Design of Detection-Jamming Shared Waveform Based on Virtual Force Field Algorithm

Due to the technical barriers between radars and jammers and the poor performance of the traditional detection-jamming shared signal in integrated radar-electronic warfare systems, a new detection-jamming shared signal waveform based on the virtual force field algorithm (VFFA) is proposed in this paper. First, a multi-objective and multi-dimensional characteristic parameter optimization model, based on a virtual force field, is established, and then the design principle of the shared signal is presented in detail. The simulation results show that the detection-jamming shared signal based on the VFFA presents the deceptive jamming of multiple false targets in non-collaborative radar. Further, there is better detection performance with the advantages of multiple pulse repetition frequency (PRF) and pulse accumulation number, which are highly sensitive to the multi-jagged PRF signals emitted by the non-collaborative radar. According to the VFFA described in this paper, the optimum detection-jamming shared signal waveform can be output in real time for specific free space targets, to improve the efficiency of integrated radar and electronic warfare systems.

Intelligent Recognition of Chirp Radar Deceptive Jamming Based on Multi-Pulse Information Fusion

The perception of jamming types is very important for protecting our radar in complex electromagnetic environments. Radar active deceptive jamming based on digital radio frequency memory (DRFM) has a high coherence with the target echo, which confuses the information of the target echo and achieves the effect of hiding the real target. Traditional deceptive jamming recognition methods need to extract complex features and artificially set classification thresholds, which is inefficient. The existing neural network-based jamming identification methods still follow the pattern of signal modulation-type identification, so there are fewer types of jamming that can be identified, and the identification accuracy is low in the case of low jamming-to-noise ratios (JNR). This paper studies the input of jamming recognition networks and proposes an improved intelligent identification method for chirp radar deceptive jamming. This method fuses three short-time Fourier transform time–frequency graphs disturbed by three consecutive pulse periods into a new graph as the input of the convolutional neural network (CNN). Using a CNN to classify the time–frequency image has realized the recognition of a variety of common deceptive jamming techniques. Similarly, by changing the network input, the original signal is used to replace the echo signal, which improves the accuracy of the jamming recognition in the case of a low JNR.

A Novel Deceptive Jamming Approach for Hiding Actual Target and Generating False Targets

Deceptive jamming is a popular electronic countermeasure (ECM) technique that generates false targets to confuse opponent surveillance radars. This work presents a novel approach for hiding the actual target while producing multiple false targets at the same time against frequency diverse array (FDA) radar. For this purpose, the modified FDA radar is assumed to be mounted on the actual aircraft. It intercepts the opponent’s radar signals and transmits back to place nulls in the radiation pattern at the desired range and direction to exploit FDA radar’s range-dependent pattern nulling capability. The proposed deceptive jammer produces delayed versions of the intercepted signals to create false targets with multiple ranges to confuse the opponent’s radar system. The novel mathematical model is proposed whose effectiveness is verified through several simulation results for different numbers of ranges, directions, and antenna elements.