Integrity of trajectory estimated by detection and avoidance tracking system

Abstract

The invention relates to integrity of a trajectory estimated by a detection and avoidance tracking system. In one embodiment, a system includes a processor onboard a vehicle; a monitoring sensor; and a DAA module that receives a sensor measurement trajectory and includes a tracking system that tracks an object in an environment around the vehicle. The tracking system includes a data association module that includes a trajectory-to-trajectory function that outputs a primary solution trajectory and a sub-solution trajectory having trajectory information. An integrity module is in communication with the DAA module and includes a trajectory integrity system in communication with the data association module and operative to provide an integrity check. A trajectory integrity system that compares, selects, and outputs a primary solution trajectory or a sub-solution trajectory based on related trajectories and separation provided by the trajectory-to-trajectory function; sending the track solution passing the integrity test to a pruning function, and sending the track which does not pass the integrity test to the data association module; and ensuring the integrity of the trajectory correlated and estimated by the tracking system.

Description

Background Technology

[0001] Unmanned Aircraft Systems (UAS) must perform the same tasks as manned aircraft to operate within National Airspace (NAS). One of the key capabilities required for UAS operation within the NAS is Detection and Avoidance (DAA) air traffic. Therefore, UAS are equipped with DAA systems to detect and track air traffic and generate avoidance or guidance commands, such as maintain avoidance or collision avoidance, enabling the UAS to maintain air traffic avoidance and comply with the rules governing operations within the NAS. DAA systems use multiple surveillance sensors, including cooperative and non-cooperative sensors, to track both cooperative and non-cooperative traffic.

[0002] A multi-sensor fusion system has been developed that uses cooperative and non-cooperative sensors within a single framework to track multiple intruding aircraft in three dimensions (3D) relative to a friendly vehicle. This system correlates measurements from both cooperative and non-cooperative sensors originating from the same intruding aircraft to provide a statistically optimal estimate of the intruding aircraft's trajectory statistics or path relative to the friendly aircraft. Evaluation and guidance systems use these trajectories to ensure that the friendly vehicle avoids other air traffic.

[0003] However, it is necessary to ensure the integrity of the estimated trajectory formed by the correlation or association of measurement trajectories from multiple sensors. Summary of the Invention

[0004] A system comprising: at least one processor mounted on a vehicle; a plurality of monitoring sensors operatively communicating with the at least one processor, the monitoring sensors including one or more cooperative sensors, one or more non-cooperative sensors, or a combination of one or more cooperative sensors and one or more non-cooperative sensors; and a detection and avoidance module hosted by the at least one processor. The detection and avoidance module is configured to receive corresponding measurement trajectories from the monitoring sensors and includes a tracking system configured to track one or more objects in an environment surrounding the vehicle. The tracking system includes a data association module including a trajectory-to-trajectory function operable to generate and output a master solution trajectory and sub-solution trajectories having trajectory information including the total number of trajectories and trajectory weights, sensor weights, a state average vector for each trajectory, and a state covariance matrix; and an integrity module hosted by the at least one processor and operatively communicating with the detection and avoidance module. The integrity module includes a trajectory integrity system operatively communicating with the data association module. The trajectory integrity system operates to provide integrity checks using fault detection and troubleshooting, which includes one or more discriminators and one or more decision thresholds. The trajectory integrity system operates to compare, select, and output a master solution trajectory or one or more sub-solution trajectories based on at least one decoupling technique and associated or related trajectories provided by the trajectory-to-trajectory function from the data association module. The trajectory integrity system operates to select trajectory solutions that pass the integrity test and send them to the pruning sub-function, and to send the master solution trajectory or the one or more sub-solution trajectories that fail the integrity test back to the data association module for further processing, and to ensure the integrity of the trajectories correlated and estimated by the tracking system. Attached Figure Description

[0005] The features of the invention will become apparent to those skilled in the art from the following description with reference to the accompanying drawings. It should be understood that the drawings illustrate only typical embodiments and are therefore not intended to limit the scope of the invention. The invention will be described with additional features and details using the drawings, wherein:

[0006] Figure 1 This is a block diagram of a system for providing integrity checks for a vehicle-specific DAA tracking system according to one implementation scheme;

[0007] Figure 2 It is a block diagram based on a specific implementation of a monitoring sensor arrangement that can be used in a DAA tracking system;

[0008] Figure 3It is a block diagram of a complete DAA tracing architecture based on an implementation scheme;

[0009] Figure 4 This is a block diagram of an exemplary aircraft tracking system that can be adopted in a complete DAA tracking architecture;

[0010] Figure 5 This is a block diagram of an exemplary tracking algorithm that can be employed in a complete DAA tracking architecture;

[0011] Figure 6 This is a schematic diagram of an exemplary desegmentation filter arrangement that can be utilized by the integrity check function of a DAA tracking system; and

[0012] Figure 7 It is a block diagram based on a specific implementation of the integrity check operation for the DAA tracking system. Detailed Implementation

[0013] In the following detailed description, embodiments are fully described to enable those skilled in the art to practice the invention. It should be understood that other embodiments may be utilized without departing from the scope of the invention. Therefore, the following detailed description should not be considered limiting.

[0014] This paper describes a system and method for ensuring the integrity of trajectories estimated by a DAA tracking system. The basic approach to ensuring the integrity of trajectories estimated by a DAA tracking system uses deconvolution to ensure the correlation or relevance of measurement trajectories that contribute to the trajectory estimated by the tracking system. Specific implementations of deconvolution, combined with trajectory statistics derived from different combinations of measurement trajectories estimated by the DAA tracking system, are used to ensure trajectory integrity.

[0015] Generally, the DAA tracking system disclosed herein operates to track various objects using cooperative and non-cooperative surveillance sensors. The DAA tracking system fuses statistical values ​​or measured trajectories from surveillance sensors to estimate a statistically optimal trajectory for the object relative to a friendly vehicle, where the object may be an intruding aircraft and the friendly vehicle may be an aircraft, including unmanned aerial vehicles (e.g., part of a UAS) or urban air traffic (UAM) vehicles.

[0016] As used herein, a “trajectory” includes the trajectory statistics of an object relative to a friendly vehicle. A “measured trajectory” is a sequence of measured statistics derived from an object with an identification number (ID), such as an intruding aircraft. An “internal trajectory” refers to an estimated trajectory within the tracking system that has not yet been transmitted to any downstream system.

[0017] Downstream applications of DAA tracking systems require one trajectory per object, regardless of the number of monitoring sensors observing that object. DAA tracking systems must identify and fuse measurement trajectories originating from the same object, regardless of the quality of the trajectories provided by the sensors. Sensor measurement errors may cause the DAA tracking system to fuse measurement trajectories from different objects, or not to fuse those originating from the same object. DAA tracking systems must also prevent erroneous measurements from leading to correlations with non-existent objects, resulting in trajectories of aircraft that do not exist in random areas of the airspace. Incorrect correlation or misassociation of measurement trajectories may result in more trajectories than actually operating in the airspace around the friendly aircraft, trajectories showing objects operating in incorrect areas of the airspace, or erroneous trajectories of non-existent objects. This causes the guidance algorithm to calculate avoidance maneuvers for non-existent or too many objects, and maneuvers may be initiated prematurely during an encounter, executed too aggressively, resulting in more fuel consumption than necessary, or potentially guiding the friendly aircraft into dangerous areas of the airspace.

