This study deals with the problem of radar waveform design based on the weighted mutual information (MI) and the difference of two mutual information metrics (DMI) in signal‐dependent interference. Since the target and clutter information are included in the received signal at the beginning of the design, DMI‐based waveform is designed according to the following criterion: maximizing the MI between the received signal and target impulse response while minimizing the MI between the received signal and the clutter impulse response. This criterion is equivalent to maximizing the difference between the first MI and the second MI. Then maximizing the difference of two types of MI is used as the objective function, and the optimization model with the transmitted waveform energy constraint is established. In order to solve it, we resort to maximum marginal allocation (MMA) method to find the DMI‐based waveform. Since DMI‐based waveform does not allocate energy to the frequency band where the clutter power spectral density (PSD) is greater than the target PSD, we propose to weight the MI‐based waveform and DMI‐based waveform to synthesize the final optimal waveform. It could provide different trade‐offs between two types of MI. Simulation results show the proposed algorithm is valid.

Due to the uncertainties of the radar target prior information in the actual scene, the waveform designed based on the radar target prior information cannot meet the needs of parameter estimation. To improve the performance of parameter estimation, a novel transmitted waveform design method under the hierarchical game model of radar and jammer, which maximizes the mutual information (MI) between the radar target echo and the random target spectrum response, is proposed. In the hierarchical game model of radar and jammer, the radar is in a leading position while the jammer is in a following position. The strategy of the jammer is optimized based on the radar transmitted waveform of previous moment, then the radar selects its own strategy based on the strategy of the jammer. It is generally assumed that the radar and the jammer have intercepted the real target spectrum and then the optimal jamming and the optimal transmitted waveform spectrum are obtained. However, the exact characteristic of the real target spectrum is hard to capture accurately in actual scenes. To simulate this, the real target spectrum is considered to be within an uncertainty range which is confined by known upper and lower bounds. Then, the minimax robust jamming and the maximin robust transmitted waveform are designed successively based on the MI criteria, which optimizes the performance under the most unfavorable condition of the radar and the jammer, respectively. Simulation results demonstrate that the robust transmitted waveform design method guarantees the parameter estimation performance effectively and provides useful guidance for waveform energy allocation.

PTS is highly efficient in peak-to-average power ratio (PAPR) reduction of OFDM systems, but the considerable computational complexity for the required search through a high-dimensional vector space is potential problem for a practical implementation. In this paper, we formulate the search problem of PTS as a combinatorial optimization (CO) problem, then propose an algorithm based on improved discrete particle swarm optimization (I-DPSO), which reduces the number of multiplications required for PTS. Numerical results show that, the proposed algorithm can achieve performance-complexity tradeoff for moderate PAPR reduction.

Cognitive radar can overcome the shortcomings of traditional radars that are difficult to adapt to complex environments and adaptively adjust the transmitted waveform through closed-loop feedback. The optimization design of the transmitted waveform is a very important issue in the research of cognitive radar. Most of the previous studies on waveform design assume that the prior information of the target spectrum is completely known, but actually the target in the real scene is uncertain. In order to simulate this situation, this paper uses a robust waveform design scheme based on signal-to-interference-plus-noise ratio (SINR) and mutual information (MI). After setting up the signal model, the SINR and MI between target and echo are derived based on the information theory, and robust models for MI and SINR are established. Next, the MI and SINR are maximized by using the maximum marginal allocation (MMA) algorithm and the water-filling method which is improved by bisection algorithm. Simulation results show that, under the most unfavorable conditions, the robust transmitted waveform has better performance than other waveforms in the improvement degree of SINR and MI. By comparing the robust transmitted waveform based on SINR criterion and MI criterion, the influence on the variation trend of SINR and MI is explored, and the range of critical value of T_y is found. The longer the echo observation time is, the better the performance of the SINR-based transmitted waveform over the MI-based transmitted waveform is. For the mutual information between the target and the echo, the performance of the MMA algorithm is better than the improved water-filling algorithm.

Spacecraft returning from lunar or interplanetary missions reenter the Earth’ s atmosphere with velocities that can approach 11 km/s. The landing position accuracy on the Earth is severely affected by small errors in the vehicle ’ s position and velocity, unforeseen variations in the atmospheric density and imperfect vehicle modeling. An adaptive guidance control approach is presented to ensure precision of the landing location. The algorithm is a multi-step predictor-corrector iterative approach. A self-adaptive control law, composed of two quantities termed the gross error adjustment and the fine error adjustment, is developed. The gross error adjustment effectively reduces the vehicle cross range error. The guidance law continuously adjusts the capsule’ s bank angle until the bias in the capsule’ s cruising range is less than a prescribed value at which point the fine adjustment reduces the capsule’ s landing position error. The algorithm’ s adaptability is applied to the deep space mission reentry profile for a variety of initial conditions. Results demonstrate adaptability to the bias ranges of -0.6 ° to +0.25° in reentry angle, -25% to +25% in lift-drag ratio, and -80% to +100% in atmospheric density. Additionally, it is shown that the algorithm has the capability to remove a reentry insertion error of more than 100 km.

Most of the previous researches on transmitted waveform design assume that the radar can perceive the real target spectrum, as the environments of the radar target become more and more complex, the real target spectrum cannot be accurately captured by radar in many cases and the required performances of detection and estimation for radar cannot be met. Therefore, under the complex electromagnetic environment and the scenario of multiple targets, to ensure the detection and estimation performance of radar system, the SINR and MI-based robust transmitted waveform design methods are proposed in this paper. Simulation results indicate that the designed robust transmitted waveforms ensure the detection and parameter estimation performance effectively and provide useful guidance for energy allocation of transmitted waveform.