Application of radar signal processing and image display algorithm based on computer hardware system in intelligence processing

Secondary surveillance radar (SSR) refers to the main sources of information support for both the airspace control system and air traffic control and is an open two-channel requesting system for transmitting an aerial object (AO) request signal, and response signal (RS) to the requester. The principle of constructing an aircraft responder (AR) (an open single-channel queuing system with refuses) and SSR as a whole (asynchronous information network) predetermined a significant density of intra-system interference both in the request channel and in the response channel. It also provided a possibility for an interested party to use an AR through an unauthorized request both to obtain on-board information and to paralyze aircraft responders by emitting request signals of the required intensity, which significantly reduces the quality of its work. The paper presents the synthesis and analysis of the optimal, based on the Neumann-Pearson criterion, the detector of air objects by the secondary surveillance radar in the conditions of the action of in-system and deliberate correlated and uncorrelated interference in the request and response channel. The detection of a burst of RSs in the SSR is reduced to a comparison with the threshold of the sum of the number of RSs with one detected pulse, and the RSs taken with a weight coefficient of the number of response codes with two detected pulses, which leads to an increase in the probability of detecting AO. The weight coefficient value shows how much, when an airborne object is detected, the RS from two detected pulses is more valuable than the RS from one detected pulse.

Optimizing Airborne Object Detection of Secondary Surveillance Radar in Intra-System Interference Conditions

This work provides a new demonstration of most of the important keys to interface the design, and implementation of the secondary surveillance radar (SSR) system, with industrial considerations to achieve a competitive SSR product. We describe the specifications, the design and implementation of real-time SSR radar system with a stand-alone interrogator. The investigation is that specializing in the economical design ideas for SSR systems by formulating the concepts of an advanced design, the requirements of the system engineering design and the disciplinary perspective for radar production. The feasibility study includes the design procedures, schematics, simulation with suitable Computer-aided design tool. The motivation behind this work are increasing reliability with competitive price. The hardware has designed to be smaller, lighter, less expensive and easy to repair than a conventional SSR system. The IF receiver has a great variations dynamic range (100 dB) varies from 400mv to 2.4V. The configuration of SSR consists of a terminal approaches radar to 300 Km with a high-resolution angle about 2.5 degree after the reception secondary lobe suppression function. It clearly shows that the proposed SSR can improve the reception performance significantly at reply with low signal strength.

An Industrial Design and Implementation Approach of Secondary Surveillance Radar System

The presented work analyzes the effect of random loss of received signals due to the finite availability factor of aircraft responders of requesting radar surveillance systems on the optimal algorithm for processing response signals. It is shown that the optimal detection algorithm when the detector and the device for estimating the parameters of the received signal data work together is reduced to comparing the likelihood ratio averaged over the measured parameter with the threshold level. Its value is determined not only by the costs of errors of the first and second kind, but also by a priori data on the measured parameter, as well as by the results of processing the adopted implementation in the meter.

Optimal Measurement of Signal Data Parameters of Requesting Radar Systems

In the presented work, based on the Neyman-Pearson criterion, the optimal structure for detecting request signals in an aircraft responder of the SSR (IFF) system is synthesized, in which the optimization of the detection of signaling information is carried out not only in time, but also in the spatial parameters of the received request signals, and also takes into account the relative throughput of the aircraft responder. It is shown that the interchannel fusion of the results of detection of the component pulses of the request signal is more preferable in comparison with the existing algorithm for merging the results of the detection of the request signal, since it allows to improve the quality of detection of the request signals and somewhat reduce the dependence of the probability of detecting the request signal on the relative throughput of the aircraft responder. 

Optimizing the Request Signals Detection of Aircraft Secondary Radar System Transponders

In addition to the target echo, the signal environment of the secondary surveillance radar (SSR) also includes environmental echo and noise interference, which greatly affect the correct decoding of the response signal. Nowadays, the processing of secondary radar response signals basically uses traditional signal processing methods, and there are few methods for denoising using deep learning neural networks. This paper proposes a secondary radar signal processing method based on the deep residual separable neural network (DRS-Net), which can effectively extract the deep features of the secondary radar signal and predict the original response signal. The core of the network is based on the deep separable convolutional neural network, and the deep residual connection structure can effectively learn the deep features of the signal. We conducted a lot of experiments and verifications using secondary radar response signals with different signal-to-noise ratio noise. The experimental results show that the method has high denoising performance in the normal radar operating environment and can accurately predict secondary radar response signals. When the signal-to-noise ratio is 5dB, the strict accuracy rate can reach 94%. When the signal-to-noise ratio is higher than 10dB, the strict accuracy rate has reached 99.95%.

Secondary Radar Signal Processing Based on Deep Residual Separable Neural Network

The secondary surveillance radar system is the main surveillance method for national defense and domestic civil aviation. It will be interfered by noise during the transmission process, affecting the clarity of signal transmission, and also leading to a reduction in the stability and reliability of radio transmission. Although the traditional denoising method is effective, there is still a lot of noise remaining, which affects the signal detection. This paper proposes a secondary radar signal processing method based on the two-channel deep residual neural network (2CD-NET), which can effectively extract the deep features of the secondary radar signal and predict the response timing signal. It is verified by experiments that under the normal working noise environment of the secondary radar, the two-channel deep residual neural network has strong noise suppression performance. When the signal-to-noise ratio is 10dB, the signal-to-noise ratio of the test set can reach 99.9S%. When the signal-to-noise ratio is 5dB, the test set accuracy rate can reach 94.71%.

Du Xue

Papers

Secondary Radar Signal Processing Based on Deep Residual Separable Neural Network

Secondary Surveillance Radar Signal Processing Based on Two-channel Deep Residual Network