[0018] The method disclosed herein ensures the integrity of the trajectory estimated by the DAA tracking system by ensuring the integrity of the correlation or related results performed by the DAA tracking system. The method disclosed herein also minimizes clutter on the pilot's display.

[0019] The method of this disclosure enhances the tracking framework by utilizing dissociation for correlation or correlation integrity. The system of this disclosure monitors the correlation or correlation of measured trajectories to meet trajectory requirements, wherein different combinations of monitoring sensors contribute to the trajectory estimated by the tracking system. This process ensures the correlation or correlation between the trajectory and the resulting trajectory statistics. Furthermore, common processing of monitored sensor measured trajectories occurs within a single tracking and dissociation framework. Therefore, the contributions of cooperative and non-cooperative monitoring sensors to the correlation or correlation of the estimated trajectory can be ensured within a single framework. The enhanced tracking system of this disclosure can eliminate erroneous correlations or correlations caused by faulty sensor measured trajectories, large measured trajectory errors, and incorrect sensor measured trajectories, and can recover more quickly once the sensor measured trajectories become available or are re-available.

[0020] The threat space includes monitoring sensor failure modes. These modes include cooperative sensor failure modes and non-cooperative sensor failure modes.

[0021] Further details of various embodiments are described below with reference to the accompanying drawings.

[0022] Figure 1This is a block diagram of a system 100 for providing integrity checks of a DAA tracking system for vehicle 102, according to one embodiment. System 100 typically includes: at least one processor 110, which is onboard on vehicle 102 (such as an aircraft); and a plurality of surveillance sensors 120 operatively communicating with processor 110. Surveillance sensors 120 may include one or more cooperative sensors 122, one or more non-cooperative sensors 124, or a combination of cooperative and non-cooperative sensors 124. Examples of cooperative sensors 122 include Automatic Dependent Surveillance-Broadcast (ADS-B) sensors and Traffic Collision Avoidance System (TCAS) Mode-S sensors. Examples of non-cooperative sensors 124 include TCAS Mode-C sensors, airborne radar, ground-based radar, and visual sensors (such as electro-optical (EO) or infrared cameras).

[0023] The cooperative sensor 122 operates to provide a corresponding first measurement trajectory for each of one or more objects in the environment surrounding the vehicle 102. Each corresponding first measurement trajectory includes a corresponding first trajectory identifier (ID). The non-cooperative sensor 124 includes three categories of sensors. A first category of the non-cooperative sensor 124 operates to provide a corresponding second measurement trajectory for each of one or more objects in the environment surrounding the vehicle, wherein each corresponding second measurement trajectory includes a corresponding second trajectory ID. A second category of the non-cooperative sensor 124 includes: a first subcategory that operates to provide a consistent sequence of trajectory measurement results originating from the same object but having inconsistent and varying trajectory IDs; and a second subcategory that operates to provide multiple measurement trajectories of an object with consistent IDs, but having multiple measurement trajectories of that object at measurement time points. A third category of the non-cooperative sensor 124 operates to provide measurement returns that do not have measurement trajectory correlation or IDs.

[0024] Furthermore, system 100 includes a detection and avoidance module 130, hosted by processor 110, wherein the detection and avoidance module 130 is configured to receive corresponding measurement trajectories from monitoring sensors 120. The detection and avoidance module 130 includes a tracking system 132 configured to track one or more objects in the environment surrounding vehicle 102. Tracking system 132 includes a trajectory-to-trajectory function in data association module 134, which operates to generate and output a master solution trajectory and sub-solution trajectories with trajectory information including the total number of trajectories, trajectory weights, sensor weights, the state average vector of each internal trajectory, and the state covariance matrix of each internal trajectory.

[0025] System 100 also includes an integrity module 140, which is hosted by processor 110 and operatively communicates with detection and avoidance module 130. Integrity module 140 includes a trajectory integrity system 142, which operatively communicates with data association module 134. Trajectory integrity system 142 operates to provide integrity checks on associated trajectories using fault detection and elimination (FDE), which includes one or more discriminators and decision thresholds. Trajectory integrity system 142 operates to compare, select, and output a master solution trajectory or one or more sub-solution trajectories based on decoupling techniques and internal trajectories provided by trajectory-to-trajectory functionality from data association module 134. Trajectory integrity system 142 operates to send the master solution trajectory or sub-solution trajectory back to data association module 134 for further processing and to ensure the integrity of the trajectory estimated by tracking system 132.

[0026] Generally, the integrity method includes the following steps: calculating a master solution trajectory using all sensor measurement trajectories that contribute to the trajectory; calculating one or more sub-solution trajectories using combinations of sensors that contribute to the master solution trajectory; and calculating one or more sub-sub-solution trajectories using combinations of sensors that contribute to each sub-solution trajectory. Incorrectly associated trajectories or measurement trajectories determined not to originate from the same object are returned to the data association module for further processing. When estimating the master solution trajectory using measurement trajectories from at least three of the multiple sensors providing the measurement trajectories, one or more sub-solutions are configured to identify one incorrectly associated sensor measurement trajectory, and one or more sub-sub-solutions are configured to identify two or more incorrectly associated sensor measurement trajectories.

[0027] As further described below, the tracking system 132 operates to fuse corresponding measurement trajectories from the cooperative sensor 122 and the non-cooperative sensor 124 to estimate the optimal trajectory for each object in the operating environment surrounding the vehicle 102. The estimated trajectory is used by the detection and avoidance module 130 to provide guidance data, such as for guidance and control (block 150), to various vehicle systems, enabling the vehicle 102 to be manipulated to avoid the tracked object.

[0028] Additional details relating to various aspects of the complete DAA tracking system disclosed herein are described below.

[0029] Monitoring sensors

[0030] The DAA tracking system supports a range of sensor modalities and fusion strategies to maximize the probability that a trajectory corresponds to a real object (such as an intruding aircraft) and minimize the number of erroneous trajectories. The various surveillance sensors employed in the system can be airborne on friendly vehicles (such as a UAS), non-airborne sensors such as ground-based sensors, or a combination of both.

[0031] As mentioned above, surveillance sensors can include cooperative sensors and various categories of non-cooperative sensors. Cooperative sensors provide measurement trajectories of objects (such as intruding aircraft) along with their ICAO IDs. Non-cooperative surveillance sensors provide measurement trajectories of objects with sensor-specific IDs. Measurement trajectories consist of sequences of measurements originating from the same object, each accompanied by either an ICAO ID or a sensor-specific ID.

[0032] Non-cooperative sensors can be categorized into three groups based on the type of measurement results provided: Category 1 – measurement tracks with a consistent sensor-specific ID; Category 2 – measurement tracks with inconsistent IDs or multiple measurement tracks with the same ID; and Category 3 – measurement returns without an ID. A typical difference between Category 1 non-cooperative sensors and Category 2 / 3 non-cooperative sensors is whether they provide a correlation algorithm with consistent track IDs, or perform data association and provide a correlation algorithm with consistent track IDs.

