Implementation of large points pulse compression using multicore DSP

The deep learning object detection algorithm has been widely applied in the field of synthetic aperture radar (SAR). By utilizing deep convolutional neural networks (CNNs) and other techniques, these algorithms can effectively identify and locate targets in SAR images, thereby improving the accuracy and efficiency of detection. In recent years, achieving real-time monitoring of regions has become a pressing need, leading to the direct completion of real-time SAR image target detection on airborne or satellite-borne real-time processing platforms. However, current GPU-based real-time processing platforms struggle to meet the power consumption requirements of airborne or satellite applications. To address this issue, a low-power, low-latency deep learning SAR object detection algorithm accelerator was designed in this study to enable real-time target detection on airborne and satellite SAR platforms. This accelerator proposes a Process Engine (PE) suitable for multidimensional convolution parallel computing, making full use of Field-Programmable Gate Array (FPGA) computing resources to reduce convolution computing time. Furthermore, a unique memory arrangement design based on this PE aims to enhance memory read/write efficiency while applying dataflow patterns suitable for FPGA computing to the accelerator to reduce computation latency. Our experimental results demonstrate that deploying the SAR object detection algorithm based on Yolov5s on this accelerator design, mounted on a Virtex 7 690t chip, consumes only 7 watts of dynamic power, achieving the capability to detect 52.19 512 × 512-sized SAR images per second.

Hyperspectral image contains various wavelength channels and the corresponding imagery processing requires a computation platform with high performance. Target and anomaly detection on hyperspectral image has been concerned because of its practicality in many real-time detection fields while wider applicability is limited by the computing condition and low processing speed. The field programmable gate arrays (FPGAs) offer the possibility of on-board hyperspectral data processing with high speed, low-power consumption, reconfigurability and radiation tolerance. In this paper, we develop a novel FPGA-based technique for efficient real-time target detection algorithm in hyperspectral images. The collaborative representation is an efficient target detection (CRD) algorithm in hyperspectral imagery, which is directly based on the concept that the target pixels can be approximately represented by its spectral signatures, while the other cannot. To achieve high processing speed on FPGAs platform, the CRD algorithm reduces the dimensionality of hyperspectral image first. The Sherman–Morrison formula is utilized to calculate the matrix inversion to reduce the complexity of overall CRD algorithm. The achieved results demonstrate that the proposed system may obtains shorter processing time of the CRD algorithm than that on 3.40 GHz CPU.

Target detection and tracking is a key technology in intelligent monitoring system. However, it is still difficult to design a robust, accurate and real-time target detection and tracking algorithm. Taking target Tracking as the research focus, this paper focuses on one of the current mainstream algorithms, namely, TLD (tracking-learning-detection) target Tracking algorithm. Based on the theory of TLD target tracking and corner detection, a new target tracking method was proposed to solve the existing problems. A classifier is built online to track and learn the target in real time, and a target contour detector is added in the system to complement the random forest detector to solve the problem of tracking failure. Experimental results show that the proposed algorithm can effectively overcome the influence of fast change or depth occlusion, and improve the tracking efficiency of moving targets.

Traditional radar target detection algorithms are mostly based on statistical theory. They have weak generalization capabilities for complex sea clutter environments and diverse target characteristics, and their detection performance would significantly be reduced. In this paper, the range-azimuth information obtained by scanning radar is converted into PPI image, and a Radar-YOLONet is proposed and used for radar image target detection. The model includes CSPDarknet53, SPP, PANet and Power Non-Maximum Suppression layers. The prediction frame coordinates, target category and corresponding confidence are directly given through the feature extraction network. The network structure strengthens the receptive field and attention distribution structure, and further improves the efficiency of network training. The verification using the constructed X-band navigation radar PPI image dataset shows that compared with the traditional constant false alarm rate detector and the two-stage Faster-RCNN algorithm, the proposed method has significantly improved detection performance under certain false alarm probability conditions. It is more suitable for various environment and target characteristics.

In this paper, we introduce an airborne hyperspectral system for real-time anomaly detection and target detection. The system, named SIM.GA, is made up of an optical head with two hyperspectral sensors, a panchromatic camera and a GPS/INS instrument, two computers to record data and perform real-time processing, and a human-machine interface (HMI) to display the results. SIM.GA has provided several data to the development of the processing module for the forthcoming hyperspectral system named SPHYDER, to be released in 2016. The system has been tested by means of flight measurement campaigns, providing satisfactory results in terms of both mechanical and electrical integrity and processing performance.

