This paper presents an effective metric to evaluate different kinds of low probability of interception (LPI) waveforms. Based on the common view that white noise is the best LPI waveform, the method introduced in this paper first use the asymptotic spectral distribution of Wigner matrix as the property of white noise and use the spectral distribution of the normalized sample covariance matrix as the property of a specific waveform. Then, a numerical approximation of Kullback-Leibler divergence (NA-KLD) is deduced to measure the distance between the two distributions. The NA-KLD is regarded as the metrication to evaluate LPI waveforms. A lower value of NA-KLD represents a better LPI performance. Simulations show that the proposed NA-KLD is effective and robust to evaluate LPI radar waveforms.

Finding a balance between observation duration and detection rates is the ultimate goal of the detection of ultrahigh speed targets. However, short observation durations, both across range unit, and Doppler frequency migration, may severely limit the detection performance of ultrahigh speed targets. Although, traditional coherent integration methods can efficiently accumulate signal energy to produce a high signal-to-noise-ratio measurement, they often need to search for unknown motion parameters. This search is time consuming and unacceptable for the real-time detection of ultrahigh speed targets. In this paper, a coherent-like detection method is designed based on the finite-dimension theory of Wigner matrices along with velocity identification. The proposed method can efficiently integrate signal energy without rendering motion parameters. We use the distribution and mean of the eigenvalues of the constructed matrix, i.e., an additive Wigner matrix, to identify velocities and detect ultrahigh speed targets, respectively. Simulation results validate the theoretical derivation, superiority and operability of the proposed method.

In this paper, the design of low probability of intercept (LPI) radar waveforms considers not only the performance of passive interception systems (PISs), but also radar detection and resolution performance. Waveform design is an important considerations for the LPI ability of radar. Since information theory has a powerful performance-bound description ability from the perspective of information flow, LPI waveforms are designed in this paper within the constraints of the detection performance metrics of radar and PISs, both of which are measured by the Kullback–Leibler divergence, and the resolution performance metric, which is measured by joint entropy. The designed optimization model of LPI waveforms can be solved using the sequential quadratic programming (SQP) method. Simulation results verify that the designed LPI waveforms not only have satisfactory target-detecting and resolution performance, but also have a superior low interception performance against PISs.

For the complex battlefield electromagnetic environment, low probability of interception (LPI) performance has become an indispensable ability for modern radars. Waveform selection is one of the most fundamental and effective technical approaches to achieve the LPI performance of radar. However, the existing LPI waveforms are often optimized by some incomplete or weak performance representation metrics of radar or passive intercept devices (PIDs), which leads to a poor LPI performance of the designed waveform. From the perspective of information flow, this paper reformulated the processes of radar target tracking and the interception and identification of PIDs. For simplifing the LPI waveform selection optimization model, the interception and identification performance optimization criterions of PIDs are well-integrated into a comprehensive metric, which is measured by Kullback-Leibler (KL) divergence. Combining it with the radar tracking performance metric which is measured by the mutual information, an LPI waveform selection optimization model is established, which gives full consideration to both radar and PIDs performance. And, a two-round selection method is proposed to solve the optimization model. In addition, a multi-sensor cooperative tracking mechanism based on the accumulated tracking error constraint is designed for radar radiation control. The optimal waveform selection in the framework of multi-sensor cooperative tracking can improve the LPI performance of radar in both the waveform domain and the energy domain. Simulation results validate the effectiveness of the performance optimization metrics of radar and PIDs, and the superiority and feasibility of the designed waveform selection method in the multi-sensor cooperative target tracking performance and LPI performance.

Since the passive sensor has the property that it does not radiate signals, the use of passive sensors for target tracking is beneficial to improve the low probability of intercept (LPI) performance of the combat platform. However, for the high-maneuvering targets, its motion mode is unknown in advance, so the passive target tracking algorithm using a fixed motion model or interactive multi-model cannot match the actual motion mode of the maneuvering target. In order to solve the problem of low tracking accuracy caused by the unknown motion model of high-maneuvering targets, this paper firstly proposes a state transition matrix update-based extended Kalman filter (STMU-EKF) passive tracking algorithm. In this algorithm, the multi-feature fusion-based trajectory clustering is proposed to estimate the target state, and the state transition matrix is updated according to the estimated value of the motion model and the observation value of multi-station passive sensors. On this basis, considering that only using passive sensors for target tracking cannot often meet the requirements of high target tracking accuracy, this paper introduces active radar for indirect radiation and proposes a multi-sensor collaborative management model based on trajectory clustering. The model performs the optimal allocation of active radar and passive sensors by judging the accumulated errors of the eigenvalue of the error covariance matrix and makes the decision to update the state transition matrix according to the magnitude of the fluctuation parameter of the error difference between the prediction value and the observation value. The simulation results verify that the proposed multi-sensor collaborative target tracking algorithm can effectively improve the high-maneuvering target tracking accuracy to satisfy the radar’s LPI performance.

With the development of airborne radar radio frequency stealth (RFS) technology, the method of improving the RFS performance of airborne radar by optimizing target detection performance has been extensively studied. However, for wideband radar signals, the traditional point target model appears as an extended target model in the range-dimension, which is unfavorable to the detection of target echoes. To overcome the existing drawbacks, this paper devises an efficient echo detection algorithm from the perspective of information theory and random matrix. Firstly, aperiodic agile wideband radar signals are utilized to observe targets. Then, one frame of echo signals in the same range gate is reconstructed into a data form conforming to the Wigner matrix spectral decomposition. Finally, according to the signal detection theory, Kullback-Leibler Divergence (KLD) is used as the test statistic to complete the echo detection of the stealthy extended targets. By statistical analysis and comparison with other established echo detection algorithms, simulation results manifest that the proposed algorithm has superior detection performance and strong robustness, which not only makes up for the deficiency of traditional narrowband radar detection algorithms, but also increases the detection probability of radar system when it is faced with stealthy extended targets.