[0033] Category 1 non-cooperative sensors provide reliable measurement trajectories of intruding aircraft with sensor-specific IDs. Examples of Category 1 non-cooperative sensors include TCAS Mode-C sensors and certain radar and vision sensors. Category 2 non-cooperative sensors comprise two subcategories. In the first subcategory (2a), the sensor correctly provides a consistent sequence of trajectory measurements originating from the same intruding aircraft, but provides inconsistent, varying trajectory IDs. In the second subcategory (2b), the sensor provides multiple measurement trajectories of the intruding aircraft with consistent IDs; however, the sensor provides multiple measurement trajectories of the same intruding aircraft at different points in time. Category 3 non-cooperative sensors provide measurement returns of intruding aircraft without measurement correlation or IDs. Examples of Category 2 and Category 3 non-cooperative sensors include some radars and vision sensors such as EO or infrared cameras.

[0034] Figure 2 It is a block diagram of a monitoring sensor arrangement 200 that can be used in a complete DAA system according to a specific implementation. Figure 2 The sensors and information sources shown can be mounted on friendly aircraft (e.g., UAS airborne sensors), located on the ground (ground-based sensors), or a combination of airborne and ground-based sensors.

[0035] like Figure 2 As depicted, information source 210 provides proprietary sensor measurements 212, ground-based sensor measurements 214, and ground-based information 216, such as air traffic location relative to a map (box 218). Both proprietary sensor measurements 212 and ground-based sensor measurements 214 can provide air traffic measurements 220 and random error measurements 222. Air traffic measurements 220 may include an associated trajectory ID (box 224) or may not include a trajectory ID (box 226).

[0036] A set of cooperative sensors 230 is equipped to receive measurement trajectories with ICAOID 232, and includes an ADS-B sensor 234 and a TCAS mode-S sensor 236. A set of category 1 non-cooperative sensors 240 is equipped to receive measurement trajectories with consistent sensor-specific IDs 242, and includes a TCAS mode-C sensor 244. A set of category 2 non-cooperative sensors 250 is equipped to provide measurement trajectories with inconsistent sensor-specific IDs 252, and includes some radar sensors 254 and some vision sensors 256. A set of category 3 non-cooperative sensors 260 is equipped to receive measurement results without trajectory IDs (from box 226), and includes other radar sensors 262 and other vision sensors 264. Furthermore, ground-based information, such as air traffic location relative to a map (from box 218), may belong to category 2 non-cooperative sensors 250 and category 3 non-cooperative sensors 260.

[0037] DAA tracking architecture

[0038] Figure 3 This is a block diagram of a DAA tracking architecture 300 with integrity for a vehicle 302 according to one embodiment. The DAA tracking architecture 300 with integrity typically includes: at least one processor 310, which is onboard on the vehicle 302 (such as a UAS vehicle or other aircraft); a detection and avoidance module 320 hosted by the processor 310; and an integrity module 330, hosted by the processor 310 and operatively communicating with the detection and avoidance module 320. Furthermore, a correlator system 340 is operatively communicating with the detection and avoidance module 320. In some embodiments, the correlator system 340 may be hosted by the processor 310, or in other embodiments, it may be hosted by a non-cooperative sensor 363. A vehicle navigation system 350 is operatively communicating with the detection and avoidance module 320 and the correlator system 340.

[0039] Multiple monitoring sensors 360 are operatively communicated with processor 310. The monitoring sensors 360 may include one or more cooperative sensors in a first group 361, one or more non-cooperative sensors (Category 1) in a second group 362, and one or more non-cooperative sensors (Category 2 and Category 3) in a third group 363. Detection and avoidance module 320 is operatively communicated with the one or more cooperative sensors in the first group 361 and the one or more non-cooperative sensors in the second group 362.

[0040] The first group 361 comprises one or more cooperative sensors (1,…,n) that operate to provide corresponding first measurement trajectories for one or more objects in the environment surrounding vehicle 302. Each corresponding first measurement trajectory includes a corresponding first trajectory identifier (ID). The second group 362 comprises one or more non-cooperative sensors (category 1; 1,…,m) that operate to provide corresponding second measurement trajectories for one or more objects in the environment surrounding vehicle 302. Each corresponding second measurement trajectory includes a corresponding second trajectory ID.

[0041] In the third group 363, one or more non-cooperative sensors, Category 2 sensors include a first subcategory 363-1 (Category 2a; 1,…,p) of non-cooperative sensors and a second subcategory 363-2 (Category 2b; 1,…,r) of non-cooperative sensors. The first subcategory 363-1 of non-cooperative sensors operates to provide a consistent sequence of trajectory measurements originating from the same object but with inconsistent and varying trajectory IDs. The second subcategory 363-2 of non-cooperative sensors operates to provide multiple measurement trajectories of an object with consistent IDs, but with multiple measurement trajectories of that object at the measurement time points. Also in the third group 363, Category 3 sensors include one or more non-cooperative sensors 363-3 (1,…,s) that operate to provide measurement returns without measurement trajectory correlation or IDs.

[0042] The detection and avoidance module 320 includes a tracking system 322 and an evaluation and guidance system 324. The tracking system 322 is configured to receive corresponding first measurement trajectories from a first set of 361 cooperative sensors and corresponding second measurement trajectories from a second set of 362 non-cooperative sensors. The tracking system 322 is configured to track one or more objects in the environment surrounding the vehicle 302. The tracking system 322 includes a trajectory-to-trajectory function in a data association module 325, which operates to generate and output a master solution trajectory and sub-solution trajectories with trajectory information including the total number of estimated trajectories, trajectory weights, sensor weights, the state average vector of each internal trajectory, and the state covariance matrix of each internal trajectory.

[0043] The correlator system 340 is operatively communicative with the third group of 363 non-cooperative sensors. The correlator system 340 is configured to receive sensor measurements directly from Category 2 sensors, which include a first subcategory 363-1 (Category 2a) of non-cooperative sensors and a second subcategory 363-2 (Category 2b) of non-cooperative sensors. The correlator system 340 is also configured to receive sensor measurements directly from Category 3 sensors, which include non-cooperative sensors 363-3.

[0044] In one specific implementation, the correlator system 340 operates to identify measurement sequences originating from the same object, regardless of non-cooperative sensors and measurement trajectory IDs. The correlator system 340 overrides any IDs provided by the sensors and assigns the IDs to the measurement trajectory itself of any sensor in the first subcategory 363-1 of non-cooperative sensors. For any sensor in the second subcategory 363-2 of non-cooperative sensors, the correlator system 340 fuses measurement returns from multiple measurement trajectories of the sensor to generate a fused measurement trajectory and assigns an ID to this fused measurement trajectory, thereby overriding any trajectory ID assigned by that sensor. For any non-cooperative sensor 363-3, the correlator system 340 also identifies sequences of measurement returns originating from the same object and assigns IDs to these sequences.

[0045] The correlator system 340 processes various sensor measurements received from the third group of 363 non-cooperative sensors and navigation data from the vehicle navigation system 350. Then, based on the sensor measurements and navigation data, the correlator system 340 outputs a relevant measurement trajectory to the detection and avoidance module 320.

[0046] The tracking system 322 of the detection and avoidance module 320 operates to fuse corresponding first measurement trajectories from a first group 361 of cooperative sensors, corresponding second measurement trajectories from a second group 362 of non-cooperative sensors, and correlated measurement trajectories from the correlator system 340. The tracking system 322 correlates and fuses the measurement trajectories to estimate the optimal trajectory for each of these objects. The estimated trajectory includes trajectory statistics for each object (such as an intruding aircraft) with a consistent ID.

