Obtaining precise position of interested emitters passively has wide applications in both civilian and military fields. Different from traditional parameter measurement and direct position determination (DPD) method, recently a new passive localization method based on synthetic aperture technique, named synthetic aperture positioning (SAP), has been proposed. The method compensates for the nonlinear phase produced by relative motion between the moving platform and the emitter, achieving coherent summation of intercepted signals. The SAP can obtain high-resolution and high-precision localization results at a low signal-to-noise ratio. This paper summarizes the research progress of SAP, including localization principles, spaceborne applications, and application scope analysis. Besides, the possible future outlook of SAP is considered.

Recently, researchers have proposed an emitter localization method based on passive synthetic aperture. However, the unknown residual frequency offset (RFO) between the transmitter and the receiver causes the received Doppler signal to shift, which affects the localization accuracy. To solve this issue, this paper proposes a RFO estimation method based on range migration fitting. Due to the high frequency modulation slope of the linear frequency modulation (LFM)-modulation radar signal, it is not affected by RFO in range compression. Therefore, the azimuth time can be estimated by fitting the peak value position of the pulse compression in range direction. Then, the matched filters are designed under different RFOs. When the zero-Doppler time obtained by the matched filters is consistent with the estimated azimuth time, the given RFO is the real RFO between the transceivers. The simulation results show that the estimation error of azimuth distance does not exceed 20 m when the received signal duration is not less than 3 s, the pulse repetition frequency (PRF) of the transmitter radar signal is not less than 1 kHz, the range detection is not larger than 1000 km, and the signal noise ratio (SNR) is not less than –5 dB.

The existing direction-of-arrival (DOA) estimation methods only utilize the current received signals, which are susceptible to noise. In this paper, a method for DOA estimation based on a motion platform is proposed to achieve high-precision DOA estimation by utilizing past and present signals. The concept of synthetic aperture is introduced to construct a linear DOA estimation model. A DOA fine-tuning method based on the linear model is proposed to eliminate the linear DOA variation, achieving a non-coherent accumulation of DOA estimations. Moreover, the baseband modulation and the phase modulation caused by the range history are compensated to achieve the coherent accumulation of all the DOA estimations. Simulation results show that the proposed method can significantly improve the DOA estimated accuracy at low signal-to-noise ratios (SNR).

The multi-source passive localization problem is a problem of great interest in signal processing with many applications. In this paper, a sparse representation model based on covariance matrix is constructed for the long-range localization scenario, and a sparse Bayesian learning algorithm based on Laplace prior of signal covariance is developed for the base mismatch problem caused by target deviation from the initial point grid. An adaptive grid sparse Bayesian learning targets localization (AGSBL) algorithm is proposed. The AGSBL algorithm implements a covariance-based sparse signal reconstruction and grid adaptive localization dictionary learning. Simulation results show that the AGSBL algorithm outperforms the traditional compressed-aware localization algorithm for different signal-to-noise ratios and different number of targets in long-range scenes.

Traditional single-satellite passive localization algorithms are influenced by frequency and angle measurement accuracies, resulting in error estimation of emitter position on the order of kilometers. Subsequently, a single-satellite localization algorithm based on passive synthetic aperture (PSA) was introduced, enabling high-precision positioning. However, its estimation of azimuth and range distance is considerably affected by the residual frequency offset (RFO) of uncooperative system transceivers. Furthermore, it requires data containing a satellite flying over the radiation source for RFO search. After estimating the RFO, an accurate estimation of azimuth and range distance can be carried out, which is difficult to achieve in practical situations. An LFM radar source passive localization algorithm based on range migration is proposed to address the difficulty in estimating frequency offset. The algorithm first provides a rough estimate of the pulse repetition time (PRT). It processes intercepted signals through range compression, range interpolation, and polynomial fitting to obtain range migration observations. Subsequently, it uses the changing information of range migration and an accurate PRT to formulate a system of nonlinear equations, obtaining the emitter position and a more accurate PRT through a two-step localization algorithm. Frequency offset only induces a fixed offset in range migration, which does not affect the changing information. This algorithm can also achieve high-precision localization in squint scenarios. Finally, the effectiveness of this algorithm is verified through simulations.

In this paper, an antenna array composed of circular array and orthogonal linear array is proposed by using the design of long and short baseline “orthogonal linear array” and the circular array ambiguity resolution design of multi-group baseline clustering. The effectiveness of the antenna array in this paper is verified by sufficient simulation and experiment. After the system deviation correction work, it is found that in the L/S/C/X frequency bands, the ambiguity resolution probability is high, and the phase difference system error between each channel is basically the same. The angle measurement error is less than 0.5°, and the positioning error is less than 2.5 km. Notably, as the center frequency increases, calibration consistency improves, and the calibration frequency points become applicable over a wider frequency range. At a center frequency of 11.5 GHz, the calibration frequency point bandwidth extends to 1 200 MHz. This combined antenna array deployment holds significant promise for a wide range of applications in contemporary wireless communication systems.

In view of low recognition rate of complex radar intra-pulse modulation signal type by traditional methods under low signal-to-noise ratio (SNR), the paper proposes an automatic recognition method of complex radar intra-pulse modulation signal type based on deep residual network. The basic principle of the recognition method is to obtain the transformation relationship between the time and frequency of complex radar intra-pulse modulation signal through short-time Fourier transform (STFT), and then design an appropriate deep residual network to extract the features of the time-frequency map and complete a variety of complex intra-pulse modulation signal type recognition. In addition, in order to improve the generalization ability of the proposed method, label smoothing and L2 regularization are introduced. The simulation results show that the proposed method has a recognition accuracy of more than 95% for complex radar intra-pulse modulation signal types under low SNR (2 dB).

Underwater direction of arrival (DOA) estimation has always been a very challenging theoretical and practical problem. Due to the serious non-stationary, non-linear, and non-Gaussian characteristics, machine learning based DOA estimation methods trained on simulated Gaussian noised array data cannot be directly applied to actual underwater DOA estimation tasks. In order to deal with this problem, environmental data with no target echoes can be employed to analyze the non-Gaussian components. Then, the obtained information about non-Gaussian components can be used to whiten the array data. Based on these considerations, a novel practical sonar array whitening method was proposed. Specifically, based on a weak assumption that the non-Gaussian components in adjacent patches with and without target echoes are almost the same, canonical correlation analysis (CCA) and non-negative matrix factorization (NMF) techniques are employed for whitening the array data. With the whitened array data, machine learning based DOA estimation models trained on simulated Gaussian noised datasets can be used to perform underwater DOA estimation tasks. Experimental results illustrated that, using actual underwater datasets for testing with known machine learning based DOA estimation models, accurate and robust DOA estimation performance can be achieved by using the proposed whitening method in different underwater conditions.