With the rapid development of the low-altitude economy and smart city construction, the demand for small target detection has been growing significantly. This paper proposes a deep learning model, MAE-ResTransFPN, for radar target detection tasks in clutter-rich environments. First, real-world measured data is acquired using holographic staring radar, and the Range-Doppler plane is used as the input to the neural network to complete dataset preparation and annotation. Second, the proposed MAE-ResTransFPN model combines the pre-training capabilities of the masked autoencoder (MAE), the feature extraction capabilities of ResNet50, and the multi-scale feature fusion capabilities of Transformer-FPN, aiming to improve the accuracy and robustness of radar target detection in complex environments. Experimental results show that the proposed model achieves 78.2% in precision, 71.4% in recall, and 73.4% in mAP50, demonstrating its effectiveness in radar target detection tasks.

A fundamental task of radar is the detection of targets in noise and clutter. It is very difficult to detect weak targets in heavy sea clutter. In this paper we apply the discrete wavelet transform (WT), along with multilayer feedforward neural networks, to the detection of radar weak targets. The wavelet transform employs the Haar scaling function, which is well matched to the signals from targets. The hard threshold filter is adopted to remove sea clutter. The neural networks are trained with the back-propagation rule, which are used to detect weak targets. The simulated results show that the proposed method is very effective for radar detection of weak targets.

In recent years radar target detection environment is more and more complex. Traditional CFAR (Constant False Alarm Rate) is a technology in which the radar system discriminates the output signal and noise of the receiver to determine whether the target signal exists under the condition that the False Alarm probability is kept Constant. In order to improve the radar target detection performance, a radar target detection method based on NN (Neural Network) is proposed. In this paper, the radar signal received by a single RBFNN is used for network training, and the probability of detection target is studied by combining the binary detection theory. Simulation results show that the proposed algorithm can effectively improve the radar target detection probability.KeywordsRadarConstant false alarm rateNeural networkTarget detection

Among various types of sensors, millimeter-wave radar is widely used, especially in the field of intelligent transportation, but often limited by low signal-to-noise ratio at the same time. As a result, sensor fusion is currently the most common way for target detection. In contrast to this, we focus on the problem of single radar target detection. Considering that automotive radar usually contains a plot generation module (which provides a limited number of candidate targets), the track-before-detect method can meet real-time requirements in ADAS. Successive track cancellation is one of the effective strategies for enhancing the performance of track-before-detect algorithms. We are inspired by this and derive a graph-based radar target detection algorithm. Since the data to be processed between frames have structural similarity, we can measure the correlation between plots better without a large amount of calculation by taking advantage of the dynamically maintained graph. Furthermore, we define a simple state transition process of each plot, which enhances robustness for temporary target signal loss. We verify the effectiveness of the proposed algorithm with both the simulation experiment and real data test in the last part of the paper.

For the neural network (NN) detector in marine radar, its superior performance relies heavily on correctly annotated large datasets bringing expensive cost. The Automatic Identification System (AIS) can be used to automatically construct datasets by obtaining the location of other ships. However, the constructed dataset inevitably contains a large number of noisy labels, which can seriously affect the performance of the NN. Therefore, we propose a robust NN training method for marine radar ship target detection using noisy AIS labels. First, we propose the radar assisted AIS label construction strategy to automatically construct datasets with noisy AIS labels. Second, we analyze and find that the differentiable Neyman Pearson (DNP) loss is robust to noisy AIS labels due to its relaxing effect on incorrect labels and reinforcing effect on correct labels, and propose a dynamic adjustment strategy to make the DNP loss robust to noisy AIS labels with an unknown number of error labels. Third, to effectively train the NN detector with noisy AIS labels and the DNP loss, we propose the Logit-Peak-Distillation network training strategy, where we use the NN logit value processing strategy to quickly switch to lower probability of false alarm (PFA) by avoiding the information loss caused by the sigmoid activation function, use the peak detection strategy to accelerate the NN training by efficiently computing the PFA, and use the ensemble knowledge distillation strategy to reduce the effect of error labels by cleaning the noisy AIS labels into cleaner soft labels. In the experiment, compared with the benchmark methods, our proposed method has the better detection performance on the measured data of marine radar.