[0047] Integrity module 330 includes trajectory integrity system 332, which is operatively in communication with data association module 325 of tracking system 322. Integrity requirement and statistics module 334 is operatively in communication with trajectory integrity system 332. As described in further detail below, trajectory integrity system 332 operates to provide integrity checks on associated trajectories, wherein correlation fault detection and correlation exclusion are performed to ensure that measured trajectories have been correctly associated, the correlation fault detection and correlation exclusion including one or more correlation discriminator thresholds. Trajectory integrity system 332 operates to compare, select, and output a master solution trajectory or one or more sub-solution trajectories based on associated trajectories provided by trajectory-to-trajectory function from data association module 325. For example, tracking system 322 may be used to compute a master solution trajectory using all sensor measurements contributing to a given trajectory; to compute one or more sub-solution trajectories using combinations of sensors contributing to the master solution trajectory; and to compute one or more sub-sub-solution trajectories using combinations of sensors contributing to each sub-solution trajectory. The trajectory integrity system 332 operates to send erroneously associated trajectories back to the data association module 325 for further processing, and to ensure the integrity of the trajectories estimated by the tracking system 322.

[0048] The tracking system 322 provides the estimated trajectory with ensured integrity to the evaluation and guidance system 324 in the detection and avoidance module 320 to provide guidance data to various vehicle applications 370, enabling the vehicle 302 to be maneuvered to avoid any object. Examples of vehicle applications 370 may include applications such as one or more displays 372, aircraft guidance systems 374, aircraft flight control systems 376, air / unmanned traffic management systems 378, etc.

[0049] Figure 4 This is a block diagram of an exemplary aircraft tracking system 400 that can be employed in a complete DAA tracking architecture. The aircraft tracking system 400 allows for the tracking of both cooperative and non-cooperative vehicles (such as intruding aircraft) within the same framework.

[0050] like Figure 4As shown, relative to the external environment of the friendly aircraft (box 410), a set of surveillance sensors provides sensor measurement trajectories (box 412) for use by the aircraft tracking system 400. The surveillance sensors can be airborne or ground-based sensors. Examples of such sensors include ADS-B sensors; sensors equipped for active surveillance of TCAS Mode-S air traffic; sensors equipped for active surveillance of TCAS Mode-C air traffic; vision-based sensors, such as electro-optical or infrared (EO / IR) imaging sensors; airborne radar; and ground-based air traffic control (ATC) radar (box 414). Furthermore, sensor metadata (box 416) can be obtained, and this sensor metadata can be used to convert sensor measurements into a common input format (box 418). This sensor metadata may include units, reference coordinate system, frequency, time delay statistics, detection probability, false alarm probability, etc. Similarly, as... Figure 4 As shown, a set of detection and avoidance trajectory requirements (box 420) are provided to the tracking system to obtain metrics for tracking multiple intruding aircraft (box 422) using sensor-measured trajectories (from box 412).

[0051] The associated tracking algorithm 430 typically includes a trajectory manager function 432, a data association function 434, and an integrity module 436. The trajectory manager function 432 operates in conjunction with the data association function 434 and the integrity module 436 to perform the following tasks: 1) initiating an intrusion aircraft trajectory; 2) maintaining the intrusion aircraft trajectory; 3) selecting associated measurement trajectories; 4) managing trajectory IDs output by monitoring sensors; 5) rejecting erroneous measurement trajectories and minimizing the generation of erroneous trajectories; 6) merging trajectory statistics; and 7) deleting the trajectories of intrusion aircraft that have left the sensor's field of interest (FOR) (box 438). Furthermore, the tracking algorithm 430 provides a tracking filter for individual intrusion aircraft (box 440), which interacts with the trajectory manager function 432, the data association function 434, and the integrity module 436. Further details of the operation of the tracking algorithm are referenced below. Figure 5 To describe.

[0052] Tracking algorithm 430 operates to calculate estimated intruding aircraft traffic trajectory statistics (trajectory) (box 440), which are sent downstream to the detection and avoidance assessment and guidance functions (box 442). The calculated trajectory statistics or trajectory may include relative position, relative velocity, distance, rate of change of distance, distance acceleration, azimuth, elevation, longitude, latitude, altitude, mean and variance of the rate of change of altitude, or other statistics such as geodesic position, rate of turn, and ground velocity (box 444). Other trajectory statistics are also possible depending on the functionality of the downstream system.

[0053] Figure 5This is a functional block diagram of an exemplary tracking algorithm 500 that can be employed in a complete DAA tracking architecture. Tracking algorithm 500 typically includes a measurement result formatting function 510; a first trajectory manager function 520 operatively communicating with the measurement result formatting function 510; a state (or trajectory) estimation function 530 operatively communicating with the first trajectory manager function 520; and a second trajectory manager function 540 operatively communicating with the state estimation function 530. The various functions (and sub-functions) of tracking algorithm 500 work together to estimate a statistically optimal trajectory for each object based on any combination of cooperative or non-cooperative sensors that provide sensor measurement trajectories derived from each object.

[0054] The measurement result formatting function 510 operates to receive sensor data, which includes measurement results, statistics, units, ID, or reference coordinate system (box 512). This sensor data is combined with the navigation solution (box 514) to calculate the coordinate transformation of the measurement trajectory (box 516) and resolve the measurement trajectory to a consistent reference coordinate system or tracking coordinate system.

[0055] The first trajectory manager function 520 performs the initialization of the measurement trajectory received from the measurement result formatting function 510 (box 522) by using a data association subfunction that performs measurement result to trajectory association, or more specifically, determines whether the sensor measurement trajectory originates from an actively tracked object. If the sensor measurement trajectory originates from an actively tracked object, the data association subfunction assigns the sensor measurement trajectory to the corresponding trajectory. If the sensor measurement trajectory does not originate from an actively tracked object, the data association subfunction can initiate a new trajectory for that object (box 524).

[0056] State estimation function 530 uses sensor metadata provided by sensor statistics 528 and sensor threshold 529 to predict trajectory statistics provided by activity trajectory statistics 526. State estimation function 530 uses sensor metadata provided by sensor statistics 528 and sensor threshold 529 to fuse the predicted trajectory statistics 532 with the statistics of the measured trajectory in the updated trajectory statistics function 534 (from trajectory manager function 520). This provides an estimate of the trajectory statistics of the object relative to the friendly aircraft.

[0057] The second trajectory manager function 540 performs various operations on the estimated trajectory statistics from the state estimation function 530, including deletion, merging, integrity, and clustering operations. Specifically, the first pruning subfunction includes a sensor trajectory threshold test (box 542) that uses a first set of deletion thresholds 543 to determine whether to retain or delete the sensor trajectory and its associated trajectory ID. The retained sensor trajectories are sent to the data-associated trajectory to trajectory subfunction (box 544) to manage the trajectory IDs (box 545) and merge the trajectory statistics (box 546) using a set of merging thresholds 547.

