Signal fusion-based target detection algorithm for spatial diversity radar

Multiple input multiple output (MIMO) radars have been intensively studied for their superior performance merits compared to conventional radars including improved spatial resolution and parameter estimation. To achieve these merits, the transmitted waveforms from the MIMO transmitting antennas should be mutually orthogonal. Herein, the orthogonality is attained in the Doppler domain, namely Doppler division multiple access (DDMA), as it enjoys the simplest structure among other orthogonality schemes. Heretofore, the research in MIMO radars has focused on optimizing MIMO radar performance from different aspects including efficient waveform design, antenna beam-forming, and adaptive processing techniques. However, MIMO radars have been also a target of deliberate jamming scenarios. In this paper, the focus is turned to the evaluation of the detection performance of DDMA MIMO radars in jamming environments by creating a two-sided engagement scenario. On the jammer side, a jamming technique is proposed, Doppler-range block jamming (DRBJ), to deny the detection of a DDMA MIMO radar equipped with a moving target detector (MTD). On the radar side, a comparative detection performance analysis of two different MIMO radar detectors, normalized adaptive matched filter (NAMF) and fast 3D constant false alarm rate (CFAR), is conducted using Monte Carlo simulations. This analysis unveils divergent design philosophies of the two detectors, with each prioritizing distinct performance objectives.

The generalized likelihood ratio test (GLRT) for spatial diversity detection is widely used in target detection problems in multiple input, multiple output (MIMO) radar systems. GLRT is derived using the probability density function (pdf) of the noise signal in a homogeneous noise environment. In a nonhomogeneous noise environment, however, the received signal is represented as a summation of several signals with different distributions such that it is difficult to estimate the pdf of the received signal. In addition, GLRT requires knowledge of the number of received signals that contain nonhomogeneous noise components, which is not easy to obtain from the received signals. In this paper, we propose an adaptive selection method for GLRT (ASMGLRT) that overcomes the noise nonhomogeneity problem in MIMO radars. The proposed algorithm estimates the noise statistics via the received signals that contain homogeneous noises only, instead of using all received signals in a reference window. Furthermore, it does not require information on the pdfs of the nonhomogeneous noises and the number of received signals containing nonhomogeneous noises when the received signal has both homogeneous and nonhomogeneous noises. The performance of the proposed algorithm is obtained in terms of detection and false alarm probabilities and is compared to GLRT in both homogeneous and nonhomogeneous noise environments.

A multiple input multiple output (MIMO) radar is an emerging field which has attracted many researchers recently. In this type of radar, unlike an standard phased array radar, one can choose transmitted probing signals freely to maximize the power around the locations of the targets of interest, or more generally to approximate a given transmit beam-pattern, or to minimize the cross-correlation of the signals reflected back to the radar by the targets of interest. These signals can be correlated to each other or noncorrelated. Many papers have investigated beampattern design and waveforming in MIMO radars. One of the most famous approaches for beampattern design is named covariance based method which design cross-correlation matrix of transmitted signals instead of the signals. On the other hand, many papers have investigated beamforming of an standard phased array radar in different ways. In this paper it is desirable to design beampattern of a phased array radar by means of the MIMO radar covariance based beampattern design method, as a special case of general MIMO radar. Although many approaches are available for beamforming of phased array radar this approach would have less complexity compare to them. MATLAB software is being employed for simulations. (6 pages)

Multiple Input-Multiple Output (MIMO) radar is the recent emergence of a new radar technology. This radar system is a new concept advanced for the inadequacy of the traditional system, it use spatial diversity and angel diversity to avoid multipath fading. In this paper, we mainly analyze the detection performance of MIMO radar, and at the same time compared its detection performance with phased array radar under the same conditions. This paper first established MIMO radar’s signal model based on the distribution of sources, and then analyzes the affect of radar’s detection performance through changing the number of antenna. The simulation result showed that the MIMO radar has a good detection performance compared with phased array radar in low signal-noise-ratio(SNR).

Polarimetric Multiple-Input Multiple-Output (MIMO) radar has multiple antennas of different polarimetric directions to transmit and receive signals. For signals in four transmit/receive polarimetric channels, the detection performance is studied via three target detection algorithms, i.e., the Bayesian optimal detection, the coherent accumulation detector and the noncoherent accumulation detector, on conditions that radar targets are anisotropy and two cross polarimetric channels have target returns with different amplitudes. It is found that polarimetric MIMO radar is more stable in target detection performance.

MIMO radar can provide enhanced spatial resolution and achieve better target detection performance. Hence the parameter identifiability of the system can be significantly improved. In this paper, the issue of direction of arrival (DOA) estimation in multiple-input multiple-output (MIMO) radar is discussed, and several algorithms for DOA tracking based on monostatic MIMO radar in impulsive noise background are proposed and studied. These tracking algorithms are based on the concept of robust covariation (ROC), fractional lower order moment (FLOM), fractional lower order cyclic (FLOC) and similar normalization. The transmit and receive arrays of a monostatic narrowband MIMO radar are assumed to be close to each other and eigenstructure based MUSIC algorithm at the receiver is used to identify the DOA's of the targets of interest. Simulation results verify the usefulness of the proposed algorithms. The comparison of these four tracking algorithms show that the algorithm based on the concept of similar normalization has better performance than the others.

