This letter proposes the use of neural networks to realize the passive localization by signal time difference of arrival (TDOA). In the face of multiple complex targets with radiation sources in a specific area, real-time localization is an urgent problem. In this letter, positions of the known targets from the prior data are obtained and their time difference is calculated, which will be connected as data pairs and input to the neural network trained to obtain a corresponding model. Subsequently, unknown targets can be localized by this network. It is verified that the localization accuracy of the algorithm is reliable and its robustness is higher than that of traditional algorithms. The proposed method also shows that great reduction of operation time depending on the previous network training can complete real-time goals.

Time-frequency images (TFIs) of radar emitter signals can reflect intra-pulse modulation information and be utilized to recognize waveforms, which is helpful for radar emitter identification. However, radar signals are usually interfered with by noise, therefore the robustness of TFIs needs to be improved. This letter proposes a TFI denoising method based on neural networks which can contribute to identifying radar waveforms at the low signal-to-noise ratio (SNR). Firstly, a neural network is trained to generate denoised spectrums from time-domain signals directly. Then the discrete Fourier transform operation in the short-time Fourier transform is replaced by the trained neural network to obtain denoised TFIs. Finally, several advanced convolutional neural networks (CNNs) are employed to classify these TFIs to realize waveform recognition and verify the effect of the proposed denoising method. In the experiment, 16 types of waveforms are simulated for recognition through several methods. The results show that the proposed method reduces the noise in the TFIs significantly, leading to the recognition accuracy being improved under different CNN structures.

To weaken the effect of receiver location error on localization accuracy and make the localization model closer to the practical scenario, this paper considers the receiver location errors, usually neglected in prior studies into the measurement model, and proposes an algebraic method for locating a moving source using time difference of arrival (TDOA), frequency difference of arrival (FDOA), and differential Doppler rate measurements. The proposed method is based on the pseudo-linear set of equations and two-step weighted least square estimator. Only noise values of receiver locations and three types of positioning measurements are available for processing. In addition, a new Cramér-Rao lower bound (CRLB) combining TDOA, FDOA, and differential Doppler rate in the presence of receiver location errors is also derived in this paper. Theoretical analysis and simulation results both indicate that the proposed method can attain CRLB at a moderate noise level, avoid the rank deficiency problem efficiently, and achieve a significant improvement over the existing methods.

Radar signal deinterleaving is used to separate interleaved pulse streams in the electronic support measure (ESM) systems. The histogram methods based on the difference in time of arrival (TOA) are more mature in engineering applications than other methods because of their more effortless implementation. In the increasingly complex electronic battlefield, pulse repetition interval (PRI) jitter, and pulse missing are inevitable in the interleaved pulse stream. The TOA differences of pulse trains with PRI jitter distributes in multiple adjacent PRI bins, which results in the significant reduction of the corresponding histogram value. Furthermore, the histogram values of real PRIs vary with pulse missing, which indicates that the histogram lacks robustness to pulse missing. First‐order difference curve based on sorted TOA difference sequence, denoted as FDC‐DTOA, is proposed in this paper to overcome the shortcomings mentioned above. In this method, multi‐order TOA differences are calculated and then sorted in ascending order. Then, FDC‐DTOA is obtained after a first‐order backward difference operation. Finally, an adaptive threshold is applied to FDC‐DTOA to extract candidate PRIs. The simulation results show that the proposed method has remarkable robustness to pulse missing and excellent performance in deinterleaving the pulse stream with PRI jitter.

A method of automatic recognition of radar waves based on time-frequency analysis (TFA) and ConvNeXt model is proposed in the paper. The method aims to address the challenges of feature extraction difficulty and low recognition correctness in complex multi-class radar waveform recognition under the conditions of low signal-to-noise ratio (SNR) and sample imbalance. The first step is to convert the signal’s time domain waveform into a two-dimensional time-frequency image (TFI) using TFA. This allows for the essential characteristics of the signal to be reflected. Next, TFI preprocessing is carried out to retain complete information, which is then adapted to the input of the deep learning (DL) model. Finally, ConvNeXt model was constructed to automatically extract signal TFI features to realize radar waveform classification and recognition, and focal loss was embedded to increase the weight of classes with small sample numbers in training and solve the problem of sample imbalance. Therefore, ConvNeXt network has stronger feature learning and representation ability compared with other algorithms based on deep learning, and effectively improves the overall recognition rate of 16 classes signals. The recognition accuracy of 6 types of signals with similar time-frequency characteristics (Frank, LFM, P1, P2, P3, P4) is higher, and the recognition rate is close to normal samples under the condition of unbalanced samples. Experimental results verify the effectiveness of the proposed algorithm.