[0058] The integrity check subfunction 548 performs fault detection and elimination (FDE) based on the received trajectory statistics (from box 546) and integrity statistics 549 using a discriminator and thresholds. The integrity check subfunction 548 compares, selects, and outputs the master solution trajectory or one or more sub-solution trajectories (box 544) based on the relevant trajectories provided by the data-associated trajectory-to-trajectory subfunction. For each trajectory, the integrity check subfunction 548 sends the trajectory solutions that pass the integrity test to a second pruning subfunction (box 550) that includes a merged trajectory threshold test. If the master solution trajectory fails the integrity check, the integrity check subfunction 548 sends the sub-solution trajectories that fail the integrity check back to the data-associated trajectory-to-trajectory subfunction (box 544) for further processing. The second pruning subfunction (box 550) uses a set of deletion thresholds 551 to determine whether to retain or delete the merged trajectory. Trajectories that pass the deletion thresholds are sent to the trajectory selection subfunction 552 and output as the active trajectory statistics 526. These activity trajectory statistics 526 are sent to downstream systems such as DAA assessment and guidance functions (box 560).

[0059] For example, multiple sensors can simultaneously provide measurement trajectories originating from the same object, each with a combination of ICAO ID and sensor-specific ID. The trajectory manager function maintains trajectory IDs from all sensors that contributed to a particular trajectory. Additionally, as an object enters or leaves a sensor's FOR, the trajectory manager function adds or removes trajectory IDs associated with the estimated trajectory. If an object or target leaves one sensor's FOR but remains within the FORs of other sensors, the trajectory manager function also maintains trajectory IDs associated with the estimated trajectory.

[0060] Data association: trajectory to trajectory

[0061] The output of the data-associated trajectory to trajectory sub-function (544) is the trajectory generated from the merged trajectory statistics (546) and managed trajectory ID (545) functions. For example, for the master solution trajectory: J ≡ the total number of master solution trajectories, where the total number of master solution trajectories is selected by the user. The trajectory weights are: And the sensor weights are: The weights of sensors contributing to trajectory j are related to the monitoring sensors. Sensor weights are independent of trajectory weights, where:

[0062] S≡The total number of monitoring sensors that contribute to the trajectory;

[0063] N ≡ the total number of cooperative sensors contributing to the trajectory; and

[0064] M ≡ The total number of non-cooperative sensors that contribute to the trajectory.

[0065] State average vector x j It is the state average vector of trajectory j, and can be represented, for example, by the following formula:

[0066]

[0067] The 3D position vector is resolved in a user-selected coordinate system, and the distance is, for example, the distance from the friendly aircraft to the intruding aircraft. The state average vector can be selected in various ways known to those skilled in the art. The state covariance matrix P... j It is the state covariance matrix of trajectory j.

[0068] Integrity check

[0069] An integrity check subfunction (548) is added after the data-associated trajectory-to-trajectory subfunction (544). For a trajectory with contributions from a single sensor, the probability of hazard misleading information is determined based on the probability of hazard misleading information from a single sensor: P(HMI). j =P(HMI) sen,i , where P(HMI) j This represents the probability of misleading information about trajectory j. For a trajectory with contributions from two sensors, the probability of misleading information is determined based on the probabilities of misleading information from each sensor. If the two sensors are different sensors, then for example... If the two sensors are of the same type, then for example, P(HMI) j =2P(HMI) sen,i For trajectories with contributions from three or more sensors, a combination of the two previous equations is used to determine the probability of misleading hazard information.

[0070] In the deconstruction process, a master filter is used to estimate the master solution trajectory with all contributing sensor measurement trajectories. Additional filters use combinations of sensor measurement trajectories to estimate sub-solution trajectories, and sub-subsolution trajectories are estimated if at least three monitoring sensors contribute to the master trajectory. For both subsolution and sub-subsolution trajectories, contributions from one or more monitoring sensors to an internal trajectory are excluded from the contribution to that trajectory, enabling the deconstruction method to identify erroneously correlated or faulty measurement trajectories. The identification of one or more erroneously correlated or faulty measurement trajectories from monitoring sensors does not preclude other measurement trajectories from the same sensors from being used to estimate the trajectory. Thresholding involves assigning integrity probability requirements to the trajectory based on the different monitoring sensors contributing to it. Various methods of performing this assignment exist, known to those skilled in the art, and the resulting integrity threshold is user-selected.

[0071] Figure 6 This is a schematic diagram of an exemplary deseparation filter arrangement 600 that can be utilized by the integrity check function. Each circle in the filter arrangement 600 represents a tracking filter (and additional filters as needed). The tracking filter can be a Kalman filter, an unscented Kalman filter, or other Bayesian filters known to those skilled in the art. The master filter 602 (circle 0) combines the measured trajectories from all monitoring sensors contributing to the trajectory to calculate the master solution trajectory, represented by the expression S = N + M.

[0072] Sub-filter bank 610 includes sub-filters 610-1 (circle 01)...sub-filters 610-S (circle 0S), and is combined with monitoring sensor measurement trajectories in S-1 combinations to calculate sub-solution trajectories, wherein each sub-filter excludes different sensor measurement trajectories. For example, if S<3, the sub-solution filter can be used to identify a sensor measurement trajectory correlation fault.

[0073] Furthermore, sub-sub-filters can be used to calculate sub-sub-solution trajectories based on the monitoring sensor measurement trajectories from each sub-filter in sub-filter group 610, where each sub-sub-filter excludes two sensor measurement trajectories. For example, sub-sub-filter group 620 of sub-filter 610-1 includes sub-sub-filter 620-1 (circle 011)...sub-sub-filter 620-S1 (circle 01S1), and combines the monitoring sensor measurement trajectories from sub-filter 610-1 in combination at S-2 to calculate the sub-sub-solution trajectory. Sub-sub-filter group 624 of sub-filter 610-S includes sub-sub-filter 624-1 (circle 0S1)...sub-sub-filter 624-S s (Circle 0SS) SThe sub-sub-solution trajectory is calculated by combining the monitored sensor measurement trajectory from sub-filter 610-S with a combination of S-2. For example, if S>2, the sub-sub-solution filter can be used to identify sensor measurement trajectory correlation faults.

[0074] Figure 7 This is a functional block diagram of an internal integrity check system 700 for a specific implementation of a DAA tracking system. Figure 7 The interaction between the data association module 710, which provides trajectory-to-trajectory functionality, and the integrity check module 720, which includes fault detection and elimination (FDE) with discriminators and thresholds, is illustrated.

[0075] The data association module 710 generates all master solution trajectories (x j,0 ,P j,0 The state average vector x j and state covariance matrix P j They are then output from the main tracking filter to the integrity check module 720 and the first subtractor 714. The data association module 710 also generates the first sub-solution trajectory (x j,01 ,P j,01 The state average vector x j and state covariance matrix P j And output them to the first subtractor 714. Differential value (dx) j,01 ,dP j,01 The subtractor 714 calculates and outputs the result to the integrity check module 720 for use in FDE. Additionally, the state covariance matrix (P) of the first sub-solution trajectory... j,01 The result is sent to the integrity check module 720 for use in FDE.

[0076] The data association module 710 also generates the trajectory (x) for each additional sub-solution. j,0n ,P j,0n The state average vector x j and state covariance matrix P j They are then output to one or more subtractors 716-n, where n = 2, ..., S, which also receive the state average vector x of all trajectories. j,0 and state covariance matrix P j,0 Differential value (dx) j,0n ,dP j,0n The state covariance matrix (P) of each additional sub-solution trajectory is calculated by one or more subtractors 716-n and output to the integrity check module 720 for use in FDE. j,0n The result is sent to the integrity check module 720 for use in FDE.

