Direction of arrival (DOA) estimation in diffuse multipath environments is a challenge for ground-based radar remote sensing applications, which has significant value in military fields, such as air defense surveillance. However, radar received echo usually contains various multipath signals caused by the reflection of complex ground or sea surface. With the introduction of multipath signals, traditional algorithms’ performance on angle estimation decreases severely. In response to this problem, the letter proposes a new time reversal (TR) algorithm used for multiple-input multiple-output (MIMO) radar angle estimation. First, the algorithm reconstructs a TR covariance matrix by multiplexing the data’s rows and columns, increasing the estimation accuracy of the TR covariance matrix. Besides, the letter applies a linearly constrained minimum power (LCMP) constraint to suppress diffuse multipath signals according to the prior knowledge of environments. Simulation results examine the improvement of estimation accuracy by the proposed algorithm, also verify the superiority of the proposed algorithm in different multipath scenarios. What’s more, the algorithm has broader applicability due to avoiding the difficulties of removing the coherence and estimating multipath number in practice.

The identification of sensing group targets and extended targets is of paramount importance in the context of vehicle tracking and early warning detection. As the scope of target monitoring and tracking extends, conventional single-sensor-based tracking techniques are proving to be inadequate in meeting the practical demands of the field. Consequently, multi-sensor fusion tracking technology has emerged as a viable alternative. However, the use of multiple sensors is constrained by their limited fields of view (FOVs), which leads to issues such as the loss of target detection and the introduction of false targets after fusion. Hence, this study proposes a combination of weighted geometric averaging (WGA) and weighted arithmetic averaging (WAA) methods to solve distributed multi-group/extended target tracking with different fields of view. Specifically, a local Poisson multi-Bernoulli mixture (PMBM) filter was first used on individual sensors. Subsequently, we combined a sequential fusion technique and the proposed fusion approach to fuse the PMBM filter densities. The efficacy and superiority of this approach were demonstrated through simulations.

Radar target detection has a wide range of applications in the military and civilian remote sensing fields; in particular, the target detection in multipath environments has attracted many scholars’ attention in recent years. The abundant multipath signals severely interfere with the detection performance and accuracy of parameter estimation of traditional algorithms. Under Gaussian white noise environments, this letter proposes an adaptive time reversal (TR) waveform covariance matrix (WCM) design method with multipath exploitation to improve the maximum signal-to-noise ratio (SNR) at the receiver in multipath environments. This equivalently improves the detection probability. The proposed two-stage algorithm firstly adapts the time-reversal echo to construct a multipath information matrix with a Hermitian structure. Secondly, the letter transforms the maximized SNR problem into semidefinite programming (SDP), which is constrained by a constant total transmit power. Consequently, the waveform covariance matrix is obtained by solving semidefinite programming. Simulation experiments verify the adaptability and effectiveness of the proposed algorithm.

This letter proposes a time-reversal (TR) post-Doppler adaptive multiple signal classification (MUSIC) algorithm for multiple-input multiple-output (MIMO) radars, which addresses the joint estimation of angle and Doppler in diffuse multipath environments. First, an improving TR MIMO multipath model is proposed to avoid the ambiguity between the direction and Doppler in one round trip. Then, the letter designs a spatial filter matrix according to transmit-receive steering matrices, suppressing undesired round trips. Finally, we combine the post-Doppler adaptive MUSIC algorithm and the designed filter to estimate angle and Doppler jointly. Simulation results verify the applicability and effectiveness of the proposed model and algorithm.