was found. However, regressing out age had a profound impact on these correlations, particularly for GFAP and NFL, while p‐tau 181 was not significantly affected (Figure 2 and 3). The mediation analysis (Figure 3), shown for Identification Accuracy, confirmed that age had a more profound impact on GFAP and NFL compared to p‐tau 181.ConclusionThese new digital metrics show a strong correlation with plasma biomarkers. Finally, age is an important mediator between plasma biomarkers and cognition, but that varies across different biomarkers.

Age differentially mediates the relationship between plasma biomarkers and cognition detected by digital screening

AbstractBackgroundPlasma biomarkers are emerging as a useful tool to measure neurodegeneration, particularly in patients with Alzheimer’s Disease (AD). Digital cognitive testing is also increasingly used to detect cognitive changes in AD. Age is a major determinant of performance at cognitive testing and can affect the levels of different plasma biomarkers. However, little is known about how age mediates the relationship between plasma biomarkers and cognition.Methods38 AD and 48 age‐matched controls were recruited from the Oxford Centre for Cognitive Disorders. We used a fully remote digital visual short‐term memory (STM) task (Figure 1) and extracted eight cognitive metrics (Figure 1): Identification Accuracy (percentage of correctly identified items), Absolute Error (distance from the response location to the original item), Target detection (probability of correctly identifying the target), Misbinding (erroneously localizing an item to the remembered location of another item in memory), Guessing (random guessing), Identification and Localization Time (time to identify and drag the correct object to its remembered location), and memory Imprecision (width of the distribution around the target). Plasma p‐tau 181, GFAP, NFL, Aβ40 and Aβ42 were measured, and the Aβ42/40 ratio was calculated. In Model 1 (Figure 2) the correlation between digital cognitive metrics and plasma biomarkers is shown, while in Model 2 age was regressed out. A mediation analysis was then performed to look at the effect of age as a mediator.ResultsIn Model 1, most cognitive metrics on the STM task were strongly associated with p‐tau 181, GFAP and NFL. Identification Accuracy was also negatively correlated with the Aβ42/40 ratio. No association with Aβ42 or Aβ40 was found. However, regressing out age had a profound impact on these correlations, particularly for GFAP and NFL, while p‐tau 181 was not significantly affected (Figure 2 and 3). The mediation analysis (Figure 3), shown for Identification Accuracy, confirmed that age had a more profound impact on GFAP and NFL compared to p‐tau 181.ConclusionsThese new digital metrics show a strong correlation with plasma biomarkers. Finally, age is an important mediator between plasma biomarkers and cognition, but that varies across different biomarkers.

Time-Domain Measurement Data Accumulation for Slow Moving Point Target Detection in Heavily Cluttered Environments Using CNN

In modern radars, the target detection probability is increased by lowering the detection threshold via signal processing to detect a point target with a small radar cross-section value. However, a lower threshold increases the number of false targets. In the conventional tracking method, which uses a general tracking filter, the measurement data between scans should be compared. Therefore, for a large amount of acquired measurement data, the computational complexity can be reduced by accumulating the acquired measurement data over time, recognizing the target movement as a pattern, and training a convolutional neural network (CNN) model. Here, we propose a method to create a desired target scenario by transfer learning and estimate the target position using the activation map of a binary detector CNN model. The model can detect a target using the actual acquired radar data, and the processing time remains constant, regardless of the number of false alarms.

Target Detection Method Based on Adaptive Step-Size SAMP Combining Off-Grid Correction for Coherent Frequency-Agile Radar

Coherent frequency-agile radar (FAR) has a low probability of intercept (LPI) and excellent performance of electronic counter-countermeasures (ECCM) and electromagnetic compatibility, which can improve radar cooperation and survivability in complex electromagnetic environments. However, due to the nonlinearity of radar carrier frequency and the limitation of the Doppler tolerance of high-resolution range cells, the undesirable blind-speed sidelobes are generated in the two-dimensional (2D) range–velocity plane after coherent integration (CI) using the traditional methods based on a matching filter, which may degrade the target detection performance. To solve this problem, an adaptive step-size sparsity adaptive matching pursuit (SAMP) algorithm combining off-grid correction (ASSAMP-OC) is proposed in this paper, which seeks to achieve a better trade-off between recovery efficiency and detection performance. Firstly, an adaptive iteration step size based on the Spearman correlation coefficients (SCCS) is devised, which solves the problem of the traditional SAMP algorithm being insensitive to the change in iteration step size when the residuals vary slightly, and improves the recovery speed. Secondly, the off-grid correction method by combining a regularized stagewise backtracking idea and gradient descent optimization (GDO) is adopted to improve the recovery accuracy and suppress the blind-speed sidelobe energy (BSSE), which helps to reduce CI gain loss and improve the target detection performance without the prior information of the sparsity lever. Finally, simulation and experimental results demonstrate the effectiveness and efficiency of the proposed method in terms of target detection probability, target signal energy ratio after recovery, and computational cost, compared to several existing methods.

Echo Preprocessing-Based Smeared Spectrum Interference Suppression

Self-protection deceptive interferences (SPDI) are widely used in electronic countermeasures. Smeared spectrum (SMSP) interference, as a typical SPDI, can form a large number of dense false targets at the receiver output to affect effective target detection. Therefore, the suppression of SMSP interference is a compelling issue. The existing SMSP interference suppression methods inevitably result in energy loss of the target due to signal processing. This paper proposes a novel interference suppression method based on echo preprocessing to address this problem. Firstly, the pulse compression (PC) and the coherent integration (CI) characteristics of SMSP interference in the pulse Doppler radar are obtained through the derivation of formulas. Then, echo preprocessing is introduced, and the steps of interference suppression are listed in detail. Finally, the SMSP interference is suppressed because the preprocessed interference forms a center-shifting and range-scaling in the distance dimension after PC, and CI gain cannot be further obtained. The proposed method does not lose the energy of the true target because it does not involve filtering and reconstruction processing. Simulations show that the target detection probability of the proposed method can reach 100% via peak search after the interference suppression when the signal-to-noise ratio is greater than −10 dB and the jamming-to-signal ratio (JSR) is less than 35 dB. Compared with three representative methods in the recent literature, the proposed method has better robustness and higher JSR tolerance.

Research on an Intra-Pulse Orthogonal Waveform and Methods Resisting Interrupted-Sampling Repeater Jamming within the Same Frequency Band

Interrupted-sampling repeater jamming (ISRJ) is a kind of intra-pulse coherent deception jamming that can generate false target peaks in the range profile and interfere with the detection and tracking of real targets. In this paper, an anti-ISRJ method based on the intra-pulse orthogonal waveform is proposed, which can recognize common interference signals by comparing sub-signal matched filtering results. For some special scenes where real targets cannot be directly differentiated from false targets, a new recognition method based on the energy discontinuity of the interference signal in the time domain is proposed in this paper. The method proposed in this paper can recognize real and false targets in all ISRJ modes without any prior information, such as jammer parameters, with a small amount of calculation, which is suitable for actual radar systems. Simulation experiments using different interference parameters show that although this method has a 3 dB loss of pulse compression gain, it can completely suppress different kinds of ISRJ interference when the SNR before pulse compression is higher than −20 dB, with 100% target detection probability.