[0077] The integrity check module 720 performs FDE based on the received input using a correlation discriminator and a threshold, as further described below. The integrity check module 720 compares, selects, and outputs the results, which are then sent to the second pruning function and back to the data association module 710 for further processing of the master solution trajectory and sub-solution trajectories (box 730).

[0078] Fault detection and troubleshooting

[0079] The discriminator used in fault detection and troubleshooting (FDE) is represented by the following expression:

[0080] The number of principal solution trajectories; k = 1, ..., S.

[0081] The separation covariance matrix is ​​represented by the following expression:

[0082] Number of principal solution trajectories; k = 1, ..., S

[0083] in:

[0084] The cross-correlation matrix between the main filter 0 and the sub-filter 0k.

[0085] The decision threshold is expressed by the following equation:

[0086]

[0087] in:

[0088]

[0089] Find the largest eigenvalue of the covariance matrix;

[0090] K j,FA ≡ Error correlation coefficient of trajectory j;

[0091] P j,FA The probability of user selection related to the error of each independently measured trajectory j.

[0092] If the discriminant is less than the decision threshold The correlation is then ensured. This means that all sensor measurement trajectories contributing to the main trajectory from the current correlation point in time (as well as accompanying internal trajectories from previous correlation points in time) are statistically consistent, and the correlation between measurement trajectories from different monitoring sensors is ensured. In this case, all sensors contributing to the trajectory are maintained. The trajectory (and all its associated statistics) is then sent to the second pruning function.

[0093] If the discriminant is greater than the decision threshold (d)j,0k >D j If S=3, then for S=3, the sub-solution trajectory k is statistically inconsistent with the main solution trajectory and other sub-solution trajectories. The sensor contribution k is the measurement trajectory of the monitoring sensor due to error correlation or fault. The sensor weights of the sensor contribution k are set to 0: w j,k =0, and the sensor weights are relative to the trajectory weights w. j The contribution k is removed. Trajectory k is sent back to the data association function for potential correlation with other trajectories, including those with contributions from one or two sensors. This additional correlation step still follows the association rules of the data association function. If S > 3, and a sub-solution trajectory is statistically inconsistent with the main solution trajectory and other sub-solution trajectories, then the sensor contribution k is an incorrectly correlated or faulty measurement trajectory, and the process outlined for S = 3 is followed, where the sensor weight contribution k is removed, and trajectory k is sent back to the data association function. If S > 3, and more than one sub-solution trajectory is statistically inconsistent with the main solution trajectory and other sub-solution trajectories, then more than one measurement trajectory contributing to the trajectory is incorrectly correlated or faulty, and sub-sub-solution trajectories are used to detect and identify incorrectly correlated or faulty measurement trajectories.

[0094] Collaborative Trajectory Only

[0095] When estimating cooperative trajectories based solely on sensor measurement trajectories from cooperative sensors (ADS-B and TCAS Mode-S), the following methods can be applied: If S = N = 1, and the internal trajectory is based solely on an ADS-B sensor, integrity statistics are obtained directly from sensor statistics. If S = N = 1, and the internal trajectory is based solely on a TCAS Mode-S sensor, integrity statistics are obtained directly from sensor statistics. If S = N = 2, and the internal trajectory is based on both ADS-B and TCAS Mode-S sensors, two ADS-B sensors, or two TCAS Mode-S sensors, integrity statistics are obtained directly from sensor statistics. If S = N ≥ 3, when the internal trajectory is based on at least one TCAS Mode-S sensor, distance and altitude statistics are used to calculate the discriminator and threshold. Note that the bearing error of the TCAS Mode-S sensor is relatively large, limiting the bearing measurement results to integrity analysis.

[0096] This method of using only cooperative trajectories has the benefit of identifying whether the ADS-B measurement trajectory is consistent with the TCAS mode S measurement trajectory, and it also mitigates the impact of interference and deception on the ADS-B measurement trajectory.

[0097] Non-cooperative trajectories only

[0098] When estimating only the non-cooperative trajectory based on sensor measurement trajectories from non-cooperative sensors (TCAS Mode-C, airborne radar, ground-based radar, or visual sensors), the following method can be applied. If S = M = 1 or S = M = 2, integrity statistics are obtained directly from sensor statistics. If S = M ≥ 3, and the trajectory is based on at least one TCAS Mode-C sensor, distance and altitude statistics are used to calculate the discriminator and threshold. Note that the bearing error of TCAS Mode-C sensors is relatively large, limiting bearing measurements to integrity analysis. If the internal trajectory is not based on a TCAS Mode-C sensor, 3D position and distance statistics are used to calculate the discriminator and threshold.

[0099] This method, which uses only non-cooperative trajectories, has the advantage of identifying non-cooperative monitoring sensor measurement trajectories of statistically consistent tracking objects and mitigating the impact of sensor measurement trajectory failures on the trajectory.

[0100] Cooperative trajectories and non-cooperative trajectories

[0101] In scenarios where both cooperative and non-cooperative trajectories are estimated based on sensor measurement trajectories from cooperative and non-cooperative sensors, the following method can be applied. If S=2, N=1, and M=1, integrity statistics are obtained directly from sensor statistics. If S=M≥3, and the trajectory is based on at least one TCAS Mode-S or TCAS Mode-C sensor, distance and altitude statistics are used to calculate the discriminator and threshold. It should be noted that the bearing errors of TCAS Mode-S and TCAS Mode-C sensors are relatively large, limiting bearing measurements to integrity analysis.

[0102] This approach of using both cooperative and non-cooperative trajectories offers the following benefits: mitigating the impact of interference and spoofing on ADS-B measurement trajectories; identifying non-cooperative monitoring sensor measurement trajectories of statistically consistent tracking objects; and mitigating the impact of sensor measurement trajectory failures on the trajectory.

[0103] The processing units and / or other computing devices used in the systems and methods described herein can be implemented using software, firmware, hardware, or suitable combinations thereof. The processing units and / or other computing devices may be supplemented or incorporated therein by specially designed application-specific integrated circuits (ASICs) or field-programmable gate arrays (FPGAs). The processing units and / or other computing devices may also include or run with software programs, firmware, or other computer-readable instructions to perform the various processing tasks, computational, and control functions used in the methods and systems described herein.

[0104] The methods described herein can be implemented using computer-executable instructions (such as program modules or components) that are executed by at least one processor or processing unit. Typically, program modules include routines, programs, objects, data components, data structures, algorithms, etc., that perform specific tasks or implement specific abstract data types.

[0105] Various procedural tasks, calculations, and instructions for generating other data used in performing the operations described herein may be implemented in software, firmware, or other computer-readable instructions. These instructions are typically stored on a suitable computer program product, including computer-readable media for storing computer-readable instructions or data structures. Such computer-readable media may be available media accessible by a general-purpose or special-purpose computer or processor or any programmable logic device.

[0106] Suitable computer-readable storage media may include, for example, non-volatile memory devices, including semiconductor memory devices such as random access memory (RAM), read-only memory (ROM), electrically erasable programmable ROM (EEPROM), or flash memory devices; disks, such as internal hard disks or removable disks; optical disk storage devices, such as optical discs (CD), digital versatile optical discs (DVD), Blu-ray discs; or any other medium that may be used to carry or store desired program code in the form of computer-executable instructions or data structures.