The moving target detection based on compressed sending (CS) multiple-input multiple output (MIMO) radar with widely separated antennas is studied. The centralized processing is considered. The traditional centralized MIMO radar requires an exhaustive search on every possible two dimensional target velocity, resulting in huge computational complexity. In this paper, we adopt the CS technique to drastically reduce the computational burden and evaluate the reduction of the required complexity. We derive the moving target detector for the CS MIMO radar. The performance of the proposed detector is analyzed, and the receiver operating characteristic (ROC) curves are provided.

The problem of tracking the direction of arrivals (DOA) and the direction of departure (DOD) of multiple moving targets is considered in bistatic multiple input multiple output (MIMO) radar. A low complexity angle tracking algorithm in bistatic MIMO radar is proposed. The proposed algorithm derived the difference between previous and current covariance matrices. And the relationship between the different of covariance matrices and the different of target angle is derived. The performance of the proposed algorithm is improved by averaging the covariance matrix elements. Last DOD and DOA are got by least square method. This algorithm avoids the decomposition of the covariance matrix and reduces the computational complexity, which realizes DOA and DOD of the adjacent moment automatic association. The simulation results demonstrate effectiveness of the proposed algorithm. The algorithm provides the technical support for the practical application of MIMO radar.

Multiple Input Multiple Output (MIMO) radar is a new radar technique. It has many advantages over conventional phased radar in many ways such as anti-intercept of radar signal, low velocity target detection, and resolution. The signal transmit matrix of MIMO radar has much information such as target angle. In this paper, we analyze the signal model for MIMO radar. And then we present a new method to estimate the target angle from the matrix. At last, we show the efficiency by some simulations.

For airborne radar systems, space-time adaptive processing (STAP) is the approach that is normally adopted to detect ground moving targets in ground clutter, but the technique becomes less effective if the speed of ground moving targets is very low because of the proximity of the Doppler frequency of targets to that of ground clutter. Typical low-speed ground target detection scenarios include detecting ground troops and slowly-moving ground vehicles. Multiple-Input Multiple-Output (MIMO) radar possesses multiple transmitting and receiving channels providing additional diversified reflections for fusion processing in target detection and measurements. MIMO radar has been extensively investigated [1, 2]; but the exact structure of MIMO radar is defined widely different. In this work, an innovative multi-level MIMO radar signal processing architecture is proposed for airborne systems to effectively detect low-speed ground moving targets. The new MIMO-based approach is demonstrated to be effective for detecting such low-speed ground targets in this work, and some simulation results are presented subsequently.

Multiple-input multiple-output (MIMO) radar requires orthogonal waveforms to be transmitted on each transmit element or subarray. One practical problem that arises is the complexity and expense of the hardware needed to implement MIMO radar. Slow-time MIMO waveforms, often referred to as SLO-MO waveforms, allow the implementation of MIMO radar without any hardware modifications to the receiver. However, generation of the SLO-MO MIMO waveforms on the transmitter typically requires a multichannel arbitrary waveform generator, variable RF phase shifters, or using multiple carrier frequencies. All of these options can be expensive, especially at microwave frequencies. This paper describes a technique to generate SLO-MO MIMO waveforms using low-cost passive frequency mixers with existing (non-MIMO) radar architectures. Passive frequency mixers are inexpensive and easy to add inline to existing radar transmitters. This paper presents the concept of double-sideband and quadrature MIMO slow-time radar, along with experimental results from the Duke S-band radar testbed.

The colocated multiple input multiple output (MIMO) radar can simultaneously obtain the multi-dimensional information of target for its diversity advantage. Combining the idea of MIMO radar and multiple pulse repetition frequency (PRF), this paper uses multiple orthogonal signals with different medium PRF simultaneously to obtain the target's range and velocity information of different PRF through matched filtering at the receiver. In the simulations, four orthogonal signals at four different PRF are transmitted to obtain the range and velocity information from the total echo signal. All of the results show the feasibility of this model.

In multiple-input multiple-output (MIMO) radar, the efficiency of target detection in clutter that is distributed over a certain range bin-Doppler frequency region depends on the sidelobe level of the waveformspsila auto- and cross-ambiguity functions within this region. We introduce the MIMO ambiguity function as the sum of all auto- and cross-ambiguity functions of the K MIMO radar waveforms, with the integral of this function over some range-Doppler region serving as a measure of the orthogonality (separability) of the waveforms within this region of interest. We derive an upper bound on the attainable ldquoclear areardquo for the MIMO ambiguity function, and show that, with respect to the conventional single-waveform ambiguity function, the maximum clear area is K times smaller for the MIMO system.

In this paper, we deal with the problem of simultaneously detecting and tracking multiple targets using polarimetric multiple input multiple output (MIMO) radars. The problem is formulated in a Bayesian framework by modeling the collection of states as a random finite set. First, we propose a multiple sensor Multi-Bernoulli (MS-MeMber) filter based track-before-detect (TBD) algorithm suitable for both MIMO radars and polarimetric MIMO radars. Then the sequential Monte Carlo (SMC) implementations are performed to prove the effectiveness of the proposed algorithm. Simulation results show that the polarization diversity can be exploited to enhance the detecting and tracking performance of MIMO radars.

For distributed multiple-input multiple-output (MIMO) radar, signal fusion based detection algorithms can significantly improve the target detection performance, but it may demand a high communication cost. In order to reduce the communication cost with a tiny loss in detection performance, this paper studies a distributed radar detection scheme that would set initial thresholds at local radar sites before transmitting local observations to a signal fusion center. The mechanism to make final decision is given and the probability of false alarm rate is formulated.