Radar detection of maritime targets plays an important role in marine environment monitoring. For civil maritime detection in the areas of inshore coastal, pulse-compression radar is universally used owing to its low cost. The complex sea clutter in the practical application will greatly affect the received radar echoes. Due to the inability to accurately describe the differences in characteristics between sea clutter and maritime targets, the detection performance of methods based on mathematical derivation is not satisfactory in actual deployment. Recently, neural-based methods have made strides in many pattern recognition tasks, such as computer vision and natural language processing. The sophisticated deep neural models can be applied to different downstream tasks due to their powerful learning ability. Inspired by this idea, we propose a maritime radar target detection method in sea clutter based on deep learning. To better model the sequence correlation of radar echoes, we propose a Self-Adaption Local Augmented Long Short-Term Memory (SALA-LSTM) structure. The proposed SALA-LSTM integrates adaptive convolution into vanilla LSTM cells, which not only maintains the inherent overall sequence modeling ability of vanilla LSTM, but also strengthens its ability to perceive the correlation on a small scale in the local scope. Based on SALA-LSTM and other neural structures, we propose a radar target detection network. A measured dataset containing different typical scenarios is utilized to evaluate the detection probability and false alarm rate. The detection performance of our proposed network is superior to that of the existing methods.

In recent years, the micro-Doppler effect has been proposed as a new technique for signature analysis and extraction of radar targets. The ballistic missile is known as a typical radar target and has been paid many attentions for the complexities of its motions in current researches. The trajectory of a ballistic missile can be generally divided into three stages: boost phase, midcourse phase and terminal phase. The midcourse phase is the most important phase for radar target recognition and interception. In this stage, the warhead forms a typical micro-motion called micro-nutation which consists of three basic micro-motions: spinning, coning and wiggle. This paper addresses the issue of signature analysis of ballistic missile warhead in terahertz band via discussing the micro-Doppler effect. We establish a simplified model (cone-shaped) for the missile warhead followed by the micro-motion models including of spinning, coning and wiggle. Based on the basic formulas of these typical micro-motions, we first derive the theoretical formula of micro-nutation which is the main micro-motion of the missile warhead. Then, we calculate the micro-Doppler frequency in both X band and terahertz band via these micro-Doppler formulas. The simulations are given to show the superiority of our proposed method for the recognition and detection of radar micro targets in terahertz band.

Constant false alarm rate (CFAR), a target detection method commonly used in the radar systems, has an inconsistent performance against various environments. For improving the radar detectability, this paper proposes a novel scheme of radar target detection using neural network-based adaptive selection CFAR. The proposed method employs cell-averaging, ordered-statistic, greatest-of and smallest-of CFAR thresholds as the basis of references. The pattern of those threshold values combined with the cell under test signal value will be identified and classified by the neural network to compute the raw threshold. Then, the final threshold is selected depending on the nearest value between raw and four referenced CFARs. The performance of the proposed method is examined against three possible cases of the radar systems including homogeneous background, multiple targets and clutter boundary. The result of this research shows that the proposed method outperforms the classical CFARs due to the adaptive selection algorithm can select properly among referenced CFARs against the given cases particularly in the homogeneous and multiple target environments.

Infrared (IR) point target detection and tracking is challenging due to lack of texture and detailed information of small dim targets. The problem becomes even more challenging when there is a real-time requirement too. A key goal in robust detection is to reduce missed detections (MD) and false alarms (FA). Complex traditional state-of-the-art point target detection algorithms may give accurate results but typically incur larger execution times which renders them unsuitable for real-time applications. On the other hand, deep learning methods for point target detection do not generalize satisfactorily with domain shifts. In this work, we address the problem of real-time detection and tracking of multiple point targets in diverse background conditions. We apply a ’top-hat’ operator as the first stage of our detection algorithm and this is followed by a systematic thresholding scheme to yield a good balance between MD and FA. This is followed by a methodology to find the exact target position from the detections, and a track association scheme in conjunction with Kalman filter for state estimation. Based on the proposed approach for target detection and track association, we perform 2D tracking of image coordinates as well as 3D tracking of azimuth and elevation angles. We verify the effectiveness of our method on 12 different IR image sequences over existing state-of-the-art methods in terms of accuracy as well as speed.

Aiming at the characteristics of small targets, few samples, many occlusions, and strong camouflage in the battlefield environment, a target detection and tracking system based on multi-source sensor data is designed. The system is mainly composed of three parts: the unmanned aerial vehicle (UAV) platform, the ground display control platform (GDCP), and the offline algorithm training platform. The offline-trained target detection and tracking algorithm is deployed in the UAV-borne AI processing chip, which can conduct the real-time detection and tracking of typical targets in the battlefield and send the results and attitude information of UAVs to the GDCP. The system can realize real-time detection of typical battlefield targets and tracking control through the GDCP.