[0107] Example Implementation Plan

[0108] Example 1 includes a system comprising: at least one processor mounted on a vehicle; a plurality of monitoring sensors operatively communicating with the at least one processor, the monitoring sensors including one or more cooperative sensors, one or more non-cooperative sensors, or a combination of one or more cooperative sensors and one or more non-cooperative sensors; and a detection and avoidance module hosted by the at least one processor, the detection and avoidance module being configured to receive corresponding measurement trajectories from the monitoring sensors, and including a tracking system configured to track one or more objects in an environment surrounding the vehicle, wherein the tracking system includes: a data association module including a trajectory-to-trajectory function, the trajectory-to-trajectory function operating to generate and output a master solution trajectory and sub-solution trajectories having trajectory information including the total number of trajectories and trajectory weights, sensor weights, a state average vector for each trajectory, and a state. A covariance matrix; and an integrity module, hosted by the at least one processor and operatively communicating with the detection and avoidance module, the integrity module comprising: a trajectory integrity system, operatively communicating with the data association module, the trajectory integrity system operating to provide integrity checks using fault detection and elimination, the fault detection and elimination including one or more discriminators and one or more decision thresholds; wherein the trajectory integrity system operates to compare, select, and output a master solution trajectory or one or more sub-solution trajectories based on at least one decoupling technique and related trajectories provided by the trajectory-to-trajectory function from the data association module; wherein the trajectory integrity system operates to send trajectory solutions that pass the integrity test to a pruning sub-function, and to send the master solution trajectory or the one or more sub-solution trajectories that fail the integrity test back to the data association module for further processing, and to ensure the integrity of the trajectories correlated and estimated by the tracking system.

[0109] Example 2 includes the system according to Example 1, wherein the data association module, including the trajectory-to-trajectory function, operates to: calculate a master solution trajectory, the master solution trajectory corresponding to all sensor measurement trajectories contributing to a given trajectory; calculate one or more sub-solution trajectories based on the master solution trajectory; and calculate one or more sub-sub-solution trajectories based on each sub-solution trajectory; wherein when at least three of the monitored sensors provide measurement trajectories, the one or more sub-solution trajectories are configured to identify a sensor measurement trajectory correlation fault, and the one or more sub-sub-solution trajectories are configured to identify a two sensor measurement trajectory correlation fault.

[0110] Example 3 includes the system according to Example 2, wherein: the master solution trajectory is calculated in a master filter, which combines sensor measurement trajectories from all S monitoring sensors contributing to the internal trajectory, where S is the total number of monitoring sensors contributing to the trajectory; the one or more sub-solution trajectories are calculated in a sub-filter group, which includes one or more sub-filters, which combine the monitoring sensor measurement trajectories in an S-1 combination, where each sub-filter excludes different sensor measurement trajectories; and the one or more sub-sub-solution trajectories are calculated in one or more sub-sub-filter groups, each sub-sub-filter group including one or more sub-sub-filters, which combine the monitoring sensor measurement trajectories in an S-2 combination, where each sub-sub-filter excludes two sensor measurement trajectories.

[0111] Example 4 includes the system according to any one of Examples 1 to 3, wherein the integrity check module further includes an integrity requirement and statistics module, the integrity requirement and statistics module being operatively in communication with the trajectory integrity system.

[0112] Example 5 includes a system according to any one of Examples 1 to 4, wherein: the one or more cooperative sensors operate to provide a corresponding first measurement trajectory for each of the one or more objects in the environment surrounding the vehicle, wherein each corresponding first measurement trajectory includes a corresponding first trajectory identifier (ID); and the one or more non-cooperative sensors include: a first category of non-cooperative sensors, the first category of non-cooperative sensors operating to provide a corresponding second measurement trajectory for each of the one or more objects surrounding the vehicle, wherein each corresponding second measurement trajectory includes a corresponding second trajectory ID; a second category of non-cooperative sensors, the second category of non-cooperative sensors including: a first subcategory, the first subcategory operating to provide a consistent sequence of trajectory measurement results originating from the same object but having inconsistent and varying trajectory IDs; a second subcategory, the second subcategory operating to provide multiple measurement trajectories of the object having consistent IDs, but having multiple measurement trajectories of the object at measurement time points; and a third category of non-cooperative sensors, the third category of non-cooperative sensors operating to provide measurement statistics that do not have measurement trajectory correlation or IDs.

[0113] Example 6 includes the system according to Example 5, wherein: for the one or more cooperative sensors, the corresponding first trajectory ID of each corresponding first measurement trajectory is an International Civil Aviation Organization (ICAO) identifier; and the one or more cooperative sensors include Automatic Dependent Surveillance-Broadcast (ADS-B) sensors or Traffic Collision Avoidance System (TCAS) Mode-S sensors.

[0114] Example 7 includes the system according to any one of Examples 5 to 6, wherein: for the first category of non-cooperative sensors, the corresponding second trajectory ID for each corresponding second measurement trajectory is a sensor-specific ID. And the first category of non-cooperative sensors includes TCAS mode-C sensors.

[0115] Example 8 includes a system according to any one of Examples 5 to 7, wherein the second sensor category and the third sensor category of the non-cooperative sensor include airborne radar, ground-based radar, or visual sensors.

[0116] Example 9 includes the system according to any one of Examples 5 to 8, the system further comprising: a correlator system operatively communicating with the detection and avoidance module, the correlator system being configured to: receive measurement trajectories or measurement returns from a second sensor category or a third sensor category of the one or more non-cooperative sensors; and output one or more correlated measurement trajectories; and a navigation system onboard the vehicle and operating to calculate a navigation solution for the vehicle, the navigation system operatively communicating with the detection and avoidance module and the correlator system.

[0117] Example 10 includes the system according to Example 9, wherein the correlator system operates to: identify a sequence of measurement results originating from the same object, regardless of the type of non-cooperative sensor and the measurement trajectory ID provided by the sensor; for any non-cooperative sensor in the first subcategory, overwrite any ID provided by the sensor and assign the ID to the measurement trajectory itself; for any non-cooperative sensor in the second subcategory, fuse measurement returns from multiple measurement trajectories from the sensor to generate a fused measurement trajectory and assign an ID to the fused measurement trajectory, thereby overwriting any trajectory ID assigned by the sensor; and for any non-cooperative sensor in the third category, identify a sequence of measurement returns originating from the same object and assign an ID to the sequence.

[0118] Example 11 includes a system according to any one of Examples 9 to 10, wherein the tracking system is configured to process the corresponding first measurement trajectory from the one or more cooperative sensors, the corresponding second measurement trajectory of the first category from the one or more non-cooperative sensors, and the one or more related measurement trajector systems.

[0119] Example 12 includes a system according to any one of Examples 9 to 11, wherein the tracking system operates to fuse corresponding measurement trajectories from the one or more cooperative sensors and the one or more non-cooperative sensors in order to estimate an optimal trajectory with ensured integrity for each object in the environment surrounding the vehicle.

[0120] Example 13 includes the system according to any one of Examples 9 to 12, wherein the detection and avoidance module further includes an evaluation and guidance system configured to: receive the optimal trajectory with ensured integrity; and provide guidance data to one or more vehicle applications such that the vehicle can be operated to avoid any object in the environment surrounding the vehicle.

[0121] Example 14 includes a system according to any one of Examples 1 to 13, wherein the vehicle is a friendly aircraft and the one or more objects include an intrusion aircraft.

[0122] Example 15 includes the system according to any one of Examples 1 to 14, wherein the vehicle is an unmanned aerial vehicle.

[0123] Example 16 includes the system according to Example 15, wherein the unmanned aerial vehicle is part of an unmanned aerial vehicle system (UAS).

[0124] Example 17 includes a system according to any one of Examples 1 to 16, wherein the vehicle is an urban air traffic (UAM) vehicle.

[0125] Example 18 includes a method for ensuring the integrity of sensor trajectories estimated by a detection and avoidance tracking system, the method comprising: providing a data association module in the detection and avoidance tracking system, the data association module including trajectory-to-trajectory functionality; and providing an integrity check module communicating with the data association module and including a fault detection and elimination (FDE) function having a set of discriminators and decision thresholds; wherein the data association module performs a process including: sending all master solution trajectories (xi, xi, xi) to the integrity check module and a first subtractor. j,0 ,P j,0 The state average vector x j and state covariance matrix P j Send the first sub-solution trajectory (x) to the first subtractor j,01 ,P j,01 The state mean vector and state covariance matrix; where the differential value (dx) j,01 ,dP j,01The state covariance matrix (P) of the first subtractor is calculated and sent to the integrity check module for the FDE function, and the state covariance matrix (P) of the first sub-solution trajectory is... j,01 The trace (x) is sent to the integrity check module for use in the FDE function; and each additional sub-solution trajectory (x) is sent to one or more additional subtractors. j,0n ,P j,0n The state average vector and state covariance matrix, where n = 2…S, and the one or more additional subtractors also receive all principal solution trajectories (x... j,0 ,P j,0 The state mean vector and the state covariance matrix; wherein one or more additional differential values ​​(dx) j,0n ,dP j,0n The state covariance matrix (P) of each additional sub-solution trajectory is calculated by the one or more additional sub-subtractors and sent to the integrity check module for use in the FDE function. j,0n The data is sent to the integrity check module for use in the FDE function.

[0126] Example 19 includes the method according to Example 18, wherein the integrity check module uses the set of discriminators and decision thresholds to perform the FDE function to compare, select the master solution trajectory or one or more sub-solution trajectories and output them to the pruning function, and outputs the master solution trajectory or the one or more sub-solution trajectories to the data association module for further processing.

[0127] Example 20 includes the method according to Example 19, wherein if the discriminator is less than a given internal trajectory determination threshold, all sensor measurement trajectories contributing to the trajectory from the current correlation time point are statistically consistent, and the correlation of measurement trajectories from different monitoring sensors is ensured.

[0128] This invention may be embodied in other specific forms without departing from its essential characteristics. The described embodiments are to be regarded in all respects as illustrative rather than restrictive. Therefore, the scope of the invention is indicated by the appended claims rather than the foregoing description. All variations within the meaning and scope of the equivalence of the claims are to be covered within its scope.

Claims

1. A system comprising: At least one processor, said at least one processor being mounted on a vehicle; A plurality of monitoring sensors, wherein the plurality of monitoring sensors are operatively in communication with the at least one processor, the monitoring sensors comprising one or more cooperative sensors, one or more non-cooperative sensors, or a combination of one or more cooperative sensors and one or more non-cooperative sensors; and A detection and avoidance module, hosted by the at least one processor, is configured to receive corresponding measurement trajectories from the monitoring sensors and includes a tracking system configured to track one or more objects in the environment surrounding the vehicle, wherein the tracking system includes: The data association module includes a trajectory-to-trajectory function, which operates to generate and output a master solution trajectory and sub-solution trajectories with trajectory information, including the total number of trajectories, trajectory weights, sensor weights, the state average vector and state covariance matrix of each trajectory; and Integrity module, hosted by the at least one processor and operatively communicating with the detection and avoidance module, the integrity module comprising: A trajectory integrity system, which is operatively communicative with the data association module, is configured to provide integrity checks using fault detection and elimination, the fault detection and elimination including one or more discriminators and one or more decision thresholds; The trajectory integrity system operates by comparing, selecting, and outputting a master solution trajectory or one or more sub-solution trajectories based on at least one decoupling technique and associated or related trajectories provided by the trajectory-to-trajectory function from the data association module; The trajectory integrity system operates to select trajectory solutions that pass the integrity test and send them to the pruning sub-function, and to send the master solution trajectory or one or more sub-solution trajectories that fail the integrity test back to the data association module for further processing, and to ensure the integrity of the trajectories correlated and estimated by the tracking system.

2. The system of claim 1, wherein the data association module, including the trajectory-to-trajectory function, operates to: Calculate the principal solution trajectory, which corresponds to all sensor measurement trajectories that contribute to a given trajectory; One or more sub-solution trajectories are calculated based on the master solution trajectory; as well as Calculate one or more sub-sub-solution trajectories based on each sub-solution trajectory; Wherein, when at least three of the monitoring sensors provide measurement trajectories, the one or more sub-solution trajectories are configured to identify a correlation fault in one sensor measurement trajectory, and the one or more sub-solution trajectories are configured to identify a correlation fault in two sensor measurement trajectories; in: The master solution trajectory is calculated in the master filter, which combines sensor measurement trajectories from all S monitoring sensors that contribute to the internal trajectory, where S is the total number of monitoring sensors that contribute to the trajectory. The one or more sub-tracks are calculated in a sub-filter bank, which includes one or more sub-filters. These sub-filters are combined in an S-1 combination to monitor sensor measurement trajectories, wherein each sub-filter excludes different sensor measurement trajectories. The one or more sub-sub-tracks are calculated in one or more sub-sub-filter groups, each sub-sub-filter group including one or more sub-sub-filters, the one or more sub-sub-filters being combined in an S-2 combination to monitor sensor measurement trajectories, wherein each sub-sub-filter excludes two sensor measurement trajectories.

3. A method for ensuring the integrity of sensor trajectories estimated by a detection and avoidance tracking system, the method comprising: The detection and avoidance tracking system provides a data association module, which includes a trajectory-to-trajectory function. as well as An integrity check module is provided, which communicates with the data association module and includes a fault detection and elimination (FDE) function with a set of discriminators and decision thresholds; The data association module performs the following processes: Send all master solution trajectories (x) to the integrity check module and the first subtractor j,0 ,P j,0 The state average vector x j and state covariance matrix P j ; Send the first sub-solution trajectory (x) to the first subtractor j,01 ,P j,01 The state mean vector and state covariance matrix; Where the differential value (dx) j,01 ,dP j,01 The state covariance matrix (P) of the first subtractor is calculated and sent to the integrity check module for the FDE function, and the state covariance matrix (P) of the first sub-solution trajectory is... j,01 The data is sent to the integrity check module for use in the FDE function; as well as Send each additional sub-solution trajectory (x) to one or more additional subtractors j,0n ,P j,0n The state average vector and state covariance matrix, where n = 2…S, and the one or more additional subtractors also receive all principal solution trajectories (x... j,0 ,P j,0 The state average vector and the state covariance matrix; One or more of the additional differential values ​​(dx) j,0n ,dP j,0n The state covariance matrix (P) of each additional sub-solution trajectory is calculated by the one or more additional sub-subtractors and sent to the integrity check module for use in the FDE function. j,0n The data is sent to the integrity check module for use in the FDE function.

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